Performance Of Manufacturing System Research Articles

Simultaneous consideration of time and cost impacts of machine failures on cellular manufacturing systems

Cellular manufacturing systems (CMS) are essential for achieving efficient production processes, and their success relies on effective cell formation and layout design. This research paper presents a novel nonlinear mathematical programming model that employs the rectilinear distance notion to define the layout within a continuous space. The proposed model incorporates the stochastic nature of machine failures, considering the unreliability with the inclusion of a stochastic time between failures. With a bi-objective approach, the model aims to minimize inter and intra-cell movements of parts, as well as the associated costs related to exceptional elements (EEs), cell reconfigurations, and machine failures. To optimize the proposed model, several multi-objective meta-heuristic algorithms, including the multi-objective particle swarm optimization (MOPSO), multi-objective taboo search (MOTS), and non-dominated sorting genetic algorithm (NSGA-II), are introduced. The effectiveness of these algorithms is validated through numerical instances and a real case study. The results indicate that the MOPSO algorithm exhibits superior performance in optimizing various multi-objective criteria. The presented mathematical model and algorithms provide valuable tools for manufacturers to optimize their CMS, resulting in reduced costs, enhanced production efficiency, and increased competitiveness. By considering the simultaneous effects of time and cost associated with machine failures, this research offers practical insights and solutions for improving the performance of cellular manufacturing systems.

Energy demand and manufacturing system performance – a data-based modelling approach towards deeper understanding and integrated improvement

From both economic as well as environmental perspective energy demand is of strong and even more increasing relevance for manufacturing nowadays. But typically energy demand and manufacturing system performance (e.g. output, time demands, quality) are considered separately while actually being inherently connected in technical systems. Against this background, the paper presents a data-based approach to analyze and model those important aspects and interdependencies in an integrated manner. The developed theoretical framework and defined methodology enable deeper understanding of energy related impacts of operative influencing factors and eventually to derive fields of action for improvement. The approach was exemplary applied in an industrial case study. Results underline the feasibility, applicability, and potential benefits of the approach.

Development of a Human Centric Cyber Physical Production System Framework for Enhanced Social Sustainability

Social sustainability focuses on building a sustainable workplace that prioritizes the occupational health, safety, and overall well-being of workers. It is considered one of the three fundamental pillars of sustainability. Human centric cyber physical production system (CPPS) emphasizes the central role of humans in the smart manufacturing process that has become an essential requirement to enhance social sustainability. This paper proposes a human centric CPPS framework for enhanced social sustainability. The social needs/requirements are identified and categorized into various types. Similarly, functional and design requirements are categorized into different elements and sub-elements of CPPS. The findings are used to create a QFD (Quality Function Deployment) matrix that integrates social requirements with the functional and design requirements of modular CPPS elements. The present work will be significant in enhancing the management capabilities and performance of traditional manufacturing systems, while also meeting diverse social needs and requirements.

Optimizing Automotive Manufacturing Systems through TOGAF Modelling

The objective of this research is to examine the viability of implementing the Open Group Architecture Framework to enhance the efficiency and performance of automotive manufacturing systems. The automotive industry remains confronted with challenges pertaining to the enhancement of manufacturing processes, the reduction of product development time, and the adjustment to swift technological progressions. The primary obstacles encountered in implementing process innovation, the complexity of the IT infrastructure, and the absence of system integration constitute the most significant challenges. The primary aim of this study is to present a resolution through the application of the TOGAF framework. By implementing this strategy, system synchronization will be enhanced, IT infrastructure will be simplified, and process innovation will be able to respond to market fluctuations more rapidly. The existing business processes are streamlined and consistent with the strategic progress of vehicle manufacturing firms. Nonetheless, business processes involving architectural applications continue to diverge from market demands and fail to align with evolving business requirements. In the context of automotive manufacturing, the TOGAF modeling methodology will be applied to analyze the data architecture, application architecture, strategic elements, and information technology infrastructure. Advise stakeholders in the automotive industry, facilitating the integration of TOGAF principles into endeavors to redesign systems. This will reduce the attainment of innovation, adaptability, and efficiency, all of which are critical for sustaining competitiveness in a dynamic marketplace. By applying TOGAF principles to the automotive manufacturing system, Enterprise Architecture can support ever more complex business requirements.

