Academic literature on the topic 'Knowledge based smart manufacturing system'
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Journal articles on the topic "Knowledge based smart manufacturing system"
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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.
Dissertations / Theses on the topic "Knowledge based smart manufacturing system"
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Cao, Qiushi. "Semantic technologies for the modeling of predictive maintenance for a SME network in the framework of industry 4.0 Smart condition monitoring for industry 4.0 manufacturing processes: an ontology-based approach Using rule quality measures for rule base refinement in knowledge-based predictive maintenance systems Combining chronicle mining and semantics for predictive maintenance in manufacturing processes." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR04.
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Dans le domaine de la fabrication, la détection d’anomalies telles que les défauts et les défaillances mécaniques permet de lancer des tâches de maintenance prédictive, qui visent à prévoir les défauts, les erreurs et les défaillances futurs et à permettre des actions de maintenance. Avec la tendance de l’industrie 4.0, les tâches de maintenance prédictive bénéficient de technologies avancées telles que les systèmes cyberphysiques (CPS), l’Internet des objets (IoT) et l’informatique dématérialisée (cloud computing). Ces technologies avancées permettent la collecte et le traitement de données de capteurs qui contiennent des mesures de signaux physiques de machines, tels que la température, la tension et les vibrations. Cependant, en raison de la nature hétérogène des données industrielles, les connaissances extraites des données industrielles sont parfois présentées dans une structure complexe. Des méthodes formelles de représentation des connaissances sont donc nécessaires pour faciliter la compréhension et l’exploitation des connaissances. En outre, comme les CPSs sont de plus en plus axées sur la connaissance, une représentation uniforme de la connaissance des ressources physiques et des capacités de raisonnement pour les tâches analytiques est nécessaire pour automatiser les processus de prise de décision dans les CPSs. Ces problèmes constituent des obstacles pour les opérateurs de machines qui doivent effectuer des opérations de maintenance appropriées. Pour relever les défis susmentionnés, nous proposons dans cette thèse une nouvelle approche sémantique pour faciliter les tâches de maintenance prédictive dans les processus de fabrication. En particulier, nous proposons quatre contributions principales: i) un cadre ontologique à trois niveaux qui est l’élément central d’un système de maintenance prédictive basé sur la connaissance; ii) une nouvelle approche sémantique hybride pour automatiser les tâches de prédiction des pannes de machines, qui est basée sur l’utilisation combinée de chroniques (un type plus descriptif de modèles séquentiels) et de technologies sémantiques; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) une nouvelle approche d’affinement de la base de règles qui utilise des mesures de qualité des règles comme références pour affiner une base de règles dans un système de maintenance prédictive basé sur la connaissance. Ces approches ont été validées sur des ensembles de données réelles et synthétiquesIn the manufacturing domain, the detection of anomalies such as mechanical faults and failures enables the launching of predictive maintenance tasks, which aim to predict future faults, errors, and failures and also enable maintenance actions. With the trend of Industry 4.0, predictive maintenance tasks are benefiting from advanced technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and Cloud Computing. These advanced technologies enable the collection and processing of sensor data that contain measurements of physical signals of machinery, such as temperature, voltage, and vibration. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. Therefore formal knowledge representation methods are required to facilitate the understanding and exploitation of the knowledge. Furthermore, as the CPSs are becoming more and more knowledge-intensive, uniform knowledge representation of physical resources and reasoning capabilities for analytic tasks are needed to automate the decision-making processes in CPSs. These issues bring obstacles to machine operators to perform appropriate maintenance actions. To address the aforementioned challenges, in this thesis, we propose a novel semantic approach to facilitate predictive maintenance tasks in manufacturing processes. In particular, we propose four main contributions: i) a three-layered ontological framework that is the core component of a knowledge-based predictive maintenance system; ii) a novel hybrid semantic approach to automate machinery failure prediction tasks, which is based on the combined use of chronicles (a more descriptive type of sequential patterns) and semantic technologies; iii) a new approach that uses clustering methods with Semantic Web Rule Language (SWRL) rules to assess failures according to their criticality levels; iv) a novel rule base refinement approach that uses rule quality measures as references to refine a rule base within a knowledge-based predictive maintenance system. These approaches have been validated on both real-world and synthetic data sets
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蕭世良 and Sai-leung Siu. "A knowledge based process planning system for prismatic parts." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31232784.
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Govindan, Saravana. "A task based manufacturing knowledge maintenance method." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/12414.
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The effective use of computer based tools to support decision making in manufacturing industry is critical to business success. One of the most critical areas is during product design and especially in design for manufacture. This research will help in understanding of how manufacturing knowledge can be effectively maintained for an existing knowledge base. The work will use modern product lifecycle management tools in combination with a knowledge based environment in order to explore the effectiveness of the methods produced. This work is a part of the SAMULET (Strategic Affordable Manufacturing in the UK through Leading Environmental Technologies) research program and was done in association with an aerospace manufacturing company. The main focus of this research is to define a novel method for maintaining the machining knowledge associated with manufacturing of Xtra Wide Body (XWB) High Pressure (HP) turbine blade. The four main elements explained in this thesis are, a) the literature review done on knowledge management and knowledge maintenance, b) industrial investigation done on a manufacturing facility, c) detailed explanation of a novel manufacturing knowledge maintenance method d) four iterative case studies used for the evaluation and iterative improvement of the method. The research concludes that the aspect of knowledge maintenance is important. It is imperative to set out a formalised and mandated knowledge maintenance process in an organisation to keep the knowledge up-to-date and relevant. It has been shown that a novel task based knowledge maintenance method comprising a Knowledge Maintenance Process (KMP) and a Knowledge Maintenance Template (KMT) provides an effective route to knowledge maintenance. Three maintenance tasks, check relevancy, knowledge filtering, and integrity checking have been considered in detail for successful knowledge maintenance. Four iterative case studies have been conducted for the experimental evaluation of the maintenance method. As the result of these evaluations a novel method for maintaining the machining knowledge of XWB HP turbine blade was defined.
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Roy, Asok K. "Development of a knowledge based vision system for automated inspection." Thesis, Glasgow Caledonian University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363131.
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Wondoloski, Karen M. "A knowledge-based cell controller and its integration in a manufacturing system." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/23341.
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Sun, Qi-zhi. "Knowledge-based interactive real-time control system in product-focused manufacturing environment." Thesis, University of Portsmouth, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292501.
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Al-Awadhi, Waleed. "Integrating machine grouping and layout by using knowledge based system approach." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242982.
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Al-Khawaldeh, Mustafa Awwad Salem. "Ubiquitous robotics system for knowledge-based auto-configuration system for service delivery within smart home environments." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10202.
