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Vogel-Heuser, Birgit, Feng Ju, Cesare Fantuzzi, Yan Lu, and Dieter Hess. "Knowledge-Based Automation for Smart Manufacturing Systems." IEEE Transactions on Automation Science and Engineering 18, no. 1 (January 2021): 2–4. http://dx.doi.org/10.1109/tase.2020.3044620.
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Adamczyk, Bruno Sérgio, Anderson Luis Szejka, and Osiris Canciglieri. "Knowledge-based expert system to support the semantic interoperability in smart manufacturing." Computers in Industry 115 (February 2020): 103161. http://dx.doi.org/10.1016/j.compind.2019.103161.
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Qu, Yuanju, Xinguo Ming, Yanrong Ni, Xiuzhen Li, Zhiwen Liu, Xianyu Zhang, and Liuyue Xie. "An integrated framework of enterprise information systems in smart manufacturing system via business process reengineering." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 11 (December 20, 2018): 2210–24. http://dx.doi.org/10.1177/0954405418816846.
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Enterprise information systems play a significant role in the Industry 4.0 era and are the crucial component to realize smart manufacturing systems. However, traditional enterprise information systems have some limits: (1) lack of complete information, (2) only satisfy limited business needs, and (3) lack of seamless integration, business intelligence, value-driven processes, and dynamic optimization. Clearly, the existing enterprise information systems are unable to satisfy the requirements for smart manufacturing systems: (1) autonomous operation, (2) sustainable values, and (3) self-optimization. In addition, smart manufacturing systems have become more efficient and effective, demanding for seamless information flow in enterprise information systems, knowledge, and data-driven accurately decision. Therefore, a new enterprise information systems framework is needed to bridge gaps between the requirements for traditional manufacturing system and smart manufacturing system. In this article, the integrative framework is proposed based on the business process reengineering, lean thinking, and intelligent management methods, with inclusion of six enterprise information systems aspects to provide upgrading guidelines from traditional manufacturing to smart manufacturing. The procedure of this method contains three steps: (1) it identifies requirements and acquires best practices using AS-IS model, (2) it redesigns six aspects of enterprise information systems using TO-BE model, and (3) it proposes a new enterprise information systems framework. Finally, the proposed framework is validated by real cases.
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Stojadinovic, Slavenko M., Vidosav D. Majstorovic, Adam Gąska, Jerzy Sładek, and Numan M. Durakbasa. "Development of a Coordinate Measuring Machine—Based Inspection Planning System for Industry 4.0." Applied Sciences 11, no. 18 (September 10, 2021): 8411. http://dx.doi.org/10.3390/app11188411.
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Industry 4.0 represents a new paradigm which creates new requirements in the area of manufacturing and manufacturing metrology such as to reduce the cost of product, flexibility, mass customization, quality of product, high level of digitalization, optimization, etc., all of which contribute to smart manufacturing and smart metrology systems. This paper presents a developed inspection planning system based on CMM as support of the smart metrology within Industry 4.0 or manufacturing metrology 4.0 (MM4.0). The system is based on the application of three AI techniques such as engineering ontology (EO), GA and ants colony optimization (ACO). The developed system consists of: the ontological knowledge base; the mathematical model for generating strategy of initial MP; the model of analysis and optimization of workpiece setups and probe configuration; the path simulation model in MatLab, PTC Creo and STEP-NC Machine software, and the model of optimization MP by applying ACO. The advantage of the model is its suitability for monitoring of the measurement process and digitalization of the measurement process planning, simulation carried out and measurement verification based on CMM, reduction of the preparatory measurement time as early as in the inspection planning phase and minimizing human involvement or human errors through intelligent planning, which directly influences increased production efficiency, competitiveness, and productivity of enterprises. The measuring experiment was performed using a machined prismatic workpiece (PW).
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Chumnumporn, Kwanchanok, Chawalit Jeenanunta, Somrote Komolavanij, Natthawadee Saenluang, Kamonda Onsri, Koraphat Fairat, and Kanchanok Itthidechakhachon. "The Impact of IT Knowledge Capability and Big Data and Analytics on Firm’s Industry 4.0 Capability." Proceedings 39, no. 1 (January 9, 2020): 22. http://dx.doi.org/10.3390/proceedings2019039022.
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Smart factory is a fully-integrated of firm’s facilities (i.e., sensors, smart machines, and robots) and information system architecture (i.e., IoT, ICT, and cloud computing) to enable high degree of automation in manufacturing processes. IT knowledge capability is the IT knowledge organization that how employees understand IT knowledge in different dimensions, i.e., general management, product design, production planning, data analysis, information security, and automation system. Since the system of smart factory depends on the massive of data collecting (big data) and the firm’s advance analyzing approach (analytics). The big data in manufacturing include the data from production planning, quality control, procurement, inventory control, human resource management (HRM), and delivery. The purpose of this study is to examine the role of IT knowledge capability and big data and analytics on the degree of smart factory. Survey data from 141 Thai manufacturing firms from the list of the ministry of industry and industrial zones were collected during March–April 2019. The multiple regression result shows that both IT knowledge capability and big data and analytics have a positive impact on the degree of smart factory. In addition, we use a firm’s age and firm’s size (based on the number of employees and total asset) as control variables. The results show that firm’s size have a positive effect on hypothesis model.
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Sujatha, M., N. Priya, A. Beno, T. Blesslin Sheeba, M. Manikandan, I. Monica Tresa, P. Subha Hency Jose, Vijayakumar Peroumal, and Sojan Palukaran Thimothy. "IoT and Machine Learning-Based Smart Automation System for Industry 4.0 Using Robotics and Sensors." Journal of Nanomaterials 2022 (September 15, 2022): 1–6. http://dx.doi.org/10.1155/2022/6807585.
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The concept of Industry 4.0, the fourth industrial revolution, is not yet widespread, despite the extensive research in this domain. Several aspects of human life will be improved with the implementation of Industry 4.0. Various levels of manufacturing processes, the end-users, cyberphysical system designers, managers, and all employees in the manufacturing process as well as the supply chains, will be influenced by the changes in manufacturing models and business paradigms caused by the implementation of Industry 4.0. Smart automation is enabled in the manufacturing industry with the evolution of Industry 4.0. Smart decision-making, knowledge, problem-solving, self-diagnosis, self-configuration, and self-automation are enabled in industries with this technology. In this work, the decision tree algorithm is used for monitoring energy consumption in machines and appliances, predicting future behaviour, and detecting anomalous behaviour. The efficiency of the proposed system is evaluated, and compared with existing methodologies, it offers an efficiency of 78%. Several standardization issues, security issues, resource planning challenges, legal issues, and issues due to changing business paradigms are faced with the implementation of this technology. The implementation of Industry 4.0 and its success or failure is completely dependent on the entire production chain and all the participants, from manufacturers to end-users.
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Horváth, Imre. "Connectors of smart design and smart systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35, no. 2 (April 19, 2021): 132–50. http://dx.doi.org/10.1017/s0890060421000068.
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AbstractThough they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them: (i) What way are smart design and smart systems enabled by artificial narrow, general, or super intelligence? and (ii) How can smart design be used in the realization of smart systems? and How can smart systems contribute to smart designing? A difficulty is that there are no exact definitions for these novel concepts in the literature. The endeavor to analyze the current situation and to answer the above questions stimulated an exploratory research whose first findings are summarized in this paper. Its first part elaborates on a plausible interpretation of the concept of smartness and provides an overview of the characteristics of smart design as a creative problem solving methodology supported by artificial intelligence. The second part exposes the paradigmatic features and system engineering issues of smart systems, which are equipped with application-specific synthetic system knowledge and reasoning mechanisms. The third part presents and elaborates on a conceptual model of AI-based couplings of smart design and smart systems. The couplings may manifest in various concrete forms in real life that are referred to as “connectors” in this paper. The principal types of connectors are exemplified and discussed. It has been found that smart design tends to manifest as a methodology of blue-printing smart systems and that smart systems will be intellectualized the enablers of implementation of smart design. Understanding the affordances of and creating proper connectors between smart design and smart systems need further explorative research.
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Koay, Fong Thai, Choo Jun Tan (Corresponding Author), Sin Yin Teh, Ping Chow Teoh, and Heng Chin Low. "SUPPORTING DECISION MAKING WITH AN ARIZ-BASED MODEL FOR SMART MANUFACTURING." Malaysian Journal of Computer Science 36, no. 1 (January 31, 2023): 53–78. http://dx.doi.org/10.22452/mjcs.vol36no1.4.