Expanding the boundaries of Zero Defect Manufacturing - A systematic literature review

With Industry 5.0 on the horizon, research and innovation is prioritising the transition to sustainable, human-centred and resilient manufacturing. In this context, Zero Defect Manufacturing (ZDM) strategies combine tools, resources and rules with the aim of avoiding defects and increasing the sustainable performance of complex manufacturing systems. Five distinct strategies for ZDM have been identified in the literature: (I) detection, (II) prediction, (III) prevention, (IV) repair, and (V) mitigation or compensation. The main motivation of this paper is to discuss an extension of these five existing dimensions by three more, which will enable the transformation of classical ZDM towards a more sustainable production. The main objective of this paper is to conduct a systematic literature review that covers the state of the art of ZDM and highlights the need to add the three (Zero3) dimensions: i) Zero Resource Loss (resource and emission improvement) ii) Zero Human Potential Loss (productivity and stability improvement) iii) Zero Data Gap (increase in efficiency of data use).

Optimal Management of Reconfigurable Manufacturing Systems

The market demands more and more varied and personalized products and services. The work aims to improve the conceptual control of reconfigurable manufacturing systems, available to companies that work on the basis of orders, generically called "make-to-order" MTO, companies that require the most efficient reconfigurable manufacturing systems. Also, increasing the economic performance of these reconfigurable manufacturing systems, already in operation, is a goal of this work.

A simulation optimization framework to solve Stochastic Flexible Job-Shop Scheduling Problems—Case: Semiconductor manufacturing

This paper addresses a Stochastic Flexible Job-Shop Scheduling Problem (SFJSSP) in the context of semiconductor manufacturing. Semiconductor industry is among the most capital-intensive businesses whose operational excellence is of vital importance. Within the front-end fab of the semiconductor industry, the photolithography workstation is the well-known bottleneck process. To elevate the performance of the whole semiconductor manufacturing system, developing a competent schedule for its bottleneck is essential. However, the re-entrant product flows, high uncertainties in operations times, and rapidly changing products and technologies within the photolithography, make it difficult to develop a schedule for the whole semiconductor fab. Considering Industry 4.0, hybrid methods such as Simulation Optimization (SO) have proven their applicability in addressing complex production scheduling problems. Thus, this paper develops a mathematical model for SFJSSP of the semiconductor manufacturing considering special constraints of the photolithography workstation (machine process capability, machine dedication, and maximum reticles (masks) sharing constraints). Next, we transform the developed model into an SO model integrated with a computer simulation model capable of modeling the photolithography workstation. The simulation model develops an initial schedule based on the Least Work Remaining (LWR) dispatching rule. Moreover, the simulation model calculates the objective function of the SFJSSP. A tailored Genetic Algorithm (GA) is then developed, which attempts to optimize the initially proposed schedule. To validate the superiority of the presented SO methodology in addressing SJSSPs, it is compared with previously proposed methods. Furthermore, to assess the impact of the three special constraints of the photolithography work area on system performance, two sets of experiments are proposed. In the first set of experiments, the performance of two SFJSS environments, one with the special constraints and one without, is compared. The second set of experiments involves observing the system’s performance while systematically varying the severity of the special constraints. The results indicate that improved performance levels can be accomplished by enhancing flexibility within both the operations of individual jobs and the machines within the manufacturing system.

An integrated control strategy for simultaneous robot assignment, tool change and preventive maintenance scheduling using Heterogeneous Graph Neural Network

There has been a leap in the field of smart manufacturing with the advancement of automation systems, robotic technology, big data analytics, and state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms. Three very important aspects of smart manufacturing systems are system productivity, product quality, and maintenance of machines and equipment. These three issues are strongly interrelated and collectively determine the performance of a smart manufacturing system. Although there has been significant studies in production control, quality control and maintenance scheduling to address each of these aspects individually, there has been a lack of sufficient studies taking all of them into consideration in one control scheme. In this paper, a mobile multi-skilled robot operated Flexible Manufacturing System (FMS) is considered and a model that integrates robots, individual workstation processes and product quality is developed using a Heterogeneous Graph Structure. Heterogeneous Graph Neural Network (HGNN) is used to aggregate local information from different nodes of the graph model to create node embeddings that represent global information. A control problem is then formulated in the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework to simultaneously consider robot assignment, product quality and maintenance scheduling. The problem is solved using Multi-Agent Reinforcement Learning (MARL). A case study is presented to demonstrate the effectiveness of the HGNN-MARL control strategy by comparing it to three baselines and the naive MARL policy without HGNN.