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The future smart home will be enhanced and driven by the recent advance of the Internet of Things (IoT), which advocates the integration of computational devices within an Internet architecture on a global scale [1, 2]. In the IoT paradigm, the smart home will be developed by interconnecting a plethora of smart objects both inside and outside the home environment [3-5]. The recent take-up of these connected devices within home environments is slowly and surely transforming traditional home living environments. Such connected and integrated home environments lead to the concept of the smart home, which has attracted significant research efforts to enhance the functionality of home environments with a wide range of novel services. The wide availability of services and devices within contemporary smart home environments make their management a challenging and rewarding task. The trend whereby the development of smart home services is decoupled from that of smart home devices increases the complexity of this task. As such, it is desirable that smart home services are developed and deployed independently, rather than pre-bundled with specific devices, although it must be recognised that this is not always practical. Moreover, systems need to facilitate the deployment process and cope with any changes in the target environment after deployment. Maintaining complex smart home systems throughout their lifecycle entails considerable resources and effort. These challenges have stimulated the need for dynamic auto-configurable services amongst such distributed systems. Although significant research has been directed towards achieving auto-configuration, none of the existing solutions is sufficient to achieve auto-configuration within smart home environments. All such solutions are considered incomplete, as they lack the ability to meet all smart home requirements efficiently. These requirements include the ability to adapt flexibly to new and dynamic home environments without direct user intervention. Fulfilling these requirements would enhance the performance of smart home systems and help to address cost-effectiveness, considering the financial implications of the manual configuration of smart home environments. Current configuration approaches fail to meet one or more of the requirements of smart homes. If one of these approaches meets the flexibility criterion, the configuration is either not executed online without affecting the system or requires direct user intervention. In other words, there is no adequate solution to allow smart home systems to adapt dynamically to changing circumstances, hence to enable the correct interconnections among its components without direct user intervention and the interruption of the whole system. Therefore, it is necessary to develop an efficient, adaptive, agile and flexible system that adapts dynamically to each new requirement of the smart home environment. This research aims to devise methods to automate the activities associated with customised service delivery for dynamic home environments by exploiting recent advances in the field of ubiquitous robotics and Semantic Web technologies. It introduces a novel approach called the Knowledge-based Auto-configuration Software Robot (Sobot) for Smart Home Environments, which utilises the Sobot to achieve auto-configuration of the system. The research work was conducted under the Distributed Integrated Care Services and Systems (iCARE) project, which was designed to accomplish and deliver integrated distributed ecosystems with a homecare focus. The auto-configuration Sobot which is the focus of this thesis is a key component of the iCARE project. It will become one of the key enabling technologies for generic smart home environments. It has a profound impact on designing and implementing a high quality system. Its main role is to generate a feasible configuration that meets the given requirements using the knowledgebase of the smart home environment as a core component. The knowledgebase plays a pivotal role in helping the Sobot to automatically select the most appropriate resources in a given context-aware system via semantic searching and matching. Ontology as a technique of knowledgebase representation generally helps to design and develop a specific domain. It is also a key technology for the Semantic Web, which enables a common understanding amongst software agents and people, clarifies the domain assumptions and facilitates the reuse and analysis of its knowledge. The main advantages of the Sobot over traditional applications is its awareness of the changing digital and physical environments and its ability to interpret these changes, extract the relevant contextual data and merge any new information or knowledge. The Sobot is capable of creating new or alternative feasible configurations to meet the system's goal by utilising inferred facts based on the smart home ontological model, so that the system can adapt to the changed environment. Furthermore, the Sobot has the capability to execute the generated reconfiguration plan without interrupting the running of the system. A proof-of-concept testbed has been designed and implemented. The case studies carried out have shown the potential of the proposed approach to achieve flexible and reliable auto-configuration of the smart home system, with promising directions for future research.
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Mahmood, Tariq. "Knowledge-based process planning and design system for the cold forging of steel." Thesis, Imperial College London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264352.
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Chun-Kit, Kwong. "A computer-aided concurrent design system." Thesis, University of Warwick, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263309.
Full textBooks on the topic "Knowledge based smart manufacturing system"
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Sun, Qi-zhi. Knowledge-based interactive real-time control system in product-focused manufacturing environment. Portsmouth: Portsmouth Polytechnic, Business School, 1991.
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Zhang, Jie. A prototype of tape tuning system with knowledge-based techniques. 1990.
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Wei, Wang. A knowledge based modelling system for the design and evaluation of flexible manufacturing facilities. 1989.
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Nowak, Dariusz, ed. Production–operation management. The chosen aspects. Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, 2021. http://dx.doi.org/10.18559/978-83-8211-059-3.
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The aim of the e-book is to present the theoretical, cognitive and practical aspects of the essence and complexity of operational management in a production company. The presented modern production methods together with the challenges and problems of contemporary enterprises should better help to understand the issues of sustainable development, with particular emphasis on waste. The book consists of six chapters devoted to relevant and topic issues relating to the core business of an industrial enterprise. Chapter 1 The nature of the industrial enterprise is an introduction to further considerations and deals with the essence of the basic aspects of the company. Both popular and less known definitions of an enterprise, its features, functions and principles of operation are presented. An important part of the chapter is the presentation and formulation of strategic, tactical and operational goals. Moreover, the division of enterprises is presented with the use of various criteria and the features of the industrial market, which make it distinct. Chapter 2 The operational management evolution and its role in the industrial enterprise discusses the evolution and concept of production and operational management. The management levels were also presented, indicating their most important functions. An integral part of the chapter is the essence of the production system, viewed through the prism of the five elements. Chapter 3 Functions and role in operations management presents the issues concerning the organization of production processes, production capacity and inventory management. This part also presents considerations on cooperation and collaboration between enterprises in the process of creating value. Chapter 4 Traditional methods used in operational activities focuses on methods such as benchmarking, outsourcing, core competences, JIT, MPR I and MRP II, as well as TQM and kaizen. Knowledge of these methods should contribute to understanding the activities of modern enterprises, the way of company functioning, the realization of production activities, as well as aspects related to building a competitive position. Chapter 5 Modern methods used in production-operations management discusses the less common and less frequently used production methods, based on a modern and innovative approach. In particular, it was focused on: Shop Floor Control and cooperative manufacturing, environment-conscious manufacturing (ECM) and life-cycle assessment ( LCA), waste management and recycling, Electronic Data Interchange (EDI), virtual enterprise, World Class Manufacturing (WCM), Quality Function Deployment (QFD) and House of Quality (HOQ), theory of constraints (TOC), Drum Buffer Rope (DBR), group technology (GT) and cellular manufacturing (CM), Demand Chain Management and competitive intelligence (CI). In the last section discusses: the role of sustainable statistical process control and Computer-Aided Process Planning in context formatting of information management. Chapter 6 Problems of sustainable development and challenges related to production and operations management describes the problem and challenges related to production and operations activities. In particular, attention was paid to the threats related to changes in global warming, the growing scale of waste, or the processes of globalization. It was pointed out that the emerging problem may be both a threat and a chance for the development of enterprises. An integral part of the chapter are also considerations on technical progress, innovation and the importance of human capital in operational activities.