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Smart manufacturing has transformed the way decisions are made. By accelerating the delivery of data to the various decision points, more rapid decision-making processes can be realized. A generic Decision Support System (DSS) utilizes an efficient technique, which integrates the algorithm for inventive problem solving (ARIZ) and supervised machine learning into a model for supporting various automated decision making processes. The proposed model is to examine the theoretical framework of ARIZ by devising an ARIZ-based DSS model. It incorporates supervised ML algorithms to assist decision making processes. Three case studies from the manufacturing sector are evaluated. The results indicate the capability of the proposed DSS in achieving a high accuracy rate and, at the same time reducing the time and resources required for decision making. Our study has simplified the data processing and extraction processes through an automated ARIZ-based DSS model; therefore enabling a non-technical user the opportunity to harvest the vast knowledge from the collected data for efficient decision making.
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Chen, Shuxian, Zongqiang Ren, Xikai Yu, and Ao Huang. "A Dynamic Model of Evolutionary Knowledge and Capabilities Based on Human-Machine Interaction in Smart Manufactures." Computational Intelligence and Neuroscience 2022 (April 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/8584888.
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The increasing use of smart machines and devices is not only changing production principles but also reshaping the value of cocreation logic. The interaction between human and smart machine is the enabler of generating augmented intelligence. A system dynamics model is abstracted from smart manufacturing practices to represent the evolutionary processes of inertia, capability, and reliability induced by human-machine interaction. Human-machine interaction is conceptualized into two dimensions: technical and cognitive interaction. Simulation experiments illustrate how the improvement of human-machine interaction can leverage the dynamic capability and reduce the inertia in enterprises through multiple nonlinear feedbacks. There are two pathways to improve reliability and performance in enterprises by human-machine interaction: (1) to promote initiative innovation (change) from endogenous enabler by improving dynamic capability and (2) to promote transformation of knowledge and variation triggered by exogenous environmental changes to improve the dynamic capability for the flexibility and reliability.
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Garetti, Marco, Luca Fumagalli, and Elisa Negri. "Role of Ontologies for CPS Implementation in Manufacturing." Management and Production Engineering Review 6, no. 4 (December 1, 2015): 26–32. http://dx.doi.org/10.1515/mper-2015-0033.
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Abstract Cyber Physical Systems are an evolution of embedded systems featuring a tight combination of collaborating computational elements that control physical entities. CPSs promise a great potential of innovation in many areas including manufacturing and production. This is because we obtain a very powerful, flexible, modular infrastructure allowing easy (re) configurability and fast ramp-up of manufacturing applications by building a manufacturing system with modular mechatronic components (for machining, transportation and storage) and embedded intelligence, by integrating them into a system, through a network connection. However, when building such kind of architectures, the way to supply the needed domain knowledge to real manufacturing applications arises as a problem to solve. In fact, a CPS based architecture for manufacturing is made of smart but independent manufacturing components without any knowledge of the role they have to play together in the real world of manufacturing applications. Ontologies can supply such kind of knowledge, playing a very important role in CPS for manufacturing. The paper deals with this intriguing theme, also presenting an implementation of this approach in a research project for the open automation of manufacturing systems, in which the power of CPS is complemented by the support of an ontology of the manufacturing domain.
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Dornhöfer, Mareike, Simon Sack, Johannes Zenkert, and Madjid Fathi. "Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective." Journal of Manufacturing and Materials Processing 4, no. 3 (September 9, 2020): 89. http://dx.doi.org/10.3390/jmmp4030089.
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The implementation of Industry 4.0 and smart factory concepts changes the ways of manufacturing and production and requires the combination and interaction of different technologies and systems. The need for rapid implementation is steadily increasing as customers demand individualized products which are only possible if the production unit is smart and flexible. However, an existing factory cannot be transformed easily into a smart factory, especially not during operational mode. Therefore, designers and engineers require solutions which help to simulate the aspired change beforehand, thus running realistic pre-tests without disturbing operations and production. New product lines may also be tested beforehand. Data and the deduced knowledge are key factors of the said transformation. One idea for simulation is applying artificial intelligence, in this case the method of multi-agent-systems (MAS), to simulate the inter-dependencies of different production units based on individually configured orders. Once the smart factory is running additional machine learning methods for feedback data of the different machine units may be applied for generating knowledge for improvement of processes and decision making. This paper describes the necessary interaction of manufacturing and knowledge-based solutions before showing an MAS use case implementation of a production line using Anylogic.
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Selyanin, O. "MODERN TYPES OF PRODUCTION AND PRODUCTS BASED ON INTELLECTUAL CAPITAL." Actual directions of scientific researches of the XXI century: theory and practice 9, no. 4 (January 19, 2022): 136–53. http://dx.doi.org/10.34220/2308-8877-2022-9-4-136-153.
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Currently the scientific community distinguishes several types of production and product based on intellectual capital. Most used are intelligent production and product, knowledge-intensive, innovative, high-tech and smart manufacturing and product. These terms were introduced a long time ago but their precise definitions still do not exist and the boundaries between them are rather arbitrary. This paper is devoted to the analysis and systematization of definitions of the concepts of science-intensive, innovative, high-tech and intellectual production and product represented in scientific issues in order to distinguish their key characteristics, compare and designate their relationship. It was found each of the concepts has at least 2 approaches to the definition and as a rule one of which reflects its consistency. The relationship between each of the types of production and the product has been determined. The system of relationships has a hierarchical structure: high-tech manufacturing and product are the subset of innovative, those are the subset of knowledge-intensive, and those are the subset of intelligent. Smart manufacturing appears to be a special characteristic. The results of this work can be used as a basis for the development of more accurate definitions of the concepts under consideration in the scientific community, their unification in scientific researches and legislative acts.
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Ehrlich, Jacques, Georges Coche, and Amal Zerrouki. "Smart sensor research at the French Laboratoire Central des Ponts et Chaussées." Sensor Review 17, no. 3 (September 1, 1997): 240–47. http://dx.doi.org/10.1108/02602289710172373.
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Highlights two works being carried out by the French Laboratoire Central des Ponts et Chaussées in the field of smart sensors. The first concerns the knowledge of loads applied to bridges in order to evaluate extreme load effects and fatigue load effects over their lifetime. To achieve these goals, a data acquisition system based on smart sensors extracting and classifying extrema in the traffic loads signal has been developed. The second concerns distributed systems software cost reduction by means of a generic model. The aim of the model is the design of a software generator for smart sensor‐based systems. The key of the system is in the description of an instrumentation plan under the form of a data dependence graph (DDG). The goal of the generator is to map and “execute” that DDG on the physical architecture according to the number of transducers, their affectation to the smart sensors and a PC‐based system controller.
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Yin, Dao, Xinguo Ming, and Xianyu Zhang. "Understanding Data-Driven Cyber-Physical-Social System (D-CPSS) Using a 7C Framework in Social Manufacturing Context." Sensors 20, no. 18 (September 17, 2020): 5319. http://dx.doi.org/10.3390/s20185319.
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The trend towards socialization, personalization and servitization in smart manufacturing has attracted the attention of researchers, practitioners and governments. Social manufacturing is a novel manufacturing paradigm responding to this trend. However, the current cyber–physical system (CPS) merges only cyber and physical space; social space is missing. A cyber–physical–social system (CPSS)-based smart manufacturing is in demand, which incorporates cyber space, physical space and social space. With the development of the Internet of Things and social networks, a large volume of data is generated. A data-driven view is necessary to link tri-space. However, there is a lack of systematical investigation on the integration of CPSS and the data-driven view in the context of social manufacturing. This article proposes a seven-layered framework for a data-driven CPSS (D-CPSS) along the data–information–knowledge–wisdom (DIKW) pyramid under a social manufacturing environment. The evolution, components, general model and framework of D-CPSS are illustrated. An illustrative example is provided to explain the proposed framework. Detailed discussion and future perspectives on implementation are also presented.
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Guan, Xiang, Jinhao Sun, Zhixia Hou, Qingdong Xiao, Long Chen, and Lu Gan. "Construction of an Smart Progress Production Unit Based on Paddle Boring." Journal of Physics: Conference Series 2235, no. 1 (May 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2235/1/012029.
