Design and Management of Manufacturing Systems

Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques

Comparative Analysis Of Production Control Systems Through Simulation

This paper presents a comparative analysis of seven different production control systems in a complex factory setup through computer simulation. Batch size, arrival rate, inter-arrival time, and maintenance type are the input parameters to the model. Work-in-process (WIP) and throughput (TH) are the system performance measurement output parameters. The study shows that a pull-based system does not outperform a push-based system with respect to WIP under all conditions. Pull-based systems prefer a smaller batch size to better control WIP. Each of the seven production control systems performs best at a specific inter-arrival time, although it is different for each system. Preventive maintenance is preferred over repair maintenance in a pull system and in a just-in-time (JIT) system. The computer simulation confirms that no single production control system is best under all conditions. The performance of a production control system depends not only on the type of manufacturing strategy chosen, but also on the values of the input parameters. This research shows that it makes no sense to comment on the superiority of one strategy over another without regard to the value of input parameters and the type of factory setup

Reactive scheduling using a multi-agent model: the SCEP framework

Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, andit permits a natural co-habitation between infinite capacity scheduling processes, performedby the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop

Microalgae production and maintenance optimization via mixed-integer model predictive control

This paper studies the joint production and maintenance scheduling in microalgae manufacturing systems comprised of multiple machines, which are subject to coupled production demand agreements and operational maintenance constraints. Namely, there are some microalgae production demands to be met over a given horizon, and the maintenance of each microalgae manufacturing unit must be done before a given deadline. Moreover, the number of units whose maintenance can be done simultaneously over the same day is limited, and the units that undergo maintenance cannot contribute to microalgae production during their maintenance day. To solve the considered problem, we design a mixed-integer nonlinear model predictive controller, which is implemented in two optimization stages. The former regards a mixed-integer model predictive control problem, while the latter considers a nonlinear model predictive control problem. The proposed approach allows us to decouple the mixed-integer and nonlinear parts of the whole problem, and thus provides more flexibility on the optimization solvers that can be employed. In addition, the first stage also evaluates the attainability of the demand agreements, and provides a mechanism to minimally adjust such constraints so that their satisfaction can be guaranteed at the second stage. The overall model predictive control approach is based on experimental data collected at VAXA Technologies Ltd., and the effectiveness of the proposed method is validated through numerical simulations including multiple manufacturing units and uncertainties.Juan Martinez-Piazuelo gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of his predoctoral grant FPI-UPC. In addition, the authors would like to thank VAXA Technologies Ltd. as well as the project PID2020-115905RB-C21 (L-BEST) funded by MCIN/ AEI /10.13039/501100011033 for supporting this research.Peer ReviewedPostprint (published version

Fault Adaptive Workload Allocation for Complex Manufacturing Systems

This research proposes novel fault adaptive workload allocation (FAWA) strategies for the health management of complex manufacturing systems. The primary goal of these strategies is to minimize maintenance costs and maximize production by strategically controlling when and where failures occur through condition-based workload allocation. For complex systems that are capable of performing tasks a variety of different ways, such as an industrial robot arm that can move between locations using different joint angle configurations and path trajectories, each option, i.e. mission plan, will result in different degradation rates and life-expectancies. Consequently, this can make it difficult to predict when a machine will require maintenance, as it will depend not only on the type and quality of the machine, but the actual tasks and mission plans it is performing. Furthermore, effective maintenance planning becomes increasingly challenging when dealing with complex systems, such as manufacturing production lines, that have multiple machines all performing different tasks, as the different degradation rates of each task will likely cause sporadic failures, leading to excessive work stoppages and lost production. In response, this work proposes novel strategies for optimizing maintenance schedules through fault adaptive workload allocation (FAWA). This work will show how we can alternate between multiple mission plans and task assignments to control degradation across multiple components, guiding failures to occur at optimal times and locations. We will present two unique strategies for degradation control. The first strategy attempts to synchronize maintenance by utilizing multiple mission plans and task assignments, such that the healthiest components do the most work, whenever possible, in order to compensate for the more degraded components. This promotes balanced degradation and synchronized failures across all components, allowing the number of work stoppages to be minimized. The second strategy involves desynchronizing maintenance by alternating between mission plans and task assignments where the healthiest components do either the most work or the least work in order to maintain an optimal difference between component degradation rates, such that overlapping failures are minimized. In this work, FAWA is applied to several case studies involving two types of manufacturing systems: industrial robot arms and 3D printers

Anomaly Detection Approaches for Semiconductor Manufacturing

Abstract Smart production monitoring is a crucial activity in advanced manufacturing for quality, control and maintenance purposes. Advanced Monitoring Systems aim to detect anomalies and trends; anomalies are data patterns that have different data characteristics from normal instances, while trends are tendencies of production to move in a particular direction over time. In this work, we compare state-of-the-art ML approaches (ABOD, LOF, onlinePCA and osPCA) to detect outliers and events in high-dimensional monitoring problems. The compared anomaly detection strategies have been tested on a real industrial dataset related to a Semiconductor Manufacturing Etching process

System performance evaluation through preventive maintenance scenarios using simulation technique