A waste reduction strategy through autonomation under a closed-loop supply chain management

Currently, apart from manufacturing processes, the remanufacturing of products is also considerably important. Appropriate remanufacturing requires the operation of long-run manufacturing systems. However, in long-run processes, the production system may convert to an ``out-of-control" state due to machine breakdowns. Then, defective products are frequently produced; this increases wastage and disrupts environmental sustainability. In this model, a smart autonomation policy is deliberated for error-free inspection in separating defective products during production. The autonomation policy facilitates waste reduction through remanufacturing. This paper also concentrates on customer awareness and service dependent demand, which directly improves the overall profitability of the system. A discrete investment to reduce setup cost, continuous investment to collect used goods, and cap-and-trade strategy to limit carbon emission are considered to obtain a more realistic model. Classical optimization method is applied for global optimality test of the profit function with respect to cycle length, customer awareness, service investment, discrete investment to reduce setup cost, number of shipments, and container capacities. Numerical testing, sensitivity to total profit in different cost parameters, and comparisons with previous research are explained. Some special scenarios including graphical representations are discussed to prove that a large investment is more beneficial than the cost of specific setup and collection.

Manipulator joint fault localization for intelligent flexible manufacturing based on reinforcement learning and robot dynamics

This article proposes a new method to address the challenge of remote monitoring in intelligent flexible manufacturing systems. Specifically, we propose a multi working condition fault localization algorithm for robotic arms, which eliminates the need for additional sensors and is based on the classic sliding window algorithm. We use reinforcement learning technology to learn detection parameter debugging experience under different working conditions, and combine the dynamics of the robot to achieve fault detection and fault source localization in a flexible environment. Through the robot's own programmable logic controller system, the remote monitoring system can sense the operating status of each link. To evaluate the effectiveness of our proposed method, we conducted experimental equipment simulations and real-world industrial operations. The results show that under multiple operating conditions, the accuracy of fault detection reaches 86%, and the accuracy of localization reaches 81.35%. The deviation of results under different robot operating conditions is significantly lower than other algorithms. This study explores the potential and implementation approaches of reinforcement learning in intelligent manufacturing systems, with a particular focus on applications in flexible scenarios. These findings reveal the prospects of reinforcement learning technology in improving the sustainable operation of intelligent manufacturing systems.

The new fuzzy bottleneck model to improve the axle manufacturing system performance

The new fuzzy bottleneck model to improve the axle manufacturing system performance

Identification of influence of digital twin technologies on production systems: a return on investment-based approach

The object of this study is the impact of different digital twin solutions on the performance of job-shop manufacturing systems, while economic aspects are also taken into consideration. This paper proposes an approach to analyze the impact of different identification systems on the efficiency and ROI of digital twin deployment in production systems. In order to achieve this aim, let’s analyze the investment and operation cost of different Internet of Things technologies. The next phase of the research work was the definition of performance parameters, which makes it possible to analyze the impact of different digital twin solutions on the productivity of the job-shop manufacturing system. It is possible to choose four financial indicators to analyze the economic impact of digital twin solution on job-shop manufacturing: return on investment, compound annual growth rate, internal rate of return and net present value. Our approach is based on a novel agent-based simulation model using AnyLogic simulation tool. From the results of this productivity analyses, the model computes the financial indicators, which describe the expected financial impact of the investment and operation cost. It is compared the impact of barcodes and radiofrequency identification technologies on the financial and technological impact of the job-shop manufacturing environment. The numerical analysis of a job-shop manufacturing system shows, that the radiofrequency identification-based digital twin solution has 9.2 % higher return on investment, 53 % higher net present value and 1.6 % higher compound annual growth rate. The model can be easily converted to analyze other types of manufacturing systems, which can lead to increased efficiency of digital twin solutions

Comprehensive Assessment of the Manufacturability of Products

Introduction. The assessment of the manufacturability of products – as a stage of production planning and a key aspect of the development of modern industrial machining systems — is an urgent task of modern mechanical engineering. In this regard, theoretical and practical research on the development of methodological approaches to determining the weight significance of quantitative indicators in assessing the manufacturability of parts is highly relevant. The objective of the presented work was to develop an evaluation method aimed at improving the quality of part processing and the effectiveness of the performance of multiproduct manufacturing systems based on the development of additional quantitative indicators for assessing production manufacturability.Materials and Methods. To assess the impact of quantitative production indicators associated with time spent during equipment downtime, a model was created. It was aimed at predicting event flows of delivery of batches of parts for manufacturing for a specific operation and flows of processed parts using the queuing theory apparatus. This approach makes it possible to take into account both the design-engineering characteristics of parts, the features of a particular production system, and the emerging manufacturing situation.Results. The degree of influence of the manufacturability indicators at the level of the process operation was determined by assessing the possible impact on the components when calculating piece-calculation time (Тшт.к.). The interrelations between the manufacturability indicators and expenses for all items of the production cost of part processing (СОП), as well as costs associated with organizational downtime of equipment (Спр.о.i) were established. The degree of influence of the indicators of manufacturability relative to other indicators was determined by using the apparatus of paired comparisons in decision-making in relation to all structural elements of production costs.Discussion and Conclusion. The approach to the implementation of this design procedure was described, which provided taking into account the composition and capabilities of processing equipment of a particular production and the actual production situation. The developed formalized models make it possible to comprehensively predict the impact of the manufacturability indicators of parts on the performance effectiveness of machining systems during their manufacture.

Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance

Machine sequence flexibility is defined as the combination of operation and routing flexibilities in this study. Its importance in the performance level of a flexible manufacturing cell (FMC) is investigated in this study. Studies related to the effects of various flexibility types, such as routing flexibility, are available in the literature. For example, studies related to routing flexibility try to measure the effects of routing flexibility on the performance levels in the operation of manufacturing systems under their own manufacturing environments. Similarly, this study also aims to present a performance measurement model based on Taguchi methods to evaluate the effects of machine sequence flexibility factors on the FMC performance and obtain an optimum and robust performance level. Two crucial responses, such as manufacturing lead time (MLT) and surface roughness (SR) are analysed to optimize the FMC performance. Robot speed, cutting tool type, and work-part material type are taken as the three other input factors to show the importance of machine sequence flexibility with respect to the other inputs. The study presented in this paper points out that machine sequence flexibility is the most effective input factor among the four input factors in the performance of the FMC.

A Distributed Supervisor Architecture for a General Wafer Production System.

The current trend in the wafer production industry is to expand the production chain with more production stations, more buffers, and robots. The goal of the present paper is to develop a distributed control architecture to face this challenge by controlling wafer industrial units in a general production chain, with a parametric number of production stations, one robot per two stations where each robot serves its two adjacent production stations, and one additional robot serving a parametric number of stations. The control architecture is analyzed for individual control units, one per robot, monitoring appropriate event signals from the control units of the adjacent robots. Each control unit is further analyzed to individual supervisors. In the present paper, a modular parametric discrete event model with respect to the number of production stations, the number of buffers, and the number of robotic manipulators is developed. A set of specifications for the total system is proposed in the form of rules. The specifications are translated and decomposed to a set of local regular languages for each robotic manipulator. The distributed supervisory control architecture is developed based on the local regular languages, where a set of local supervisors are designed for each robotic manipulator. The desired performance of the total manufacturing system, the realizability, and the nonblocking property of the proposed architecture is guaranteed. Finally, implementation issues are tackled, and the complexity of the distributed architecture is determined in a parametric formula. Overall, the contribution of the present paper is the development of a parametric model of the wafer manufacturing systems and the development of a parametric distributed supervisory control architecture. The present results provide a ready-to-hand solution for the continuously expanding wafer production industry.

AGV’s effect on the performance of flexible manufacturing system in stochastic environment

This simulation-based experimental study focuses on the performance of stochastic flexible manufacturing system with Automated Guided Vehicles AGVs. Four levels of AGV fleet size (i.e. 1 AGV, 2 AGVs, 3 AGVs and 4 AGVs) at four different AGV velocities (i.e. 2 m/s, 4 m/s, 6 m/s and 8 m/s) with four sequencing rules are used to perform the experiments. The performance of the system is measured by two performance measures like make-span time and resource utilization. It is observed that the performance improves as the numbers of AGVs increases in all combinations and this increase is more visible when the system runs with 2 AGVs instead of 1 AGV. The impact of AGV velocity is found to be more visible with the use of more number of AGVs. There was effect of sequencing rules for different combinations of number of AGVs and velocities.

Exploring the Effects of Blockchain Scalability Limitations on Performance and User Behavior in Blockchain-Based Shared Manufacturing Systems: An Experimental Approach

This study investigates the effects of blockchain technology scalability limitations on the performance of Blockchain-Based Shared Manufacturing (BBSM), an innovative smart-manufacturing paradigm aimed at enhancing the utilization of global manufacturing resources via peer-to-peer (P2P) collaboration of self-organized manufacturing assets. Despite the prevalence of research highlighting blockchain technology’s scalability limitations as the main barrier for adoption, few studies have explored their effects on the operation of blockchain-based systems. The primary goal of the presented research work is to explore the implications of blockchain technology scalability limitations on the BBSM system’s performance and user behavior. To obtain realistic behavior, an experiment is conducted using an online game played by human participants. Analysis of the players’ strategy is used for implementation of a multi-agent simulation model, which is then employed to assess the influence of varying blockchain network configurations on the BBSM concept’s performance. Preliminary experimental findings reveal that a congested blockchain network leads to increased transaction costs and reduced service prices, consequently devaluing the manufacturing role in the BBSM system and causing underutilization of existing maximum production capacities. Moreover, allocating funds to financial activities rather than manufacturing activities yields superior outcomes for system users. Simulation results indicate that the BBSM system’s response to alterations in blockchain network throughput is contingent upon the production function. The findings of this study reveal that the scalability limitations of blockchain technology impair the performance of the BBSM system and affect user behavior in the system, underscoring the necessity for future research to concentrate on incorporating scalable solutions within blockchain-based manufacturing systems.