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Abstract With the continuous improvement of hardware product performance and data processing capability and the rapid development of network communication technology, the intelligent technology of computer systems is becoming more and more mature, and the intelligence of manufacturing has become the focus of people's attention. The intelligent performance of manufacturing is to use the knowledge obtained from data analysis to make inferences and decisions to solve problems, and the Cyber-Physical System (CPS) supports the deep integration of informationization and industrialization by integrating advanced information technology and automatic control technology, such as perception, computation, communication, control, etc. Although there has been great progress in the research of CPS, it has been widely used in the development of computer systems. Despite significant progress in CPS research, some key issues have yet to be fully addressed at the shop floor level, including dynamic reorganization and local intelligence. These issues have hindered the research on the intelligence of complex processes. For progress units of complex processes, this study proposes a CPS-based smart progress unit (SPU) construction method. In order to improve machining efficiency and quality consistency, this study proposes a multi-source data acquisition technique and a multi-objective optimization algorithm to process the data within the machining unit. Finally, this study takes the paddle boring process as an example, and designs hardware and software systems to automate the paddle boring process and meet the needs of automation and intelligent transformation of the paddle boring process.
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Ullah, AMM Sharif. "Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering." Education Sciences 9, no. 3 (August 29, 2019): 228. http://dx.doi.org/10.3390/educsci9030228.
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This article addresses some fundamental issues of concept mapping relevant to discipline-based education. The focus is on manufacturing knowledge representation from the viewpoints of both human and machine learning. The concept of new-generation manufacturing (Industry 4.0, smart manufacturing, and connected factory) necessitates learning factory (human learning) and human-cyber-physical systems (machine learning). Both learning factory and human-cyber-physical systems require semantic web-embedded dynamic knowledge bases, which are subjected to syntax (machine-to-machine communication), semantics (the meaning of the contents), and pragmatics (the preferences of individuals involved). This article argues that knowledge-aware concept mapping is a solution to create and analyze the semantic web-embedded dynamic knowledge bases for both human and machine learning. Accordingly, this article defines five types of knowledge, namely, analytic a priori knowledge, synthetic a priori knowledge, synthetic a posteriori knowledge, meaningful knowledge, and skeptic knowledge. These types of knowledge help find some rules and guidelines to create and analyze concept maps for the purposes human and machine learning. The presence of these types of knowledge is elucidated using a real-life manufacturing knowledge representation case. Their implications in learning manufacturing knowledge are also described. The outcomes of this article help install knowledge-aware concept maps for discipline-based education.
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Sira, Mariya. "Efficient Practices of Cognitive Technology Application for Smart Manufacturing." Management Systems in Production Engineering 30, no. 2 (May 19, 2022): 187–91. http://dx.doi.org/10.2478/mspe-2022-0023.
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Abstract Cognitive manufacturing (CM) provides for the merging of sensor-based information, advanced analytics, and cognitive technologies, mainly machine learning in the context of Industry 4.0. Manufacturers apply cognitive technologies to review current business metrics, solve essential business problems, generate new value in their manufacturing data and improve quality. The article investigates four powerful applications for cognitive manufacturing and their influence on a company`s maintenance. The study aims to observe kinds of cognitive technology applications for smart manufacturing, distinguish their prospective gains for manufacturers and provide successful examples of their adoption. The analysis is based on the literature and report review. Assessment of the cases of technology adoption proves that cognitive manufacturing provides both enhanced knowledge management and helps organizations improve fundamental business measurements, such as productivity, product reliability, quality, safety, and yield while reducing downtime and lowering costs.
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Židek, Kamil, Ján Piteľ, Milan Adámek, Peter Lazorík, and Alexander Hošovský. "Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept." Sustainability 12, no. 9 (May 1, 2020): 3658. http://dx.doi.org/10.3390/su12093658.
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This article deals with the creation of a digital twin for an experimental assembly system based on a belt conveyor system and an automatized line for quality production check. The point of interest is a Bowden holder assembly from a 3D printer, which consists of a stepper motor, plastic components, and some fastener parts. The assembly was positioned in a fixture with ultra high frequency (UHF) tags and internet of things (IoT) devices for identification of status and position. The main task was parts identification and inspection, with the synchronization of all data to a digital twin model. The inspection system consisted of an industrial vision system for dimension, part presence, and errors check before and after assembly operation. A digital twin is realized as a 3D model, created in CAD design software (CDS) and imported to a Tecnomatix platform to simulate all processes. Data from the assembly system were collected by a programmable logic controller (PLC) system and were synchronized by an open platform communications (OPC) server to a digital twin model and a cloud platform (CP). Digital twins can visualize the real status of a manufacturing system as 3D simulation with real time actualization. Cloud platforms are used for data mining and knowledge representation in timeline graphs, with some alarms and automatized protocol generation. Virtual digital twins can be used for online optimization of an assembly process without the necessity to stop that is involved in a production line.
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Robinson, David Charles, David Adrian Sanders, and Ebrahim Mazharsolook. "Ambient intelligence for optimal manufacturing and energy efficiency." Assembly Automation 35, no. 3 (August 3, 2015): 234–48. http://dx.doi.org/10.1108/aa-11-2014-087.
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Purpose – This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Design/methodology/approach – Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Findings – The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support. Research limitations/implications – Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems. Practical implications – Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support. Social implications – This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making. Originality/value – This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.
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Carreño, Ricardo, Verónica Aguilar, Daniel Pacheco, Marco Antonio Acevedo, Wen Yu, and María Elena Acevedo. "An IoT Expert System Shell in Block-Chain Technology with ELM as Inference Engine." International Journal of Information Technology & Decision Making 18, no. 01 (January 2019): 87–104. http://dx.doi.org/10.1142/s0219622018500499.
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Knowledge society blockchain is one of the most powerful and recent tools to make the internet environment safer and reliable. Manufacturing has traditionally been dominated by standard designs that are mass-produced, due to the fact, that custom production causes additional costs that make it less affordable than mass production. This paper proposes to develop a designer expert system for IoT installation layout designs, using blockchain distributed system based on a machine learning, with users entering data to the expert system by a smart bot software. This expert system will work using extreme learning machine as inference engine; therefore, this is a shell to develop any expert system with fast learning. The whole system is represented by a smart contract with a value linked to the value of the expert system, the more this expert system be quoted on the web, the more the shares of the smart contract will cost.
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Lv, Yaqiong, and Danping Lin. "Design an intelligent real-time operation planning system in distributed manufacturing network." Industrial Management & Data Systems 117, no. 4 (May 8, 2017): 742–53. http://dx.doi.org/10.1108/imds-06-2016-0220.
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Purpose With the new generation Industry 4.0 coming, as well as globalization and outsourcing, products are fabricated by different parties in the distributed manufacturing network and enterprises face the challenge of consistent planning of semi-finished product in each manufacturing process in different geographical locations. The purpose of this paper is to propose a real-time operation planning system in the distributed manufacturing network to intelligently control/plan the manufacturing networks. Design/methodology/approach The feature of the proposed system is to model and simulate large distributed manufacturing networks to streamline the mechanical and production engineering processes with radio frequency identification (RFID) technology, which can keep track of process variants. To deal with concurrency and synchronization, the hierarchical timed colored Petri net (HTCPN) formalism for modeling is selected in this study. This method can help to model graphically and test the discrete events of concurrent operations. Fuzzy inference system can help for knowledge representation, so as to provide knowledge-based decision assistance in distributed manufacturing environment. Findings In this proposed system, there are two main sub-systems: one is the real-time modeling system, and the other one is intelligent operation planning system. These two systems are not parallel in the whole systems while the intelligent operation planning system should be embedded in any stage of the real-time modeling system as needed. That means real time modeling system provides the holistic structure of the studied distributed manufacturing system and realize real-time data transfer and information exchange. At the same time the embedded intelligent operation planning system fulfill operation plan function. Originality/value This new intelligent real-time operation system realizes real-time modeling with RFID-based HTCPN and smart fuzzy engine to fulfill intelligent operation planning which is highly desirable in the environment of Industry 4.0. The new intelligent manufacturing architecture will highly reduce the traditional planning workload and improve the planning results without manual error interference. The new system has been applied in a practical case to demonstrate its feasibility.
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Oh, Seokju, Donghyun Kim, Chaegyu Lee, and Jongpil Jeong. "Edge-Cloud Alarm Level of Heterogeneous IIoT Devices Based on Knowledge Distillation in Smart Manufacturing." Electronics 11, no. 6 (March 14, 2022): 899. http://dx.doi.org/10.3390/electronics11060899.
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Along with the fourth industrial revolution, smart factories are receiving a great deal of attention. Large volumes of real-time data that are generated at high rates, especially in industries, are becoming increasingly important. Accordingly, the Industrial Internet of Things (IIoT), which connects, controls, and communicates with heterogeneous devices, is important to industrial sites and is now indispensable. To ensure the fairness and quality of the IIoT with limited network resources, the network connection of the IIoT needs to be constructed more intelligently. Many studies are being conducted on the efficient use of the resources that are imposed on IIoT devices. Therefore, in this paper, we propose a collaboration optimization method for heterogeneous devices that is based on cloud–fog–edge architecture. First, this paper proposes a knowledge distillation-based algorithm that can collaborate on cloud–fog–edge computing on the basis of distributed control. Second, to compensate for the shortcomings of knowledge distillation, we propose a framework for combining a soft-label-based alarm level. Finally, the method that is proposed in this paper was verified through several experiments, and it is shown that this method can effectively shorten the response time and solve the problems of existing IIoT networks, and that it can be efficiently applied to heterogeneous devices.