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Shengelia, Revaz. Modern Economics. Universal, Georgia, 2021. http://dx.doi.org/10.36962/rsme012021.
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Economy and mankind are inextricably interlinked. Just as the economy or the production of material wealth is unimaginable without a man, so human existence and development are impossible without the wealth created in the economy. Shortly, both the goal and the means of achieving and realization of the economy are still the human resources. People have long ago noticed that it was the economy that created livelihoods, and the delays in their production led to the catastrophic events such as hunger, poverty, civil wars, social upheavals, revolutions, moral degeneration, and more. Therefore, the special interest of people in understanding the regulatory framework of the functioning of the economy has existed and exists in all historical epochs [A. Sisvadze. Economic theory. Part One. 2006y. p. 22]. The system of economic disciplines studies economy or economic activities of a society. All of them are based on science, which is currently called economic theory in the post-socialist space (the science of economics, the principles of economics or modern economics), and in most countries of the world - predominantly in the Greek-Latin manner - economics. The title of the present book is also Modern Economics. Economics (economic theory) is the science that studies the efficient use of limited resources to produce and distribute goods and services in order to satisfy as much as possible the unlimited needs and demands of the society. More simply, economics is the science of choice and how society manages its limited resources. Moreover, it should be emphasized that economics (economic theory) studies only the distribution, exchange and consumption of the economic wealth (food, beverages, clothing, housing, machine tools, computers, services, etc.), the production of which is possible and limited. And the wealth that exists indefinitely: no economic relations are formed in the production and distribution of solar energy, air, and the like. This current book is the second complete updated edition of the challenges of the modern global economy in the context of the coronary crisis, taking into account some of the priority directions of the country's development. Its purpose is to help students and interested readers gain a thorough knowledge of economics and show them how this knowledge can be applied pragmatically (professionally) in professional activities or in everyday life. To achieve this goal, this textbook, which consists of two parts and tests, discusses in simple and clear language issues such as: the essence of economics as a science, reasons for origin, purpose, tasks, usefulness and functions; Basic principles, problems and peculiarities of economics in different economic systems; Needs and demand, the essence of economic resources, types and limitations; Interaction, mobility, interchangeability and efficient use of economic resources. The essence and types of wealth; The essence, types and models of the economic system; The interaction of households and firms in the market of resources and products; Market mechanism and its elements - demand, supply and price; Demand and supply elasticity; Production costs and the ways to reduce them; Forms of the market - perfect and incomplete competition markets and their peculiarities; Markets for Production Factors and factor incomes; The essence of macroeconomics, causes and importance of origin; The essence and calculation of key macroeconomic indicators (gross national product, gross domestic product, net national product, national income, etc.); Macroeconomic stability and instability, unemployment, inflation and anti-inflationary policies; State regulation of the economy and economic policy; Monetary and fiscal policy; Income and standard of living; Economic Growth; The Corona Pandemic as a Defect and Effect of Globalization; National Economic Problems and New Opportunities for Development in the conditions of the Coronary Crisis; The Socio-economic problems of moral obsolescence in digital technologies; Education and creativity are the main solution way to overcome the economic crisis caused by the coronavirus; Positive and negative effects of tourism in Georgia; Formation of the middle class as a contributing factor to the development of tourism in Georgia; Corporate culture in Georgian travel companies, etc. The axiomatic truth is that economics is the union of people in constant interaction. Given that the behavior of the economy reflects the behavior of the people who make up the economy, after clarifying the essence of the economy, we move on to the analysis of the four principles of individual decision-making. Furtermore, the book describes how people make independent decisions. The key to making an individual decision is that people have to choose from alternative options, that the value of any action is measured by the value of what must be given or what must be given up to get something, that the rational, smart people make decisions based on the comparison of the marginal costs and marginal returns (benefits), and that people behave accordingly to stimuli. Afterwards, the need for human interaction is then analyzed and substantiated. If a person is isolated, he will have to take care of his own food, clothes, shoes, his own house and so on. In the case of such a closed economy and universalization of labor, firstly, its productivity will be low and, secondly, it will be able to consume only what it produces. It is clear that human productivity will be higher and more profitable as a result of labor specialization and the opportunity to trade with others. Indeed, trade allows each person to specialize, to engage in the activities that are most successful, be it agriculture, sewing or construction, and to buy more diverse goods and services from others at a relatively lower price. The key to such human interactions is that trade is mutually beneficial; That markets are usually the good means of coordination between people and that the government can improve the results of market functioning if the market reveals weakness or the results of market functioning are not fair. Moroever, it also shows how the economy works as a whole. In particular, it is argued that productivity is a key determinant of living standards, that an increase in the money supply is a major source of inflation, and that one of the main impediments to avoiding inflation is the existence of an alternative between inflation and unemployment in the short term, that the inflation decrease causes the temporary decline in unemployement and vice versa. The Understanding creatively of all above mentioned issues, we think, will help the reader to develop market economy-appropriate thinking and rational economic-commercial-financial behaviors, to be more competitive in the domestic and international labor markets, and thus to ensure both their own prosperity and the functioning of the country's economy. How he/she copes with the tasks, it is up to the individual reader to decide. At the same time, we will receive all the smart useful advices with a sense of gratitude and will take it into account in the further work. We also would like to thank the editor and reviewers of the books. Finally, there are many things changing, so it is very important to realize that the XXI century has come: 1. The century of the new economy; 2. Age of Knowledge; 3. Age of Information and economic activities are changing in term of innovations. 1. Why is the 21st century the century of the new economy? Because for this period the economic resources, especially non-productive, non-recoverable ones (oil, natural gas, coal, etc.) are becoming increasingly limited. According to the World Energy Council, there are currently 43 years of gas and oil reserves left in the world (see “New Commersant 2007 # 2, p. 16). Under such conditions, sustainable growth of real gross domestic product (GDP) and maximum satisfaction of uncertain needs should be achieved not through the use of more land, labor and capital (extensification), but through more efficient use of available resources (intensification) or innovative economy. And economics, as it was said, is the science of finding the ways about the more effective usage of the limited resources. At the same time, with the sustainable growth and development of the economy, the present needs must be met in a way that does not deprive future generations of the opportunity to meet their needs; 2. Why is the 21st century the age of knowledge? Because in a modern economy, it is not land (natural resources), labor and capital that is crucial, but knowledge. Modern production, its factors and products are not time-consuming and capital-intensive, but science-intensive, knowledge-intensive. The good example of this is a Japanese enterprise (firm) where the production process is going on but people are almost invisible, also, the result of such production (Japanese product) is a miniature or a sample of how to get the maximum result at the lowest cost; 3. Why is the 21st century the age of information? Because the efficient functioning of the modern economy, the effective organization of the material and personal factors of production largely depend on the right governance decision. The right governance decision requires prompt and accurate information. Gone are the days when the main means of transport was a sailing ship, the main form of data processing was pencil and paper, and the main means of transmitting information was sending letters through a postman on horseback. By the modern transport infrastructure (highways, railways, ships, regular domestic and international flights, oil and gas pipelines, etc.), the movement of goods, services and labor resoucres has been significantly accelerated, while through the modern means of communication (mobile phone, internet, other) the information is spreading rapidly globally, which seems to have "shrunk" the world and made it a single large country. The Authors of the book: Ushangi Samadashvili, Doctor of Economic Sciences, Associate Professor of Ivane Javakhishvili Tbilisi State University - Introduction, Chapters - 1, 2, 3, 4, 5, 6, 9, 10, 11,12, 15,16, 17.1,18 , Tests, Revaz Shengelia, Doctor of Economics, Professor of Georgian Technical University, Chapters_7, 8, 13. 14, 17.2, 17.4; Zhuzhuna Tsiklauri - Doctor of Economics, Professor of Georgian Technical University - Chapters 13.6, 13.7,17.2, 17.3, 18. We also thank the editor and reviewers of the book.
