Academic literature on the topic '091099 Manufacturing Engineering not elsewhere classified'

In recent years, many design approaches have been developed for automated manufacturing systems in the fields of reconfigurable manufacturing systems (RMSs), holonic manufacturing systems (HMSs), and multi-agent systems (MASs). One of the principle reasons for these developments has been to enhance the reconfigurability of a manufacturing system, allowing it to adapt readily to changes over time. However, to date, reconfigurability assessment has been limited. Hence, the efficacy of these design approaches remains inconclusive. This paper is the first of two in this issue to address reconfigurability measurement. Specifically, it seeks to address ‘reconfiguration potential’ by analogy. Mechanical degrees of freedom have been used in the field of mechanics as a means of determining the independent directions of motion of a mechanical system. By analogy, manufacturing degrees of freedom can be used to determine independent ways of production. Furthermore, manufacturing degrees of freedom can be classified into their production and product varieties. This paper specifically focuses on the former to measure the product-independent aspects of manufacturing system ‘reconfiguration potential’. This approach will be added to complementary work on the measurement of ‘reconfiguration ease’ so as to form an integrated reconfigurability measurement process described elsewhere [1—5].

This thesis starts with a literature review, outlining the major issues identified in the literature concerning virtual manufacturing enterprise (VME) transformation. Then it details the research methodology used – a systematic approach for empirical research. next, based on the conceptual framework proposed, this thesis builds three modules to form a reference model, with the purpose of clarifying the important issues relevant to transforming a traditional manufacturing company into a VME. The first module proposes a mechanism of VME transformation – operating along the VME metabolism. The second module builds a management function within a VME to ensure a proper operation of the mechanism. This function helps identify six areas as closely related to VME transformation: lean manufacturing; competency protection; internal operation performance measurement; alliance performance measurement; knowledge management; alliance decision making. The third module continues and proposes an alliance performance measurement system which includes 14 categories of performance indicators. An analysis template for alliance decision making is also proposed and integrated into the first module. To validate these three modules, 7 manufacturing organisations (5 in China and 2 in the UK) were investigated, and these field case studies are analysed in this thesis. The evidence found in these organisations, together with the evidence collected from the literature, including both researcher views and literature case studies, provide support for triangulation evidence. In addition, this thesis identifies the strength and weakness patterns of the manufacturing companies within the theoretical niche of this research, and clarifies the relationships among some major research areas from the perspective of virtual manufacturing. Finally, the research findings are summarised, as well as their theoretical and practical implications. Research limitations and recommendations for future work conclude this thesis.

Manufacturing today is increasingly competitive and every organization around the world is looking to decrease costs. Maintenance costs generated an average of 28 percent of total manufacturing cost at the Fiat Chrysler Indiana Transmission Plant One in 2018, states Rex White, Head Maintenance Planner at Fiat Chrysler (2018). Maintenance is a supportive expense that does not generate a profit, which makes maintenance an attractive expense to decrease. The cost for components and skilled labor are expensive; however, the downtime is exponentially a larger threat to production cost. One most feared scenarios within a manufacturing facility is that one machine takes down several as it backs up the entire production process.

The three major types of maintenance are reactive, preventive, and predictive. The research project focused on applying the principles of predictive maintenance to the Fiat Chrysler facilities in Indiana. The report explains the techniques and principles of applying the technology currently available to reduce downtime and maintenance cost. The predictive maintenance procedures and saving are compared with reactive and preventive methods to determine a value of return. The report will examine the benefits of using the Internet of Things technology to create autonomous self-diagnosing smart machines. The predictive maintenance plan in this research illustration will introduce health check equipment used to implement longer lasting machine components. In conclusion, the project developed out an entire predictive maintenance plan to reduce downtime and maintenance costs.

Manufacturing facilities need control for sustainability and longevity. If no control is provided for the manufacturing facility, then chaos can be unleashed causing much alarm. Therefore, it is essential to understand how control can be utilized to support the manufacturing facility and the corresponding manufacturing processes. This thesis will walk through a tool to help provide control and that tool is a Manufacturing Execution System (MES). Thisthesis will start with research to defineMESand its implications, then will work into the development of MES from the ground up. The design process willbe systematic and utilize the Collective System Design (CSD) approach with the aiding tool of the axiomatic decomposition map. Then examples will be given for the implementation and execution of the decomposition map as it relates to inventory and traceability. Finalwork will show the 7 FRs ofmanufacturing and how they are applicable to MES with given examples. Throughout the entire design and implementation, the initial hypothesis will be evaluated to determine if MES can provide the control requiredfor a robust manufacturing facility.