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Fattahi, Saman, Takuya Okamoto, and Sharifu Ura. "Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing." Big Data and Cognitive Computing 5, no. 4 (October 25, 2021): 58. http://dx.doi.org/10.3390/bdcc5040058.
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In smart manufacturing, human-cyber-physical systems host digital twins and IoT-based networks. The networks weave manufacturing enablers such as CNC machine tools, robots, CAD/CAM systems, process planning systems, enterprise resource planning systems, and human resources. The twins work as the brains of the enablers; that is, the twins supply the required knowledge and help enablers solve problems autonomously in real-time. Since surface roughness is a major concern of all manufacturing processes, twins to solve surface roughness-relevant problems are needed. The twins must machine-learn the required knowledge from the relevant datasets available in big data. Therefore, preparing surface roughness-relevant datasets to be included in the human-cyber-physical system-friendly big data is a critical issue. However, preparing such datasets is a challenge due to the lack of a steadfast procedure. This study sheds some light on this issue. A state-of-the-art method is proposed to prepare the said datasets for surface roughness, wherein each dataset consists of four segments: semantic annotation, roughness model, simulation algorithm, and simulation system. These segments provide input information for digital twins’ input, modeling, simulation, and validation modules. The semantic annotation segment boils down to a concept map. A human- and machine-readable concept map is thus developed where the information of other segments (roughness model, simulation algorithm, and simulation system) is integrated. The delay map of surface roughness profile heights plays a pivotal role in the proposed dataset preparation method. The successful preparation of datasets of surface roughness underlying milling, turning, grinding, electric discharge machining, and polishing shows the efficacy of the proposed method. The method will be extended to the manufacturing processes in the next phase of this study.
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Lee, Sojung, Siyeon Kim, Daeyoung Lim, Dong-Eun Kim, and Wonyoung Jeong. "Analysis of EMG Electrode Locations Using 3D Body Scanning for Digital Pattern Construction of a Smart EMG Suit." Sustainability 13, no. 5 (March 2, 2021): 2654. http://dx.doi.org/10.3390/su13052654.
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According to recent trends, smart clothing products that can receive electromyography (EMG) signals during the wearer’s muscle activity are being developed and commercialized. On the other hand, there is a lack of knowledge on the way to specify the electrode locations on the clothing pattern. Accurately located EMG electrodes in the clothing support the reliability and usefulness of the products. Moreover, a systematic process to construct anatomically validated smart clothing digitally should be performed to facilitate the application of a mass-customized manufacturing system. The current study explored the EMG measurement locations of nine muscles and analyzed them in association with various anthropometric points and even postures based on the 3D body scan data. The results suggest that several line segments of the patterns can be substituted by size-dependent equations for the electrodes in place. As a final step, a customized pattern of a smart EMG suit was developed virtually. The current study proposes a methodology to develop body-size dependent equations and patterns of a smart EMG suit with well-located electrodes using 3D scan data. These results suggest ways to produce smart EMG suits in response to impending automation and mass customization of the clothing manufacturing system.
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Amalnik, Morteza Sadegh. "A Reference Model and a vision for manufacturing system for 2030." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 7 (April 30, 2015): 5861–68. http://dx.doi.org/10.24297/ijct.v14i7.1899.
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The manufacturing enterprises are now experiencing high pressure of competition. In addition, the advancement in computer software, hardware, networks, information technologies and integration has been gradually reshaping the manufacturing companies by shifting from the industrial age to the information and knowledge era. Due to these elevated competitiveness and advanced computer technology, a number of new manufacturing and management strategies (e.g., Lean production, Just in time, Kaizen, Concurrent Engineering (CE), Cellular Manufacturing (CM), Agile manufacturing, Business process re-engineering (BPR), Agent-based systems (ABS), Computer Integrated Manufacturing (CIM), virtual manufacturing system have emerged for the innovation of manufacturing industries. The developments in organizational concepts created new concepts such as Smart organizations, Centers of excellence, Intelligent enterprises, Integrated enterprises, Virtual enterprises, Virtual enterprises networks, Dynamic enterprises, Extended enterprises, Agile enterprises, Lean enterprises, Process-driven organizations, e-enterprises, Borderless enterprises, Complicated or complex manufacturing systems, Flat structures and others. These terms have been used by researchers to describe various aspects of enterprises and its operational aspects. Although they have different definitions and scopes, there are several common issues: integration of enterprise functions; integration of enterprise resources; and collaboration. In addition Various vendors produced software applications such as Materials Requirement Planning (MRP), Manufacturing Resource Planning (MRP II), Enterprise Resource Planning (ERP),CAD/CAM systems, Manufacturing Execution System (MES), Advanced Planning & Scheduling System (APS), Supply Chain Execution (SCE), Customer Relationship Management (CRM), Advanced Order Management (AOM), Warehouse Management Systems (WMS), Transport Management System (TMS) and others. This paper proposes a Reference Model and vision for Manufacturing System for 2030 and discussed various aspects of future manufacturing enterprise..It supports the inter-enterprise functions/resources integration and collaboration over the networked environment.
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Bauer, Martin, Flavio Cirillo, Jonathan Fürst, Gürkan Solmaz, and Ernö Kovacs. "Urban Digital Twins – A FIWARE-based model." at - Automatisierungstechnik 69, no. 12 (November 27, 2021): 1106–15. http://dx.doi.org/10.1515/auto-2021-0083.
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Abstract This article describes the use of digital twins for smart cities, i. e., the Urban Digital Twin (UDTw) concept. It shows how UDTws can be realized using the open source components from the FIWARE ecosystem that are already used in more than 200 cities worldwide. The used NGSI-LD standard is supported by the European Connecting Europe Facility, the Open and Agile Smart City community, the Indian Urban Data Exchange platform, and the Japanese Smart City Reference Model. Unlike digital twins in other domains, e. g., manufacturing, where digital twins are co-developed with their physical counterparts, UDTws often evolve driven by different stakeholders, on different time scales, as well as by utilizing many different data sources from the city. This article builds on a well-established lifecycle model for Digital Twins and combines this with a conceptual model for digital twins consisting of data, reactive, predictive and forecasting (“what if”) digital twin functionalities. The article also describes how AI-based technologies can be used to extract knowledge to build the UDTws from the IoT-based infrastructure of a smart city.
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Yang, Yu Xue, Xiang Su, Jian Lu, and Ye Wei Xu. "Review on Smart Factory Operations: A Bibliometric Analysis." Applied Mechanics and Materials 906 (April 29, 2022): 87–104. http://dx.doi.org/10.4028/p-40l741.
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Over the last few years, existing and emerging Information and Communication Technologies (ICT) and artificial intelligence have been changing the way that factories conduct their manufacturing activities. Operation system of smart factories has been of great interest to researchers in recent years. However, the research concerning operations for the smart factory is still at the nascent stage. To address this need, we conduct a citation and co-citation analysis on smart factory operation system research published in the 11-year period from 2010-2020. A total of 351 papers were selected from Web of Science database. In the citation analysis, we depend on the degree centrality and betweenness centrality to identify 36 important papers. In addition, our main path analysis reveals the role of ICT in facilitating fast development of operation in smart factory. In the co-citation analysis, we identify four major research themes: resource reconfiguration, predictive production planning model, collaborative scheduling mechanism and technology basis of logistics. This is among the first studies to examine the knowledge structure of smart factory operations research by using evidence-based analysis methods. Recommendations for the future research directions have suggested based on our analysis.
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Xu, Ying. "Intelligent Identification of Embedded System Equipment State Based on Internet of Things." Mobile Information Systems 2022 (May 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/3076432.
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IoT (Internet of things) technology will be more widely used in the manufacturing industry, which will bring new trends to the development of the manufacturing industry. With the continuous advancement of IoT technology in the power field, the integration of IoT and smart grid has become a new topic. In the process of monitoring and troubleshooting the whole EPSE (Embedded Power System Equipment), it is very important to conduct a comprehensive analysis and routine testing on specific equipment and systems, so that the power system can work stably. In this paper, the knowledge sharing and transfer model of EPSE based on IoT is analyzed, and an intelligent identification method of EPSE state based on the GA-C_HMM (Genetic Algorithm-Coupled Hidden Markov Model) algorithm is proposed. After the GA parameters are optimized, the state model library is constructed by fitting each state data of the equipment to C_HMM, and the state of the equipment is determined by calculating the maximum probability value of the signal to be diagnosed. The experimental results show that when the recognition time is 12 hours, the recognition accuracy rate of the existing recognition method is 69.7%, and that of the recognition method in this paper is 98.3%, which shows that the recognition accuracy rate of the recognition method adopted in this study is higher than that of the existing methods and the recognition ability is stronger.