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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research centers and directions in different regions of Russia; fourthly, to outline the main directions of the further development of Russian psycholinguistics. There is no doubt that in the theoretical, methodological and applied aspects, the main problems and the results of their development by Russian psycholinguistics have no analogues in world linguistics and psycholinguistics, or are represented by completely original concepts and methods. We have tried to show this uniqueness of the problematics and the methodological equipment of Russian psycholinguistics in this book. The main role in the formation of Russian psycholinguistics was played by the Moscow psycholinguistic school of A.A. Leontyev. It still defines the main directions of Russian psycholinguistics. Russian psycholinguistics (the theory of speech activity - TSA) is based on the achievements of Russian psychology: a cultural-historical approach to the analysis of mental phenomena L.S. Vygotsky and the system-activity approach of A.N. Leontyev. Moscow is the most "psycholinguistic region" of Russia - INL RAS, Moscow State University, Moscow State Linguistic University, RUDN, Moscow State Pedagogical University, Moscow State Pedagogical University, Sechenov University, Moscow State University and other Moscow universities. Saint Petersburg psycholinguists have significant achievements, especially in the study of neurolinguistic problems, ontolinguistics. The most important feature of Russian psycholinguistics is the widespread development of psycholinguistics in the regions, the emergence of recognized psycholinguistic research centers - St. Petersburg, Tver, Saratov, Perm, Ufa, Omsk, Novosibirsk, Voronezh, Yekaterinburg, Kursk, Chelyabinsk; psycholinguistics is represented in Cherepovets, Ivanovo, Volgograd, Vyatka, Kaluga, Krasnoyarsk, Irkutsk, Vladivostok, Abakan, Maikop, Barnaul, Ulan-Ude, Yakutsk, Syktyvkar, Armavir and other cities; in Belarus - Minsk, in Ukraine - Lvov, Chernivtsi, Kharkov, in the DPR - Donetsk, in Kazakhstan - Alma-Ata, Chimkent. Our researchers work in Bulgaria, Hungary, Vietnam, China, France, Switzerland. There are Russian psycholinguists in Canada, USA, Israel, Austria and a number of other countries. All scientists from these regions and countries have contributed to the development of Russian psycholinguistics, to the development of psycholinguistic theory and methods of psycholinguistic research. Their participation has not been forgotten. We tried to present the main Russian psycholinguists in the Appendix - in the sections "Scientometrics", "Monographs and Manuals" and "Dissertations", even if there is no information about them in the Electronic Library and RSCI. The principles of including scientists in the scientometric list are presented in the Appendix. Our analysis of the content of the resulting monograph on psycholinguistic research in Russia allows us to draw preliminary conclusions about some of the distinctive features of Russian psycholinguistics: 1. cultural-historical approach to the analysis of mental phenomena of L.S.Vygotsky and the system-activity approach of A.N. Leontiev as methodological basis of Russian psycholinguistics; 2. theoretical nature of psycholinguistic research as a characteristic feature of Russian psycholinguistics. Our psycholinguistics has always built a general theory of the generation and perception of speech, mental vocabulary, linked specific research with the problems of ontogenesis, the relationship between language and thinking; 3. psycholinguistic studies of speech communication as an important subject of psycholinguistics; 4. attention to the psycholinguistic analysis of the text and the development of methods for such analysis; 5. active research into the ontogenesis of linguistic ability; 6. investigation of linguistic consciousness as one of the important subjects of psycholinguistics; 7. understanding the need to create associative dictionaries of different types as the most important practical task of psycholinguistics; 8. widespread use of psycholinguistic methods for applied purposes, active development of applied psycholinguistics. The review of the main directions of development of Russian psycholinguistics, carried out in this monograph, clearly shows that the direction associated with the study of linguistic consciousness is currently being most intensively developed in modern Russian psycholinguistics. As the practice of many years of psycholinguistic research in our country shows, the subject of study of psycholinguists is precisely linguistic consciousness - this is a part of human consciousness that is responsible for generating, understanding speech and keeping language in consciousness. Associative experiments are the core of most psycholinguistic techniques and are important both theoretically and practically. The following main areas of practical application of the results of associative experiments can be outlined. 1. Education. Associative experiments are the basis for constructing Mind Maps, one of the most promising tools for systematizing knowledge, assessing the quality, volume and nature of declarative knowledge (and using special techniques and skills). Methods based on smart maps are already widely used in teaching foreign languages, fast and deep immersion in various subject areas. 2. Information search, search optimization. The results of associative experiments can significantly improve the quality of information retrieval, its efficiency, as well as adaptability for a specific person (social group). When promoting sites (promoting them in search results), an associative experiment allows you to increase and improve the quality of the audience reached. 3. Translation studies, translation automation. An associative experiment can significantly improve the quality of translation, take into account intercultural and other social characteristics of native speakers. 4. Computational linguistics and automatic word processing. The results of associative experiments make it possible to reveal the features of a person's linguistic consciousness and contribute to the development of automatic text processing systems in a wide range of applications of natural language interfaces of computer programs and robotic solutions. 5. Advertising. The use of data on associations for specific words, slogans and texts allows you to predict and improve advertising texts. 6. Social relationships. The analysis of texts using the data of associative experiments makes it possible to assess the tonality of messages (negative / positive moods, aggression and other characteristics) based on user comments on the Internet and social networks, in the press in various projections (by individuals, events, organizations, etc.) from various social angles, to diagnose the formation of extremist ideas. 