Spherical agglomeration (SA) is a novel process intensification strategy for particulate manufacturing. In the context of pharmaceutical manufacturing, it has the potential to reduce the number of unit operations in downstream processing from seven to three, which significantly reduces the manufacturing cost. However, SA process development for a new API in the drug pipeline is still a challenging exercise, which has impeded its practical implementation. The major bottleneck lies in the lack of fundamental understanding of the mechanistic principles underlying agglomeration of primary crystals, which can enable rational process design. In addition, most SA processes reported in literature focus on only the API, which does not eliminate the blending and wet granulation unit operations. The major purposes of this thesis are to (i) develop a first principle mathematical framework which can identify the fundamental agglomeration mechanism (ii) develop a model based online optimization framework, which can control the process, even in the presence of model parametric uncertainties (iii) develop a rational framework for co-agglomerating APIs and excipients, guided by process analytical technology tools. It is believed that the novel technology developed in this thesis will lay the groundwork for fast and robust process development of co-agglomerating APIs and excipients in the future, thereby enabling one-step direct compression. The large-scale development and deployment of this technology will significantly reduce the time to market and the manufacturing costs for new APIs, thereby ensuring higher accessibility of life-saving drugs.

Business incubation is a dynamic process of business enterprise development. Incubators nurture young firms, helping them to survive and grow during the start-up period. Among various types of incubators the university-based incubators are particularly studied. Although most university incubators are quite successful in terms of the success rate and the growth rate of tenant companies, their financial contributions to the sponsoring universities, however, are still not satisfied. It is found that behind the successful history records there are still some barriers impeding the development of an efficient incubator. In this research, a new model, which integrates merits of public and private incubators into the university incubator, is proposed for the betterment of its management scheme. The goal is to develop a successful incubator, which can earn profits not only for its own financial sustainability but also be able to generate income for the university. The outcomes of this research are summarized as follows: 1. From questionnaire survey around more than 100 university incubators around the world, this research received constructive opinions from incubator experts to support the proposed concept. This inspires the author to consider the necessity of a new incubation model for long-term sustainability. 2. The method of this survey study combines the Delphi Method and Scenario Analysis, called modified Delphi method, for worldwide survey and the Microsoft Excel method for data statistic for both of the Taiwan and worldwide surveys. By breaking down long questionnaire into two successive surveys, the replied rate did significantly increase. 3. An integrative framework for the new incubation model has been proposed for the sustainable operation of university incubator. National Taiwan University has validated this model in a similar way. 4. The process of privatization of university incubator is proposed to meet the university administrative procedure. Both of the government initialized top-down and incubator initialized bottom-up processes are considered. A Business Plan to suit for the proposed incubation company is also designed in this work. The sustainability in terms of financial status has been predicted based on some reasonable assumptions. 5. In order to verify the proposed model, three case studies through on-site visits have been carried out to compare their incubation systems and financial status up-to-date. This can provide a guideline to adjust the proposed model of this work. Finally, a comprehensive conclusion and discussions are given to summarize the contribution and future work of this research.

Robust process monitoring in real-time is a challenge for Continuous Pharmaceutical Manufacturing. Sensors and models have been developed to help to make process monitoring more robust, but they still need to be integrated in real-time to produce reliable estimates of the true state of the process. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this work, we present such a systematic framework, which for a given sensor network and measurement uncertainties will predict the most likely state of the process. As a result, real-time process decisions, whether for process control, exceptional events management or process optimization can be based on the most reliable estimate of the process state.

Data reconciliation (DR) and gross error detection (GED) have been developed to accomplish robust process monitoring. DR and GED mitigate the effects of random measurement errors and non-random sensor malfunctions. This methodology has been used for decades in other industries (i.e., Oil and Gas), but it has yet to be applied to the Pharmaceutical Industry. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. However, it requires the sensor network to be redundant (i.e., the number of measurements has to be greater than the degrees of freedom).