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Morkun, V. S., and I. A. Kotov. "Knowledge base formation for automation of dispatch control over power systems of the mining and metallurgical complex." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 4 (2021): 103–9. http://dx.doi.org/10.33271/nvngu/2021-4/103.
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Purpose. The research is aimed at developing and introducing methods of knowledge extraction concerning online control over power systems under emergency modes and building smart complexes of automatizing managerial decision making based on incorporated ontological knowledge bases. Methodology. The authors use the calculated planned experiment method applied to building sensitivity matrices of controlled parameters of power systems in sensor points to controlled factors and introduction of sensitivity coefficients into knowledge bases. Findings. The research suggests methods for obtaining and building a knowledgebase of professional ontologies for online control over power system modes. The problem of calculating sensitivity of controlled parameters to controlling actions is solved. Calculation results for the emergency mode enable building impact functions and determining sensitivity matrix coefficients. The smart system knowledgebase is built to provide decision support for dispatch control over power system modes under standard and emergency conditions. There are obtained sets of mode data used as knowledgebase components enabling efficient assessment of the emergency mode rate and its dispatch correction. Besides calculation parameters of intensity of controlling actions, the knowledgebase also comprises linguistic concepts, facts and rules of instructive dispatch materials. A knowledge base has been built on the basis of a subset of the linguistic corpus of concepts for the professional area of emergency response in the power system. Originality. For the first time, there is suggested an approach to incorporating various linguistic knowledge forms represented by a single ontological model and numerical parameters of sensitivity of the power system mode to controlling actions into an integrated knowledgebase, which enables building effective smart systems of dispatch decision support and implementing them into the operating automatized dispatch control system. Practical value. The ontological knowledgebase of online dispatch control is built that enables realizing a software complex of a decision support system aimed at automatizing online dispatch control over standard and emergency modes of power systems. Application of the suggested approach to building the knowledgebase and its use with online dispatch personnels decision support enhance reliability and increase maximum accessible time of personnels non-stop work by 1.5 years with absolute accident elimination, thus providing a significant economic effect.
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Zhou, Minggui, and Gongxing Yan. "Performance of Ferroelectric Materials in the Construction of Smart Manufacturing for the New Infrastructure of Smart Cities." Advances in Materials Science and Engineering 2022 (August 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/3451281.
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Smart city construction is the inevitable product of scientific development and transformation of life by building digital cities, building the Internet of Things, and making city management systems simple and intelligent through cloud computing. Smart city is a new generation information technology. Make full use of the advanced form of urban informatization based on the next generation innovation of knowledge society in all walks of life in the city. Cloud computing is a new network application concept. The core concept of cloud computing is to take the Internet as the center, and provide fast and secure cloud computing services and data storage on the website, so that everyone who uses the Internet can use the huge computing resources and data center on the network. The role of smart city engineering infrastructure is to build the infrastructure of this platform, so that smart cities can operate effectively, such as deformation test of ferroelectric materials, particle suitability analysis of ferroelectric materials, etc., This research is oriented to the intelligent manufacturing of new infrastructures in smart cities and analyzes the performance of ferroelectric materials in construction, aiming to better grasp the performance of ferroelectric materials and provide constructive suggestions for smart manufacturing in smart cities. The article first understands and states the related concepts, related construction requirements, development status and problems that need to be solved for smart city smart manufacturing by consulting relevant materials; then, it discusses the ferroelectric materials involved in the construction, analyzes the data of piezoelectric properties, etc., which will help to give more clear guidance on the process of tooling design; finally, the application link of ferroelectric materials is tested, and the deformation of ferroelectric materials and this premise are discussed on the problem of intelligent manufacturing efficiency and intelligent manufacturing efficiency. The experimental results show that the maximum value in the group of smart manufacturing benefits is 559.37; the maximum value between groups is 172.35. For efficiency of smart manufacturing, the maximum value between groups reaches 187.07; the maximum value in groups is 286.35. Whether it is a significant analysis of smart manufacturing benefits or smart manufacturing efficiency, the experimental results are quite impressive.
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Abduljabbar, Ali, Omar Alsaydia, Aya Mahfoodh, and Rushd Mohammed. "Design of an IOT smart current control system based on Google Assistant." Eastern-European Journal of Enterprise Technologies 5, no. 2(119) (October 30, 2022): 86–94. http://dx.doi.org/10.15587/1729-4061.2022.262118.
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In locations where power is restricted, such as off-grid, solar, and generator-powered houses, considering the capacity of the power source is critical for the effectiveness of home automation systems. During regular power system outages, millions of houses all over the globe are reliant on a fixed current power supply to keep their lights on. In such circumstances, prioritizing and arranging the home's workload is essential. The goal of this paper is to decrease the amount of effort required by the user to manually control a gadget. To connect with the Raspberry Pi and the users, this system makes use of Google Assistant Software Development Kit (SDK), which is offered by Google. Users use voice commands to manage the devices in their homes, check the amount of current available, and chat to the Google Assistant to turn on/off the smart switch. This paper suggests using a sensor, Message Queuing Telemetry Transport (MQTT) protocol, a controller (OpenHAB open source), and an actuator in conjunction with each other (smart switch) has the capability of measuring and monitoring the entire power that is available and making choices based on that knowledge. Finally, the usage of Google Assistant as an artificial intelligence system makes end-user engagement with the smart home more pleasant. The proposed network was executed in both unlimited and limited power or electrical current modes to compare the standard unlimited smart home setup and our current control design. The system was programmed to function based on the proposed algorithm, with a 10 Ampere as a maximum available current. The water heater was considered a low priority load in this trial as a heavy load. In this system’s run, the smart controller was continuously monitoring the load, and when the total load reaches 10 Amperes or above it turns off the low priority loads. Thus, preventing the power supply overload.
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Vogel-Heuser, Birgit, Felix Ocker, Iris Weiß, Robert Mieth, and Frederik Mann. "Potential for combining semantics and data analysis in the context of digital twins." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2207 (August 16, 2021): 20200368. http://dx.doi.org/10.1098/rsta.2020.0368.
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Modern production systems can benefit greatly from integrated and up-to-date digital representations. Their applications range from consistency checks during the design phase to smart manufacturing to maintenance support. Such digital twins not only require data, information and knowledge as inputs but can also be considered integrated models themselves. This paper provides an overview of data, information and knowledge typically available throughout the lifecycle of production systems and the variety of applications driven by data analysis, expert knowledge and knowledge-based systems. On this basis, we describe the potential for combining data analysis and knowledge-based systems in the context of production systems and describe two feasibility studies that demonstrate how knowledge-based systems can be created using data analysis. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.
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Brizhak, O. V. "SUBJECTIVE FORMS OF CORPORATE CAPITAL: PRACTICE OF ORGANIZATION OF ECONOMIC RELATIONS." Scientific bulletin of the Southern Institute of Management, no. 4 (December 30, 2017): 17–23. http://dx.doi.org/10.31775/2305-3100-2017-4-17-23.
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Topicality of the research is connected with the objective to increase the vision of smart, flexible, agile manufacturing (smart manufacturing), the role of the concept of lean production in the management of Russian corporations, enhancing the motivational mechanisms and the growth of labor productivity. In the formation of a complex creative teams, when formed sufficiently rigidly connected indicators of quality and on-time workforce, a typical system vnutritorakalnah subject relations and analysis of the human qualities of employees on the basis of compatibility with each other and with management. This applies not only to creative production, knowledge-intensive production based on the latest technology and providing system integration of all links of the production chain, but also traditional industrial technologies, lean technologies (lean production), when the concept of management of enterprises based on the constant quest to eliminate all kinds of losses. In the process of research, the author used empirical, structural, dialectical and system approaches. In the process of research, the author used empirical, historico-logical, dialectical and systemic approaches. The author defines the basic concepts that characterize a qualitative assessment of the subjective forms of corporate capital through motivational mechanisms and productivity, implemented in practice in modern Western and Russian corporations in conditions of competitive lean production.