7. Content control and protection of personal data. Associative experiments improve the quality of content detection and filtering by identifying associative fields in areas subject to age restrictions, personal information, tobacco and alcohol advertising, incitement to ethnic hatred, etc. 8. Gender and individual differences. The data of associative experiments can be used to compare the reactions (and, in general, other features of thinking) between men and women, different social and age groups, representatives of different regions. The directions for the further development of Russian psycholinguistics from the standpoint of the current state of psycholinguistic science in the country are seen by us, first of all: in the development of research in various areas of linguistic consciousness, which will contribute to the development of an important concept of speech as a verbal model of non-linguistic consciousness, in which knowledge revealed by social practice and assigned by each member of society during its inculturation is consolidated for society and on its behalf; in the expansion of the problematics, which is formed under the influence of the growing intercultural communication in the world community, which inevitably involves the speech behavior of natural and artificial bilinguals in the new object area of psycholinguistics; in using the capabilities of national linguistic corpora in the interests of researchers studying the functioning of non-linguistic and linguistic consciousness in speech processes; in expanding research on the semantic perception of multimodal texts, the scope of which has greatly expanded in connection with the spread of the Internet as a means of communication in the life of modern society; in the inclusion of the problems of professional communication and professional activity in the object area of psycholinguistics in connection with the introduction of information technologies into public practice, entailing the emergence of new professions and new features of the professional ethos; in the further development of the theory of the mental lexicon (identifying the role of different types of knowledge in its formation and functioning, the role of the word as a unit of the mental lexicon in the formation of the image of the world, as well as the role of the natural / internal metalanguage and its specificity in speech activity); in the broad development of associative lexicography, which will meet the most diverse needs of society and cognitive sciences. The development of associative lexicography may lead to the emergence of such disciplines as associative typology, associative variantology, associative axiology; in expanding the spheres of applied use of psycholinguistics in social sciences, sociology, semasiology, lexicography, in the study of the brain, linguodidactics, medicine, etc. This book is a kind of summarizing result of the development of Russian psycholinguistics today. Each section provides a bibliography of studies on the relevant issue. The Appendix contains the scientometrics of leading Russian psycholinguists, basic monographs, psycholinguistic textbooks and dissertations defended in psycholinguistics. The content of the publications presented here is convincing evidence of the relevance of psycholinguistic topics and the effectiveness of the development of psycholinguistic problems in Russia.
Book chapters on the topic "Knowledge based smart manufacturing system"
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Gembarski, Paul Christoph. "Design Catalogues as Knowledge-Base for CAD-Based Design Automation." In Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems, 645–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90700-6_73.
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Szejka, Anderson Luis, Fernando Mas, and Osiris Canciglieri Junior. "Towards Knowledge-Based System to Support Smart Manufacturing Processes in Aerospace Industry Based on Models for Manufacturing (MfM)." In Product Lifecycle Management. Green and Blue Technologies to Support Smart and Sustainable Organizations, 425–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94399-8_31.
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Park, Byungjun, and Jongpil Jeong. "Knowledge-Based Multi-agent System for Smart Factory of Small-Sized Manufacturing Enterprises in Korea." In Computational Science and Its Applications – ICCSA 2019, 81–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24311-1_6.
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Gembarski, Paul Christoph, Dörthe Behrens, Jan Feldkamp, Lorenz Kies, and Lukas Hoppe. "Design Automation of a Motor Hoisting Crane – Results of Student Project on Knowledge-Based CAD." In Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems, 927–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90700-6_106.
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Torim, Ants, Innar Liiv, Chahinez Ounoughi, and Sadok Ben Yahia. "Pattern Based Software Architecture for Predictive Maintenance." In Communications in Computer and Information Science, 26–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_3.
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AbstractMany industrial sectors are moving toward Industry Revolution (IR) 4.0. In this respect, the Internet of Things and predictive maintenance are considered the key pillars of IR 4.0. Predictive maintenance is one of the hottest trends in manufacturing where maintenance work occurs according to continuous monitoring using a healthiness check for processing equipment or instrumentation. It enables the maintenance team to have an advanced prediction of failures and allows the team to undertake timely corrective actions and decisions ahead of time. The aim of this paper is to present a smart monitoring and diagnostics system as an expert system that can alert an operator before equipment failures to prevent material and environmental damages. The main novelty and contribution of this paper is a flexible architecture of the predictive maintenance system, based on software patterns - flexible solutions to general problems. The presented conceptual model enables the integration of an expert knowledge of anticipated failures and the matrix-profile technique based anomaly detection. The results so far are encouraging.
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Reisinger, Gerhard, Philipp Hold, and Wilfried Sihn. "Automated Information Supply of Worker Guidance Systems in Smart Assembly Environment." In IFIP Advances in Information and Communication Technology, 235–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72632-4_17.
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AbstractThe global megatrends of digitization and individualization substantially affect manufacturing enterprises. Assembly workers are exposed to increased process complexity resulting in physical and cognitive workload. Worker guidance systems (WGS) are used to overcome this challenge through output of information regarding what should be done, how it should be done and why it should be done. An unsolved scientific challenge in this context is efficient information supply of WGS. Information such as worker’s instruction texts, pictures or 3D representations are created by employees of the work preparation department and transferred to the WGS. Manual information supply is a time-consuming and complex process, which requires a high (non-value-adding) effort as well as comprehensive knowledge in handling 3D CAD modelling and software programming. This paper presents a novel approach to reduce the required manual effort in information supply process. A knowledge-based model is proposed that enables an automated information supply of WGS in smart assembly environment by means of algorithms and self-learning expert systems, which pursues a holistic and consistent approach without media breaks. The automated approach assists employees of work preparation department, which means they can concentrate on their essential core competencies instead of being busy, for example, creating assembly plans, instruction texts or pictures for individual WGS. Finally, the technical implementation as a software-based proof-of-concept demonstrator and sub-sequent integration into the IT environment of TU Wien Pilot Factory Industry 4.0 is outlined.
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Behrendt, Wernher, and Felix Strohmeier. "Orchestration and Situation Awareness in an Assistance System for Assembly Tasks." In IFIP Advances in Information and Communication Technology, 332–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72632-4_25.