Equipment is one of the major contributors to the performance and profitability of manufacturing systems and its importance is rather increasing in the growing advanced manufacturing technology application stages (Kutucuoglu et al., 2001). The equipment is subject to deteriorations and failures as the consequence of usage, aging, fatigue, environmental conditions or other extreme events. Such system deterioration may lead to higher production cost, lower product quality and increase probability of breakdown (Mobley, 2002). Unexpected breakdowns not only increase the operating cost of the productive machines but in fact, the cost of lost production is inevitable for operation interruptions. Scheduled downtime is to control when the system will be down in favour of production. According to Ali et al. (2008), downtime will be more predictable if preventive maintenance is employed. The area of asset maintenance is becoming increasingly important as greater asset availability is demanded. Effective maintenance management is essential and critical as a way to reduce the adverse effect of equipment failures and to maximize equipment availability (Muhammad et al., 2010). The ultimate goal of maintenance is to provide optimal reliability and availability of production equipment, and maintain its operability to meet the business needs of the company

Web service control of component-based agile manufacturing systems

Current global business competition has resulted in significant challenges for manufacturing and production sectors focused on shorter product lifecyc1es, more diverse and customized products as well as cost pressures from competitors and customers. To remain competitive, manufacturers, particularly in automotive industry, require the next generation of manufacturing paradigms supporting flexible and reconfigurable production systems that allow quick system changeovers for various types of products. In addition, closer integration of shop floor and business systems is required as indicated by the research efforts in investigating "Agile and Collaborative Manufacturing Systems" in supporting the production unit throughout the manufacturing lifecycles. The integration of a business enterprise with its shop-floor and lifecycle supply partners is currently only achieved through complex proprietary solutions due to differences in technology, particularly between automation and business systems. The situation is further complicated by the diverse types of automation control devices employed. Recently, the emerging technology of Service Oriented Architecture's (SOA's) and Web Services (WS) has been demonstrated and proved successful in linking business applications. The adoption of this Web Services approach at the automation level, that would enable a seamless integration of business enterprise and a shop-floor system, is an active research topic within the automotive domain. If successful, reconfigurable automation systems formed by a network of collaborative autonomous and open control platform in distributed, loosely coupled manufacturing environment can be realized through a unifying platform of WS interfaces for devices communication. The adoption of SOA- Web Services on embedded automation devices can be achieved employing Device Profile for Web Services (DPWS) protocols which encapsulate device control functionality as provided services (e.g. device I/O operation, device state notification, device discovery) and business application interfaces into physical control components of machining automation. This novel approach supports the possibility of integrating pervasive enterprise applications through unifying Web Services interfaces and neutral Simple Object Access Protocol (SOAP) message communication between control systems and business applications over standard Ethernet-Local Area Networks (LAN's). In addition, the re-configurability of the automation system is enhanced via the utilisation of Web Services throughout an automated control, build, installation, test, maintenance and reuse system lifecycle via device self-discovery provided by the DPWS protocol...cont'd

Battery Production Systems: State of the Art and Future Developments

This paper discusses the state of the art in battery production research, focusing on high-importance topics to address industrial needs and sustainability goals in this rapidly growing field. We first present current research around three themes: human-centred production, smart production management, and sustainable manufacturing value chains. For each theme, key subtopics are explored to potentially transform battery value chains and shift to more sustainable production models. Such systemic transformations are supported by technological advances to enable superior manufacturing performance through: skills and competence development, improved production ergonomics and human factors, automation and human-robot collaboration, smart production planning and control, smart maintenance, data-driven solutions for production quality and its impact on battery performance (operational efficiency and durability), circular battery systems supported by service-based business models, more integrated and digitalized value chains, and increased industrial resilience. Each subtopic is discussed to suggest directions for further research to realise the full potential of digitalization for sustainable battery production

A flexible architecture for manufacturing planning software maintenance

Computer software systems took on a new role in manufacturing planning with the introduction of Material Requirement Planning (MRP) system in 1965. The MRP system generates material requirement lists in response to given production requirements. In this way, inventory management, purchasing, and shipping activities are linked to manufacturing. In 1979, Manufacturing Resource Planning (MRP II) systems were introduced [VerDuin 1995]. MRP II typically includes planning applications, customer order entry, finished goods inventory, forecasting, sales analysis, production control, purchasing, inventory control, product data management, cost accounting, general ledger processing, payables, receivables, and payroll [Turbide 1995]. An emerging market is developing for software systems that expand the scope of\u27MRP II farther to encompass activities for the entire organization. Among these systems are Enterprise Resource Planning (ERP), Customer-Oriented Manufacturing Management System (COMMS), and Manufacturing Execution Systems (MES). These systems integrate marketing, manufacturing, sales, finance, and distribution to move beyond optimizing production alone, to optimizing the organization\u27s multiple objectives of low cost, rapid delivery, high quality, and customer satisfaction [VerDuin 1995]. MRP II is still the dominant solution for manufacturing in tens of thousands of companies. These companies range in size from less than a million dollars in sales right up to the top of Fortune 500 companies. However, this is a market penetration of only 11% which clearly shows the size and potential of the opportunity for MRP II development. Yet, despite the commonality of needs across the scope of manufacturing, there are distinct differences when comparing plant to plant, company to company, and industry to industry. Often MRP II has to be modified to adapt to a particular industry [Turbide 1993]. This modification often pushes the cost even higher and makes MRP II more out-of-reach for many companies. Therefore, it would be highly beneficial for the overall scope of manufacturing if a highly flexible low-cost MRP II system can be developed. This research presents a flexible architecture for development and maintenance of manufacturing planning software, especially MRP II. The architecture uses the concept of software reuse and is built on top of run-time object-oriented framework