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Susanto, Akhmad Hadi, Togar Simatupang, and Meditya Wasesa. "Industry 4.0 Maturity Models to Support Smart Manufacturing Transformation: A Systematic Literature Review." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 2 (March 26, 2023): 334–44. http://dx.doi.org/10.29207/resti.v7i2.4588.
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With increasing pressure to revitalize manufacturing industries with Smart Manufacturing capability within the Industry 4.0 (I4.0) context, companies have uneven readiness reflecting their gaps and barriers for transforming to the I4.0 state. Understanding factors and measuring a company’s maturity in addressing the I4.0 transformation is crucial to diagnose the company’s current condition and provide corresponding prescriptive action plan effectively. Despite the positive trend of maturity models for the industries, companies still face challenges with low I4.0 adoption rate. Designing a corresponding diagnostic framework into an intelligent maturity model will ultimately lead the company’s pathways toward the desired capabilities. In response, we systematically review and select the state-of-the-art research through a Systematic Literature Review (SLR) conduct to scrutinize the main characteristics of 14.0 Maturity Models. Subsequently, 35 exceptional articles published between 1980-2020 were selected for in-depth analysis of their structure, dimensions, and analytical features. Our analysis revealed the descriptive method have been widely used in many maturity models while few more-advanced prescriptive models design adopt fuzzy rule-base analytical hierarchy, knowledge based, Monte-Carlo methods, and even expert-system approaches. Furthermore, people, culture, organization, resources, information system, business processes, and smart technology, products and services have been treated as the popular evaluation dimensions which will define the state of an industry’s maturity level.
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Selicati, Valeria, Marco Mazzarisi, Francesco Saverio Lovecchio, Maria Grazia Guerra, Sabina Luisa Campanelli, and Michele Dassisti. "A monitoring framework based on exergetic analysis for sustainability assessment of direct laser metal deposition process." International Journal of Advanced Manufacturing Technology 118, no. 11-12 (October 16, 2021): 3641–56. http://dx.doi.org/10.1007/s00170-021-08177-x.
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Abstract With the constant increase of energy costs and environmental impacts, improving the process efficiency is considered a priority issue for the manufacturing field. A wide knowledge about materials, energy, machinery, and auxiliary equipment is required in order to optimize the overall performance of manufacturing processes. Sustainability needs to be assessed in order to find an optimal compromise between technical quality of products and environmental compatibility of processes. In this new Industry 4.0 era, innovative manufacturing technologies, as the additive manufacturing, are taking a predominant role. The aim of this work is to give an insight into how thermodynamic laws contribute at the same time to improve energy efficiency of manufacturing resources and to provide a methodological support to move towards a smart and sustainable additive process. In this context, a fundamental step is the proper design of a sensing and real-time monitoring framework of an additive manufacturing process. This framework should be based on an accurate modelling of the physical phenomena and technological aspects of the considered process, taking into account all the sustainability requirements. To this end, a thermodynamic model for the direct laser metal deposition (DLMD) process was proposed as a test case. Finally, an exergetic analysis was conducted on a prototype DLMD system to validate the effectiveness of an ad-hoc monitoring system and highlight the limitations of this process. What emerged is that the proposed framework provided significant advantages, since it represents a valuable approach for finding suitable process management strategies to identify sustainable solutions for innovative manufacturing procedures.
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Bonnaud, Olivier, and Ahmad Bsiesy. "Adaptation of the Higher Education in Engineering to the Advanced Manufacturing Technologies." Advances in Technology Innovation 5, no. 2 (April 1, 2020): 65–75. http://dx.doi.org/10.46604/aiti.2020.4144.
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The 21st century will be the era of the fourth industrial revolution with the progressive introduction of the digital society, with smart/connected objects, smart factories driven by robotics, the Internet of Things (IoT) and artificial intelligence. Manufacturing should be performed by the industry entitled 4.0. These are advanced technologies resulting from steady development of information technology associated with new objects and systems that can fulfil manufacturing tasks. The industry 4.0 concept relies largely on the ability to design and manufacture smart and connected devices that are based on microelectronics technology. This evolution requires highly-skilled technicians, engineers and PhDs well prepared for research, development and manufacturing. Their training, which combines knowledge and the associated compulsory know-how, is becoming the main challenge for the academic world. The curricula must therefore contain the basic knowledge and associated know-how training in all the specialties in the field. The software and hardware used in microelectronics and its applications are becoming so complex and expensive that the most realistic solution for practical training is to share facilities and human resources. This approach has been adopted by the French microelectronics education network, which includes twelve joint university centres and 2 industrial unions. It makes it possible to minimize training costs and to train future graduates on up-to-date tools similar to those used in companies. Thus, this paper deals with the strategy adopted by the French network in order to meet the needs of the future industry 4.0.
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Vještica, Marko, Vladimir Dimitrieski, Milan Pisarić, Slavica Kordić, Sonja Ristić, and Ivan Luković. "Towards a Formal Specification of Production Processes Suitable for Automatic Execution." Open Computer Science 11, no. 1 (January 1, 2021): 161–79. http://dx.doi.org/10.1515/comp-2020-0200.
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Abstract Technological advances and increasing customer need for highly customized products have triggered a fourth industrial revolution. A digital revolution in the manufacturing industry is enforced by introducing smart devices and knowledge bases to form intelligent manufacturing information systems. One of the goals of the digital revolution is to allow flexibility of smart factories by automating shop floor changes based on the changes in input production processes and ordered products. In order to make this possible, a formal language to describe production processes is needed, together with a code generator for its models and an engine to execute the code on smart devices. Existing process modeling languages are not usually tailored to model production processes, especially if models are needed for automatic code generation. In this paper we propose a research on Industry 4.0 manufacturing using a Domain-Specific Modeling Language (DSML) within a Model-Driven Software Development (MDSD) approach to model production processes. The models would be used to generate instructions to smart devices and human workers, and gather a feedback from them during the process execution. A pilot comparative analysis of three modeling languages that are commonly used for process modeling is given with the goal of identifying supported modeling concepts, good practices and usage patterns.
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Manca, Marco Manolo, and Luca Massidda. "Deep learning based non-intrusive load monitoring with low resolution data from smart meters." Communications in Applied and Industrial Mathematics 13, no. 1 (January 1, 2022): 39–56. http://dx.doi.org/10.2478/caim-2022-0004.
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Abstract A detailed knowledge of the energy consumption and activation status of the electrical appliances in a house is beneficial for both the user and the energy supplier, improving energy awareness and allowing the implementation of consumption management policies through demand response techniques. Monitoring the consumption of individual appliances is certainly expensive and difficult to implement technically on a large scale, so non-intrusive monitoring techniques have been developed that allow the consumption of appliances to be derived from the sole measurement of the aggregate consumption of a house. However, these methodologies often require additional hardware to be installed in the domestic system to measure total energy consumption with high temporal resolution. In this work we use a deep learning method to disaggregate the low frequency energy signal generated directly by the new generation smart meters deployed in Italy, without the need of additional specific hardware. The performances obtained on two reference datasets are promising and demonstrate the applicability of the proposed approach.
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Kao, Nawata, and Huang. "Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry." Sustainability 11, no. 8 (April 18, 2019): 2342. http://dx.doi.org/10.3390/su11082342.
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Technological innovations are regarded as the tools that can stimulate economic growth and the sustainable development of technology. In recent years, as technologies based on the internet of things (IoT) have rapidly developed, a number of applications based on IoT innovations have emerged and have been widely adopted by various public and private sectors. Applications of IoT in the manufacturing industry, such as manufacturing intelligence, not only play a significant role in the enhancement of industrial competitiveness and sustainability, but also influence the diffusion of innovative applications that are based on IoT innovations. It is crucial for policy makers to understand these potential reasons for stimulating IoT industrial sustainability, as they can facilitate industrial competitiveness and technological innovations using supportive means, such as government procurement and financial incentives. Therefore, there is a need to ascertain different factors that may affect IoT industrial sustainability and further explore the relationship between these factors. However, finding a set of factors that affects IoT industrial sustainability is not easy. Recently, the robustness of a theoretical framework, termed the technological innovation system (TIS), has been verified and has been used to explore and analyze technological and industrial development. Thus, it is suitable for this research to use this theoretical model. In order to find out appropriate factors and accurately analyze the causality among factors that influence IoT industrial sustainability, this research presents a Bayesian rough Multiple Criteria Decision Making (MCDM) model based on TIS functions by integrating random forest (RF), decision making trial and evaluation (DEMATEL), Bayesian theory, and rough interval numbers. The proposed analytical framework is validated by an empirical case of defining the causality between TIS functions to enable the industrial sustainability of IoT in the Taiwanese smart manufacturing industry. Based on the empirical study results, the cause group consists of entrepreneurial activities, knowledge development, market formation, and resource mobilization. The effect group is composed of knowledge diffusion through networks’ guidance of the search, and creation of legitimacy. Moreover, the analytical results also provide several policy suggestions promoting IoT industrial sustainability that can serve as the basis for defining innovation policy tools for Taiwan and late coming economies.