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AbstractWe report on the design, specification and implementation of a situation awareness module used for assistive systems in manufacturing, in the context of Industry 4.0. A recent survey of research done in Germany and Europe, concerning assistive technology in industry shows a very high potential for “intelligent assistance” by combining smart sensors, networking and AI. While the state of the art concerning actual technology in industrial use points more towards user-friendly, speech-based interaction with personal assistants for information retrieval (typically of in-house documentation), the research presented here addresses an enterprise-level assistance system that is supported by a number of specialized Assistance Units that can be customized to the end users’ specifications and that range from tutoring systems to tele-robotics. Key to the approach is situation awareness, which is achieved through a combination of a-priori, task knowledge modelling and dynamic situation assessment on the basis of observation streams coming from sensors, cameras and microphones. The paper describes a working fragment of the industrial task description language and its extensions to cover also the triggering of assistive interventions when the observation modules have sent data that warrants such interventions.
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Liu, Hongxia, Qingcheng Hu, Yong Zhang, Chunxiao Xing, and Ming Sheng. "A Knowledge-Based Health Question Answering System." In Smart Health, 286–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67964-8_29.
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Zhu, Kunpeng. "Machine Vision Based Smart Machining System Monitoring." In Springer Series in Advanced Manufacturing, 267–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87878-8_8.
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Liu, Penghe, Xiaoqing Wang, Xiaoping Sun, Xi Shen, Xu Chen, Yuzhong Sun, and Yanjun Pan. "HKDP: A Hybrid Knowledge Graph Based Pediatric Disease Prediction System." In Smart Health, 78–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59858-1_8.
Full textConference papers on the topic "Knowledge based smart manufacturing system"
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Brundage, Michael P., Boonserm Kulvatunyou, Toyosi Ademujimi, and Badarinath Rakshith. "Smart Manufacturing Through a Framework for a Knowledge-Based Diagnosis System." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2937.
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Various techniques are used to diagnose problems throughout all levels of the organization within the manufacturing industry. Often times, this root cause analysis is ad-hoc with no standard representation for artifacts or terminology (i.e., no standard representation for terms used in techniques such as fishbone diagrams, 5 why’s, etc.). Once a problem is diagnosed and alleviated, the results are discarded or stored locally as paper/digital text documents. When the same or similar problem reoccurs with different employees or in a different factory, the whole process has to be repeated without taking advantage of knowledge gained from previous problem(s) and corresponding solution(s). When discussing the diagnosis, personnel may miscommunicate over terms used in the root cause analysis leading to wasted time and errors. This paper presents a framework for a knowledge-based manufacturing diagnosis system that aims to alleviate these miscommunications. By learning from diagnosis methods used in manufacturing and in the medical community, this paper proposes a framework which integrates and formalizes root cause analysis by categorizing faults and failures that span multiple organizational levels. The proposed framework aims to enable manufacturing operations by leveraging machine learning and semantic technologies for the manufacturing system diagnosis. A use case for the manufacture of a bottle opener demonstrates the framework.
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Kuvychko, Igor. "Knowledge and vision engines: a new generation of image understanding systems combining computational intelligence methods and model-based knowledge representation and reasoning." In Intelligent Systems and Smart Manufacturing, edited by David P. Casasent. SPIE, 2000. http://dx.doi.org/10.1117/12.403787.
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Zuoxu, Wang, Li Xinyu, Chen Chun-Hsien, and Zheng Pai. "A Hypergraph-Based Knowledge Representation Model for Smart Product-Service System Development." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-66732.
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Abstract In the trend of digital servitization, manufacturing companies have been transforming their business paradigms to Smart product-service systems (Smart PSS) by integrating products and associated services as bundles. To support the knowledge-intensive process of Smart PSS development, massive domain knowledge should be well-organized and reused. However, due to the existence of non-binary relations caused by product-service bundles (PSB) and context-awareness concerns in the Smart PSS development activities, conventional graph-based approaches for knowledge representation may lose essential information in transforming non-binary relations into binary ones, and hence cause incorrect results in the subsequent knowledge queries. To mitigate this problem, a hypergraph-based knowledge representation model for Smart PSS was proposed, which represents the non-binary relations among multiple entities with hyperedges. Technically, the knowledge source and the typical hyperedge schema in Smart PSS development are identified in this paper. A detailed case study in the scenarios of 3D printing troubleshooting and PSB recommendation was conducted to showcase the proposed hypergraph-based knowledge representation model and demonstrate its validity. The results show that the hypergraph-based knowledge model significantly relieves the sparsity in the ordinary KG by adding multiple hyperedges. It is anticipated that the proposed hypergraph knowledge representation model can serve as a fundamental study for further knowledge reasoning activities.
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Wang, Xi Vincent, Brenda N. Lopez N., Lihui Wang, Jinhui Li, and Winifred Ijomah. "A Smart Cloud-Based System for the WEEE Recovery/Recycling." In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/msec2014-4109.
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Waste Electrical and Electronic Equipment (WEEE) is both valuable and harmful since it contains a large number of profitable and hazardous materials and elements at the same time. At component level, many parts of the discarded equipment are still functional and recoverable. Thus it is necessary to develop a distributed and intelligent system to support WEEE recovery and recycling. In recent years, the Cloud concept has gained increasing popularity since it provides a service-oriented architecture that integrates various resources over the network. Cloud Manufacturing systems are proposed world-wide to support operational manufacturing processes. In this research, Cloud Manufacturing is further extended to the WEEE recovery and recycling context. A Cloud-based WEEE Recovery system is developed to provide modularized recovery services on the Cloud. A data management system is developed as well, which maintains the knowledge throughout the product lifecycle. A product tracking mechanism is also proposed with the help of the Quick Respond code method.
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Kangru, Tavo, Kashif Mahmood, Tauno Otto, Madis Moor, and Jüri Riives. "Knowledge-Driven Based Performance Analysis of Robotic Manufacturing Cell for Design Improvement." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23541.