A transformer-based approach for novel fault detection and fault classification/diagnosis in manufacturing: A rotary system application

Owing to the rapid development of Industry 4.0, new sensing and communication technologies have made vast amounts of untapped process data available. In order to transform such data assets into strong insights and knowledge that support manufacturing decisions, condition-based maintenance (CBM) and fault detection and diagnosis (FDD) have become effective ways to enhance equipment reliability and reduce costs. A successful data-driven FDD method must not only be capable of identifying the types of known faults, but also in detecting unseen or uncharacterized events during manufacturing system operation. To this end, this paper presents a Transformer-based classifier that can efficiently identify different known types and severity levels of fault conditions, in addition to novel fault detection. In this method, time-frequency spectrograms transformed from raw vibration signals are input to the classifier for known fault classification. Utilizing the advanced feature extracting performance of the classifier, a simple yet effective technique based on Mahalanobis distance is adopted to detect whether the fault comes from a previously unseen fault condition. When a novel condition is detected, the model is subsequently retrained using the novel data in an incremental learning manner. The proposed method is verified by an experimental case study with data collected from a testbed that has many features representative of common manufacturing equipment. The results demonstrated that the proposed method has superior performance in both fault diagnosis and novelty identification when compared with the baseline models and a cutting-edge model.

Digital twin-enabled automated anomaly detection and bottleneck identification in complex manufacturing systems using a multi-agent approach

Digital twin (DT) models are increasingly being used to improve the performance of complex manufacturing systems. In this context, DTs automatically enabling anomaly detection, such as increase in orders, and bottleneck identification, such as shortage of products, can significantly enhance decision-making to mitigate the consequences of the identified bottlenecks. The existing literature has mainly focused on implementing top-down approaches for analysing the bottlenecks without considering the emergent behaviour of micro-level agents, including inventory levels and human resources, and their impact on the macro-level system’s performance. In order to handle the aforementioned challenges, this paper extends the current literature by proposing a novel DT integrated in a multi-agent cyber physical system (CPS) for detecting anomalies in sensor data, while identifying and removing bottlenecks that emerge during the operation of complex manufacturing systems. An extended 5 C CPS architecture, using multi-agent approach, is implemented to allow DT integration. The agent-based simulation technique enables capturing the probabilistic variability, and aggregate parallelism and dynamism of parallel dynamic interactions within the DT-CPS. A new single agent at the exo-level of the multi-level agent-based modelling structure, called the ‘monitoring agent’, is introduced in this research. The agent detects anomalies and identify bottlenecks through communicating with other agents in different levels automatically. The DT-CPS provides feedback automatically to the physical space to remove and mitigate the identified bottlenecks. The proposed DT based multi-agent CPS has been tested successfully on a real case study in a cryogenic warehouse shop-floor from the cell and gene therapy industry. The performance of the studied cryogenic warehouse is continuously measured using real-time sensor data. The analyses of the results show that the proposed DT-CPS improves the utilisation rates of human resources, on average, by 30% supporting decision making and control in complex manufacturing systems.

Digital innovation: An essence for Industry 4.0

AbstractThe goal of this paper is to explain why digital innovation is so important in business organizations in order to survive in Industry 4.0. The study helps to understand the new era of Industry 4.0 and the importance of introducing digital innovation into organizations. A systematic review of the literature and studies on Industry 4.0 and digital innovation were synthesized to find answers to the research questions. To improve their manufacturing industry, organizations have implemented digital technologies such as augmented reality, robotic sensing, artificial intelligence, cloud computing, cyber physical systems, and remote sensing technologies. These technologies focused on automating logistics and supply chain systems, improving manufacturing system performance, and simplifying automated production systems. Because digital innovations save time and energy, employees can devote more time and energy to creative and innovative activities. Organizations should plan to implement digital technology in order to keep the environment healthy and sustainable while meeting the demands of customers, consumers, and the Industry 4.0 dimension.