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Tayeh, Tareq, Sulaiman Aburakhia, Ryan Myers, and Abdallah Shami. "An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series." Machine Learning and Knowledge Extraction 4, no. 2 (April 2, 2022): 350–70. http://dx.doi.org/10.3390/make4020015.
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As a substantial amount of multivariate time series data is being produced by the complex systems in smart manufacturing (SM), improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is challenging, as a sufficiently large amount of defective training data is often not available and frameworks are required to capture both the temporal and contextual dependencies across different time steps while being robust to noise. In this paper, we propose an unsupervised Attention-Based Convolutional Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding (ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time series. The framework starts by pre-processing and enriching the data, before constructing feature images to characterize the system statuses across different time steps by capturing the inter-correlations between pairs of time series. Afterwards, the constructed feature images are fed into an attention-based ConvLSTM autoencoder, which aims to encode the constructed feature images and capture the temporal behavior, followed by decoding the compressed knowledge representation to reconstruct the feature images’ input. The reconstruction errors are then computed and subjected to a statistical-based, dynamic thresholding mechanism to detect and diagnose the anomalies. Evaluation results conducted on real-life manufacturing data demonstrate the performance strengths of the proposed approach over state-of-the-art methods under different experimental settings.
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Chang, Xiao, Xiaoliang Jia, Kuo Liu, and Hao Hu. "Knowledge-enabled digital twin for smart designing of aircraft assembly line." Assembly Automation 41, no. 4 (June 8, 2021): 441–56. http://dx.doi.org/10.1108/aa-09-2020-0133.
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Purpose The purpose of this paper is to provide a knowledge-enabled digital twin for smart design (KDT-SD) of aircraft assembly line (AAL) to enhance the AAL efficiency, performance and visibility. Modern AALs usually need to have capabilities such as digital-physical interaction and self-evaluation that brings significant challenges to traditional design method for AAL. The digital twin (DT) combining with reusable knowledge, as the key technologies in this framework, is introduced to promote the design process by configuring, understanding and evaluating design scheme. Design/methodology/approach The proposed KDT-SD framework is designed with the introduction of DT and knowledge. First, dynamic design knowledge library (DDK-Lib) is established which could support the various activities of DT in the entire design process. Then, the knowledge-driven digital AAL modeling method is proposed. At last, knowledge-based smart evaluation is used to understand and identify the design flaws, which could further improvement of the design scheme. Findings By means of the KDT-SD framework proposed, it is possible to apply DT to reduce the complexity and discover design flaws in AAL design. Moreover, the knowledge equips DT with the capacities of rapid modeling and smart evaluation that improve design efficiency and quality. Originality/value The proposed KDT-SD framework can provide efficient design of AAL and evaluate the design performance in advance so that the feasibility of design scheme can be improved as much as possible.
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Teti, Roberto, Pascal Le Masson, Mitsutaka Matsumoto, and AMM Sharif Ullah. "Special Issue on Intelligent Computation in Design and Manufacturing." International Journal of Automation Technology 12, no. 3 (May 1, 2018): 273–74. http://dx.doi.org/10.20965/ijat.2018.p0273.
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To solve problems underlying design and manufacturing we often rely on methodologies of computational intelligence such as machine learning, artificial neural networks, fuzzy logic, fuzzy inference systems and smart optimization algorithms. In this Special Issue of the International Journal of Automation Technology, original articles are presented with reference to the engagement of intelligent computation in diverse application areas of design and manufacturing, including manufacturing process monitoring, manufacturing systems management, scheduling, design theory and methodology. The six research papers in this Special Issue propose the use of intelligent computation methodologies to deal with various topics related to manufacturing and design. In particular, the first three papers focus on manufacturing process monitoring with reference to different manufacturing technologies, including tool wear monitoring in drilling of composite materials, sensor monitoring in CNC turning and residual stress prediction in welding. Diverse intelligent approaches such as artificial neural networks and adaptive neuro-fuzzy inference systems are proposed to support manufacturing process monitoring. The fourth paper deals with the manufacturing system level, proposing the employment of a solution algorithm combining metaheuristics and operation simulation for scheduling of production processes. The fifth paper aims at developing tools to guide the manufacturers to manage the technology investment and cost saving target for customer satisfaction based on the application of internet of things. The last paper proposes a methodology to support the introduction of customer requirements in product and service design via a decision support system which exploits artificial intelligence algorithms (machine learning) based on inductive inference, allowing knowledge related to product/service to be mapped, structured and managed to design the service and product semantic model. The editors deeply appreciate all the authors and anonymous reviewers for their effort and excellent work to make this Special Issue unique. We hope that future research on intelligent computation in manufacturing and design will advance manufacturing technology and systems as well as design methodologies.
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Celent, Luka, Dražen Bajić, Sonja Jozić, and Marko Mladineo. "Hard Milling Process Based on Compressed Cold Air-Cooling Using Vortex Tube for Sustainable and Smart Manufacturing." Machines 11, no. 2 (February 10, 2023): 264. http://dx.doi.org/10.3390/machines11020264.
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Improving machining performance and meeting the requirements of sustainable production at the same time represents a major challenge for the metalworking industry and scientific community. One approach to satisfying the above challenge is to apply different types of cutting fluids or to optimise their usage during the machining process. The fact that cutting fluids are well known as significant environmental pollutants in the metalworking industry has encouraged researchers to discover new environmentally friendly ways of cooling and lubricating in the machining process. Therefore, the main goal is to investigate the influence of different machining conditions on the efficiency of hard machining and find a sustainable solution towards smart manufacturing. In the experimental part of the work, the influence of various machining parameters and conditions on the efficiency of the process was investigated and measured through the surface roughness, tool wear and cutting force components. Statistical data processing was carried out, and predictive mathematical models were developed. An important achievement is the knowledge of the efficiency of compressed cold air cooling for hard milling with the resulting lowest average flank wear of 0.05 mm, average surface roughness of 0.28 µm, which corresponds to grinding procedure roughness classes of N4 and N5, and average tool durability increase of 26% compared to dry cutting and conventional use of cutting fluids. Becoming a smart machining system was assured via technological improvement achieved through the reliable prediction of tool wear obtained by radial basis neural networks modelling, with a relative prediction error of 3.97%.
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Arai, Tamio, Yasushi Umeda, Fumio Kojima, Sadayo Hirata, and Tomohiko Sakao. "Special Issue on Service Engineering." International Journal of Automation Technology 12, no. 4 (July 3, 2018): 447–48. http://dx.doi.org/10.20965/ijat.2018.p0447.
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To solve problems underlying design and manufacturing we often rely on methodologies of computational intelligence such as machine learning, artificial neural networks, fuzzy logic, fuzzy inference systems and smart optimization algorithms. In this Special Issue of the International Journal of Automation Technology, original articles are presented with reference to the engagement of intelligent computation in diverse application areas of design and manufacturing, including manufacturing process monitoring, manufacturing systems management, scheduling, design theory and methodology. The six research papers in this Special Issue propose the use of intelligent computation methodologies to deal with various topics related to manufacturing and design. In particular, the first three papers focus on manufacturing process monitoring with reference to different manufacturing technologies, including tool wear monitoring in drilling of composite materials, sensor monitoring in CNC turning and residual stress prediction in welding. Diverse intelligent approaches such as artificial neural networks and adaptive neuro-fuzzy inference systems are proposed to support manufacturing process monitoring. The fourth paper deals with the manufacturing system level, proposing the employment of a solution algorithm combining metaheuristics and operation simulation for scheduling of production processes. The fifth paper aims at developing tools to guide the manufacturers to manage the technology investment and cost saving target for customer satisfaction based on the application of internet of things. The last paper proposes a methodology to support the introduction of customer requirements in product and service design via a decision support system which exploits artificial intelligence algorithms (machine learning) based on inductive inference, allowing knowledge related to product/service to be mapped, structured and managed to design the service and product semantic model. The editors deeply appreciate all the authors and anonymous reviewers for their effort and excellent work to make this Special Issue unique. We hope that future research on intelligent computation in manufacturing and design will advance manufacturing technology and systems as well as design methodologies.
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Ghorashi, S. S., and S. R. Hejazi. "TOWARD A MODEL FOR THE POLICY REQUIREMENTS OF TECHNOLOGICAL ENTREPRENEURSHIP IN THE URBAN ECOSYSTEM." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 235–40. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-235-2023.