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Abstract Manufacturing companies must ensure high productivity and low production cost in rapidly changing market conditions. At the same time products and services are evolving permanently. In order to cope with those circumstances, manufacturers should apply the principles of smart manufacturing together with continuous processes improvement. Smart manufacturing is a concept where production is no longer highly labor-intensive and based only on flexible manufacturing systems, but production as a whole process should be monitored and controlled with sophisticated information technology, integrated on all stages of the product life cycle. Process improvements in Smart Manufacturing are heavily reliance on decisions, which can be achieved by using modeling and simulation of systems with different analyzing tools based on Big Data processing and Artificial Intelligence (AI) technologies. This study was performed to automate an estimation process and improve the accuracy for production cell’s performance evaluation. Although there have been researches performed in the same field, the substantial estimation process outcome and accuracy still need to be elaborated further. In this article a robot integrated production cell simulation framework is developed. A developed system is used to simulate production cell parametric models in the real-life situations. A set of rules and constraints are created and inserted into the simulation model. Data for the constraints were acquired by investigating industries’ best production cells performance parameters. Information was gathered in four main fields: company profile and strategy, cell layout and equipment, manufactured products process data and shortcomings of goal achievements or improvement necessary to perform. From those parametric case model, a 3D virtual manufacturing simulation model is built and simulated for achieving accurate results. The integration of manufacturing data into decision making process through advanced prescriptive analytics models is a one of the future tasks of this study. The integration makes it possible to use “best practice” data and obtained Key Performance Indicators (KPIs) results to find the optimal solutions in real manufacturing conditions. The objective is to find the best solution of robot integrated cell for a certain industry using AI enabled simulation model. It also helps to improve situation assessment and deliberated decision-making mechanism.
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Wei, Qiongqiong, and Yanshuo Zhu. "On the Structure Model of Knowledge Manufacturing and Service System of Smart Library Based on Super-Network." In 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2022. http://dx.doi.org/10.1109/ddcls55054.2022.9858543.
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Heilala, Janne, and Pawel Krolas. "Locating A Smart Manufacturing based on Supply Chain Segregation." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003899.
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The recent EMS 2021 small-scale pilot assessment in Finland covered a perspective for evaluating corporations' relocation activities, key enabling technologies, and organizational concepts. Relocation activities were reflected in off- or backshoring manufacturing or R&D. Depthness was taken by withdrawals from knowledge boundaries from the database content, which was combined with additive manufacturing and energy management systems. This is a representative study on the relocation factors, and what is outside scope is considered from the management human systems integrative angle of entry. The main research problem was can relocation activities be explained by the corporation's manufacturing key technologies and organizational concepts with cross-sectional indices of growth, manpower, and capital utilization. The response method was mixed in reviewing the EMS structural connections. The empirical finding content variables were bound with standardization and explaining scientific philosophy. As a result, the framework for relocation activities can explain firms' intentions over additive manufacturing and sustainable business. Conventionally selected literature sampling was used to identify likely supporting factors for the relocating activities. In conclusion, in terms of empirical findings, good politics, financing opportunities, and cooperation enable business growth and development.
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Scholz, S., A. Elkaseer, T. Muller, U. Gengenbach, and V. Hagenmeyer. "Smart modular reconfigurable fully-digital manufacturing system with a knowledge-based framework: example of a fabrication of microfluidic chips." In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). IEEE, 2018. http://dx.doi.org/10.1109/coase.2018.8560405.
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Diaz-Elsayed, Nancy, Luis Hernandez, Ravi Rajamani, and Brian A. Weiss. "Asset Condition Management: A Framework for Smart, Health-Ready Manufacturing Systems." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8326.
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Abstract Unscheduled downtime in manufacturing systems can be a major source of lost productivity, profits, and, ultimately, reduced process quality and reliability. However, the incorporation of asset condition management (ACM) into manufacturing systems offers an approach to improve equipment and plant operations by providing real-time condition awareness, system diagnostics, and estimates of future health to enable predictive maintenance. ACM is a framework for assessing the current and future state of health of a manufacturing system and integrating that knowledge with enterprise applications to meet the demand of production operations. In manufacturing systems, successful operations rely on the ability to maintain production assets at their optimal working levels to optimize operations and system performance. Some large corporations have made great strides in incorporating smart technologies to enhance their asset management strategy; however, small- and medium-sized enterprises (SMEs) face distinct challenges. One of the key challenges is that most SMEs do not have the wherewithal to invest in new machines nor is there standard guidance on how older machines can be integrated into an ACM solution, so that their end-to-end manufacturing process can be optimized from a health management point of view. This research presents a framework for ACM to facilitate its introduction into manufacturing systems based on their “health-ready” capabilities. Specifically, an ACM system architecture is defined for manufacturing systems, the health-ready principles and capability levels from the aerospace and automotive industries are adapted to the manufacturing domain, and the results from the outreach effort to the manufacturing community are discussed.
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Li, Yunpeng, and Utpal Roy. "Challenges in Developing a Computational Platform to Integrate Data Analytics With Simulation-Based Optimization." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46410.
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The focus of the work presented in this paper is to identify and find possible solutions for major implementation challenges in designing a computational platform for integrating data analytics paradigm with the simulation-based optimization technique to facilitate the modeling of a smart manufacturing system. A simulation model of a manufacturing system generates real-time monitoring data for machine status and these data are then mined by data mining algorithms to discover hidden knowledge that might not be predefined in the simulation model. The new knowledge is then fed into the simulation model such that the model adapts and evolves, and eventually it can predict future status. This procedure involves heterogeneous modeling techniques, information exchange among different tools, as well as model composition and interaction. We extend an early presented “Hypercube” information model that was specifically developed for the purpose of formal representation of smart manufacturing systems, in order to harmonize the information required by the simulation modeling tool and the data analytics tool. A strong emphasis is given to emerging areas of multi-domain and multiscale modeling by means of integration and interoperability between existing modeling tools and technologies. A specific case study related to preventive and predictive maintenance of a typical manufacturing system has been elaborated in the paper as the initial scope and application area in order to illustrate and validate the proposed computational framework.
Reports on the topic "Knowledge based smart manufacturing system"
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Canto, Patricia, ed. The role of vocational training knowledge intensive business services. (Main conclusions). Universidad de Deusto, 2020. http://dx.doi.org/10.18543/vyqr9353.
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In the global economic model, the service sector continues to gain ground on the manufacturing sector and trends such as the integration of new technologies into production processes are advancing inexorably. Advanced economies are pushed to specialise, supported by their regional innovation systems, and cities are emerging as key and strategic centres of activity. In this context, Knowledge Intensive Business Services (KIBS) are presented as critical due to their capacity to promote innovation within the regional productive fabric and smart specialisation strategies, the promotion of advanced manufacturing, the generation of quality employment and the stimulation of economic growth, especially in urban environments. This is why many cities, prioritizing KIBS to stimulate their economy, need to create and retain talent for this sort of industry. Likewise, vocational and education training (VET) systems, such as the Basque VET system, have so far developed their greatest strengths in the field of manufacturing knowledge. Due to this, VET seems to be obliged to adapt to this new scenario, in which KIBS and cities stand out, in order to continue to maintain their level of excellence. KIBS have been extensively examined, but until now no one had posed the following questions: What is the role of vocational training in KIBS? To what extent are VET profiles (and will VET profiles be) relevant in KIBS? This study will show an emerging trend in the labour market. This is the growing relevance of technology profiles with VET background in KIBS, especially in technology-based KIBS. VET technology profiles can be consolidated as one of the main implementing agents of the digital transformation (cybersecurity, blockchain, cloud computing, UX design, artificial intelligence, scientific computing...). To this end, hybridisation with other fields of knowledge but also with studies of other kinds such as university studies may be essential.