In this dissertation, the SSDR framework is defined and implemented it in two different continuous tableting lines: direct compression and dry granulation. The results for two pilot plant scales via continuous direct compression tableting line are reported in this work. The two pilot plants had different equipment and sensor configurations. The results for the dry granulation continuous tableting line studies were also reported on a pilot-plant scale in an end-to-end operation. New measurements for the dry granulation continuous tableting line are also proposed in this work.

A comparison is made for the model-based DR approach (SSDR-M) and the purely data-driven approach (SSDR-D) based on the use of principal component constructions. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. The reconciled measurement values generate using SSDR-M satisfy the model equations and can be used together with the model to estimate unmeasured variables. However, in the presence of nonlinearities, the SSDR-M and SSDR-D will differ. SSDR successfully estimates the real state of the process in the presence of gross errors, as long as steady-state is maintained and the redundancy requirement is met. Gross errors are also detected whether using SSDR-M or SSDR-D.

Numerous optimization problems in production systems can be considered as decision-making processes that determine the best allocation of resources to tasks over time to optimize one or more objectives in concert with big data. Among the optimization problems, production scheduling and routing of robots for material handling are becoming more important due to their impacts on system performance. However, the development of efficient algorithms for scheduling or routing faces several challenges. While the scheduling and vehicle routing problems can be solved by mathematical models such as mixed-integer linear programming to find optimal solutions to smallsized problems, they are not applicable to larger problems due to the nature of NP-hard problems. Thus, further research on machine learning applications to those problems is a significant step towards increasing the possibilities and potentialities of field application. In order to create truly intelligent systems, new frameworks for scheduling and routing are proposed to utilize machine learning (ML) techniques. First, the dynamic single-machine scheduling problem for minimization of total weighted tardiness is addressed. In order to solve the problem more efficiently, a decisiontree-based approach called Generation of Rules Automatically with Feature construction and Treebased learning (GRAFT) is designed to extract dispatching rules from existing or good schedules. In addition to the single-machine scheduling problem, the flexible job-shop scheduling problem with release times for minimizing the total weighted tardiness is analyzed. As a ML-based solution approach, a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) is developed to solve the problem by generating dispatching rules automatically. Finally, an optimization problem for routing of autonomous robots for minimizing total tardiness of transportation requests is analyzed by decomposing it into three sub-problems. In order to solve the sub-problems, a comprehensive framework with consideration of conflicts between routes is proposed. Especially to the sub-problem for vehicle routing, a new local search algorithm called COntextual-Bandit-based Adaptive Local search with Tree-based regression (COBALT) that incorporates the contextual bandit into operator selection is developed. The findings from my research contribute to suggesting a guidance to practitioners for the applications of ML to scheduling and control problems, and ultimately to lead the implementation of smart factories.

Additive Manufacturing (AM) allows for the creation of elements with novel forms and functions. Utilizing AM in development of components of civil infrastructure allows for achieving more advanced, innovative, and unique performance characteristics. The research presented in this dissertation is focused on development of a better understanding of the fabrication challenges and opportunities in AM of cement-based materials. Specifically, challenges related to printability and opportunities offered by 3D-printing technology, including ability to fabricate intricate structures and generate unique and enhanced mechanical responses have been explored. Three aspects related to 3D-printing of cement-based materials were investigated. These aspects include: fresh stability of 3D-printed elements in relation to materials rheological properties, microstructural characteristics of the interfaces induced during the 3D-printing process, and the mechanical response of 3D-printed elements with bio-inspired design of the materials’ architecture. This research aims to contribute to development of new pathways to obtain stability in freshly 3D-printed elements by determining the rheological properties of material that control the ability to fabricate elements in a layer-by-layer manner, followed by the understanding of the microstructural features of the 3D-printed hardened cement paste elements including the interfaces and the pore network. This research also introduces a new approach to enhance the mechanical response of the 3D-printed elements by controlling the spatial arrangement of individual filaments (i.e., materials’ architecture) and by harnessing the weak interfaces that are induced by the 3D-printing process.

One way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.

Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.

Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.

However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.

A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.

To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.