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Abstract. Cities are creating an environment that encourages digital growth. Cities' capacity to facilitate and drive the development of a digital core system emphasizes the importance of cities in creating the necessary circumstances for a successful ecosystem - access to talent, access to funding, access to spaces and locations, and access to markets. Cities, on the other hand, can contribute to the creation of mechanisms such as co-investment funds, in which the public and private sectors share the risk of supporting new creative businesses. All facets of manufacturing, consumption, and regulatory services could be altered by digital technologies in our everyday routines. By introducing new capabilities and business models, as well as by influencing their environment and the policy systems that govern them, they will have a significant impact on entrepreneurial ecosystems. In this article, we are looking for a model that may express policy requirements of technological entrepreneurship in the urban ecosystem, based on knowledge about the importance and requirements of entrepreneurial ecosystems, the smart city approach, and the knowledge-based development strategy.A comparative analytical method was used to conduct this research. We review the literature on ecosystems, urban ecosystems, smart cities, and knowledge-based urban development. Then we classified the parameters of each and presented how these categories are related in a model. In the research literature and in an executive position, dealing with a model for the policy requirements of technological entrepreneurship in the urban ecosystem can explain how the issue is operationalized, examine the city as an ecosystem where entrepreneurship occurs through technology and requires its own policies.
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Sava, Dorina, and Mădălina Cărbureanu. "ANDROID APPLICATION FOR USER’S REAL-TIME INFORMATION REGARDING THE POSIBILITY OF BEING CONTACT TO A COVID-19 INFECTED PERSON." Romanian Journal of Petroleum & Gas Technology 4 (75), no. 1 (2023): 5–16. http://dx.doi.org/10.51865/jpgt.2023.01.01.
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Soon after the pandemic inflicted by Covid-19 was declared, different organizations and smart application development companies suggested that artificial intelligence could be an useful tool in helping the medical staff monitoring the virus spread for a better understanding of its behavior. This paper presents a smart application CovidWatch developed for users real-time warning regarding the possibility of contracting the Sars-CoV-2 virus from an infected person. For this purpose, the developed application uses the Bluetooth module of the user’s device in order to check the social distance and to determine the possible contacts. The developed application has a number of four modules (the expert system (ES), the data retrieval, the real-time information and warning and the interface), that work on Android devices. Using the artificial intelligence techniques (knowledge-based systems), the application has a knowledge base populated with user’s data, the user’s time of contact and distance and the users identified as being contacts. If a user warns the android application that he has been infected with Covid-19, it further triggers the expert system inference engine, allowing the knowledge to be inferred. Based on the implemented rules, the inference engine analyses the data regarding the users that were in contact with an infected person. If those meet the conditions taken into consideration for declaring a person as being contact (a certain distance and time) they receive in real time a warning message and some advice regarding how it is recommended to act. The main advantage of android application is that it provides real-time warning regarding the possibility or probability of being infected with Covid-19, warning that can be deactivated only after the user has confirmed that the took note of this. A set of CovidWatch application simulation results is also presented taking into consideration different scenarios.
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Stavropoulos, Panagiotis, Alexios Papacharalampopoulos, and Kyriakos Sabatakakis. "Robust and Secure Quality Monitoring for Welding through Platform-as-a-Service: A Resistance and Submerged Arc Welding Study." Machines 11, no. 2 (February 17, 2023): 298. http://dx.doi.org/10.3390/machines11020298.
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For smart manufacturing systems, quality monitoring of welding has already started to shift from empirical modeling to knowledge integration directly from the captured data by utilizing one of the most promising Industry 4.0’s key enabling technologies, artificial intelligence (AI)/machine learning (ML). However, beyond the advantages that they bring, AI/ML introduces new types of security threats, which are related to their very nature and eventually, they will pose real threats to the production cost and quality of products. These types of security threats, such as adversarial attacks, are causing the targeted AI system to produce incorrect or malicious outputs. This may undermine the performance (and the efficiency) of the quality monitoring systems. Herein, a software platform servicing quality monitoring for welding is presented in the context of resistance and submerged arc welding. The hosted ML classification models that are trained to perform quality monitoring are subjected to two different types of untargeted, black-box, adversarial attacks. The first one is based on a purely statistical approach and the second one is based on process knowledge for crafting these adversarial inputs that can compromise the models’ accuracy. Finally, the mechanisms upon which these adversarial attacks are inflicting damage are discussed to identify which process features or defects are replicated. This way, a roadmap is sketched toward testing the vulnerability and robustness of an AI-based quality monitoring system.
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Sipos, Bence, Gábor Katona, and Ildikó Csóka. "A Systematic, Knowledge Space-Based Proposal on Quality by Design-Driven Polymeric Micelle Development." Pharmaceutics 13, no. 5 (May 12, 2021): 702. http://dx.doi.org/10.3390/pharmaceutics13050702.
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Nanoparticle research and development for pharmaceuticals is a challenging task in the era of personalized medicine. Specialized and increased patient expectations and requirements for proper therapy adherence, as well as sustainable environment safety and toxicology topics raise the necessity of well designed, advanced and smart drug delivery systems on the market. These stakeholder expectations and social responsibility of pharma sector open the space and call new methods on the floor for new strategic development tools, like Quality by Design (QbD) thinking. The extended model, namely the R&D QbD proved to be useful in case of complex and/or high risk/expectations containing or aiming developments. This is the case when we formulate polymeric micelles as promising nanotherapeutics; the risk assessment and knowledge-based quality targeted QbD approach provides a promising tool to support the development process. Based on risk assessment, many factors pose great risk in the manufacturing process and affect the quality, efficacy and safety profile. The quality-driven strategic development pathway, based on deep prior knowledge and an involving iterative risk estimation and management phases has proven to be an adequate tool, being able to handle their sensitive stability issues and make them efficient therapeutic aids in case of several diseases.
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Grunt, Maciej, Andrzej Błażejewski, Sebastian Pecolt, and Tomasz Królikowski. "BelBuk System—Smart Logistics for Sustainable City Development in Terms of the Deficit of a Chemical Fertilizers." Energies 15, no. 13 (June 23, 2022): 4591. http://dx.doi.org/10.3390/en15134591.
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Purpose: This paper presents an aspect of asset tracking and storage conditions. This paper aims to fill the gap in the development of Industry 4.0 in terms of fully digital asset tracking to be implemented by medium and large-size manufacturing and logistics facilities. The article presents an innovative technology for the remote monitoring of chemical raw materials, including fertilizers, during their storage and transport from the place of manufacture to the local distributor or recipient. Methods: The method assumes the monitoring and identification of special transport bags, so-called “big-bags,” through embedded RFID tags or LEB labels and monitoring the key parameters of their content, i.e., temperature, humidity, insolation, and pressure, using a measuring micro-station that is placed in the transported raw material. Results: The automation of inference based on the collected information about the phenomenon in question (the distribution of parameters: pressure, temperature, and humidity), and expert knowledge, allows the creation of an advisory system prototype indicating how to manage the measuring devices. Conclusions: No similar solution in the field of monitoring environmental parameters has been implemented in the Polish market. The developed system enables the monitoring of 10,000 pieces of big bags in at least 30 locations simultaneously.
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Łabędzka, Joanna. "Industry 4.0 — policy-based approaches to efficient implementation in SMEs." Engineering Management in Production and Services 13, no. 4 (December 1, 2021): 72–78. http://dx.doi.org/10.2478/emj-2021-0032.
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Abstract Industry 4.0 (I4.0), driven by the need to access real-time insights and information across the manufacturing process, creates a disruptive impact on industries. Large-scale machine-to-machine communication, virtual reality (VR), the Internet of Things (IoT), simulation technologies and network management are integrated for increased automation, machine learning, self-controlled social and technical systems (Smart Factories). The uptake of advanced manufacturing solutions represents a challenge for businesses and SMEs in particular. SMEs possess neither the organisational capability nor financial resources to systematically investigate the potential and risks of introducing Industry 4.0. However, the so-called Fourth Industrial Revolution is a matter of technology and cooperation between European regions to share knowledge concerning alternative regional and national approaches to reinforcing the I4.0 uptake. Therefore, this paper primarily aims to analyse practical experience on how European policies related to the European Regional Development Fund (ERDF) can unlock the full potential of Industry 4.0 and overcome the fragmentation of Industry 4.0 solutions. Case studies of successful transfer of I4.0 to SMEs in Europe and supporting regional policy instruments presented in the paper could inspire and enable the potential of digitalisation by dealing with main challenges hampering their diffusion into the business ecosystem.