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Unknown, Author. WINMOP-R03 Performance of Offshore Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2003. http://dx.doi.org/10.55274/r0011744.
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The objective of the project was to validate existing pipeline integrity prediction models through field testing multiple pipelines, validate the performance of in-line instrumentation through smart pig runs, and finally, to assess the actual integrity of aging damaged and defective pipelines. The objectives were accomplished by the testing of aging out-of-service lines using "smart pigs", followed by hydrotesting of the lines to failure, recovery of the failed sections, and determination of the pipeline characteristics in the vicinity of the failed sections (failure analysis). One objective of the project was to validate the dented, gouged, and corroded pipeline burst strength prediction models currently in existence, such as ASME B31-G, R-Streng, and DNV 99 for pipelines. Another model was being developed as a joint international project sponsored by the U. S. Minerals Management Service, Petroleos Mexicanos (PEMEX), and Instituto Mexicano del Petroleo (IMP) titled RAM PIPE REQUAL and an associated JIP identified as PIMPIS (Pipeline Inspection, Maintenance, and Performance Information System), this would be tested and validated as well. The validation was provided by hydrotesting in-situ pipelines to failure. Sustained and rapidly applied hydro-pressures were used to investigate the effects of delayed and dynamic pressure related failures. After testing, the pipelines were scheduled for decommissioning; with the failed sections located, and brought to the laboratory for testing and analysis. Class A predictions were made before the pipelines were hydrotested to failure based on results from in-line instrumentation (instrumented) and from knowledge of the pipeline products and other characteristics (not instrumented). Based on the results from the testing, the analytical models were to be revised to provide improved agreement between the measured and predicted burst pressures. Since the pipelines were inspected with smart pigs before the hydro-tests, it was possible to compare the smart-pig data gathered during pig runs to the actual condition of the pipeline. This was accomplished by recovering sections of the pipeline that were identified by the pig as having pits or metal-loss areas. Reviewed pipeline decommissioning inventory and selected a pipeline candidate. The specific scope of work included: � Selected pipelines for testing. � Conducted field tests with an instrumented pig to determine pipeline denting, gouging and corrosion conditions. � Used existing analytical models to determine burst strength for both instrumented and non-instrumented pipelines. � Hydrotested the selected pipelines to failure (sustained and rapidly applied pressures). � Located and retrieve failed sections and other sections identified as problem spots by the "smart-pig." � Compared "smart pig" data to actual pipeline condition. � Analyzed the failed sections to determine their physical and material characteristics. � Revised the analytical models to provide improved agreements between predicted and measured burst pressures.
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Torrijos, Ivan Dario Pinerez, Tina Puntervold, Skule Strand, Panagiotis Aslanidis, Ingebret Fjelde, and Aleksandr Mamonov. Core restoration: A guide for improved wettability assessments. University of Stavanger, November 2021. http://dx.doi.org/10.31265/usps.198.
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The initial wetting of a reservoir sets a limit for the EOR potential during water-based recovery operations and “Smart Water” injection. For this reason, an improved understanding of the factors influencing the wetting can help to control and better forecast oil production during water-based floods. To preserve and reproduce the original reservoir wettability is a challenging task and wrong cleaning and core restoration procedures can lead to incorrect wettability estimations and thus induce serious errors when evaluating the initial wettability of a reservoir system or its EOR potential by water-based methods. Thereby, there is a need to improve the chemical knowledge on interactions among the rock, brine and fluids present in reservoir systems. This will help to understanding the influence of the parameters affecting wettability during cleaning and core restoration processes. Understanding which are the main parameters influencing oil recovery processes is of great relevance. The objective of this document is to provide suggestions for added-value experiments, complementing and challenging the standard RCA and SCAL procedures, prior to performing experimental research in which wettability and wettability alteration processes are important. Lessons learned will be highlighted and new ideas to optimize core restoration protocols to preserve and closely reproduce wettability are put forward. These recommended practices target core restoration procedures after the core material has been received in the laboratory. The target audience for this document is engineers and scientists with an interest in core preparation for wettability studies.
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Uche, Chidi, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Retrospective Study of Inspectors Competency in the Act of Writing GMP Inspection Report. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317445.
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The research was a retrospective study of twenty-five Good Manufacturing Practice (GMP) inspection reports (from March 2017 through to December 2018) of a national medicine regulatory agency, drug Inspectorate, in West Africa, designed to assess the inspectors’ expertise in the act of inspection report writing. The investigation examined a paper-based tool of thirteen pre-registration Inspection reports and twelve GMP reassessment reports written prior and following an intervention program by external GMP trainers to enhance inspectors’ skill in pharmaceutical cGMP inspection. The study made use of quantitative analysis to investigate each team’s expertise in the act of writing GMP inspection report. Likewise, each report’s compliance with the requirements of three regulatory standards on GMP inspection report writing was ascertained. Impact of intervention program on lead inspectors’ competence was assessed. Lastly, gap in each team writing effectiveness, and lead inspectors’ abilities to deliver an effective report were determined. The results showed one of the inspection team (4.0%) wrote an excellent report. Two (8.0%) of the twenty-five inspection teams penned good inspection reports. Eleven (44.0%) teams drafted needs improvement reports and the remaining eleven teams (44.0%) prepared unacceptable reports. The excellent report and the two good reports had report format that meet expectation. One (50.0%) of the good reports showed the authors possess excellent knowledge of cGMP technical areas. The remain good report (50.0%) revealed the writers’ knowledge.as good. The excellent report showed the authors displayed partial mastery in the use of objective evidence while the two good reports disclosed theirs as having partial and evolving abilities. One of the teams (50.0%) that wrote good reports displayed good use of third person narrative past tense in report writing whereas the other team used the same tense and voice excellently. Generally, a sort of marginal level of performance was prominent among the inspection teams. A gap, if not tackled, will slow down regulatory process through increase report review, litigations that query report factual accuracy (AIHO, 2017) and delay in issuance of marketing authorization. In conclusion, trainings on quality attributes, such as technical content (Quality Management System (QMS) and Site), the use of objective evidence, assignment of risk levels to GMP violations and citing of applicable laws, regulation and guidelines that substantiate GMP observations, were recommended, to enhance knowledge sharing and regulators’ performance in the act of writing inspection report.