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Disabled people can be successfully employed in most production processes, provided that one knows how to exploit their abilities and take into account their limitations in order to give them an appropriate job. However, because the level and type of production must be constantly adapted to the needs of the market, the involvement of disabled people in the production process may also change. Additionally, people with disabilities have limitations as well as additional rights that must be considered. As a result, the organisation and planning of their work, side by side with other employees, becomes more complex. Computer simulations can be a support for organising and planning the involvement of employees with disabilities in production processes. The aim of the article is to show how simulations can facilitate the organisation of work of employees with disabilities, with the changing demand for manufactured products. The paper identifies the factors that should be considered, and then presents how the employment of disabled people can affect the operation of the production line and the commercial image of the company. The study uses a combination of System Dynamics and Discrete Event Simulations. The relevant data for the simulation were derived from a production company. [ABSTRACT FROM AUTHOR]

Manufacturers experience random environmental fluctuations that influence their supply and demand processes directly. To cope with these environmental fluctuations, they typically utilise operational hedging strategies in terms of pricing, manufacturing, and procurement decisions. We focus on this challenging problem by proposing an analytical model. Specifically, we study an integrated problem of procurement, manufacturing, and pricing strategies for a continuous-review make-to-stock system operating in a randomly fluctuating environment with exponentially distributed processing times. The environmental changes are driven by a continuous-time discrete state-space Markov chain, and they directly affect the system's procurement price, raw material flow rate, and price-sensitive demand rate. We formulate the system as an infinite-horizon Markov decision process with a long-run average profit criterion and show that the optimal procurement and manufacturing strategies are of state-dependent threshold policies. Besides that, we provide several analytical results on the optimal pricing strategies. We introduce a linear programming formulation to numerically obtain the system's optimal decisions. We, particularly, investigate how production rate, holding cost, procurement price and demand variabilities, customers' price sensitivity, and interaction between supply and demand processes affect the system's performance measures through an extensive numerical study. Furthermore, our numerical results demonstrate the potential benefits of using dynamic pricing compared to that of static pricing. In particular, the profit enhancement being achieved with dynamic pricing can reach up to 15%, depending on the problem parameters. [ABSTRACT FROM AUTHOR]

In a multi-state manufacturing system, preventive maintenance is performed on other machines once the performance of a machine falls below a threshold state. Many investigations have been performed on the maintenance of manufacturing systems. However, studies on preventive maintenance for manufacturing systems that account for the different costs involved are limited. This paper focuses on the analysis of multi-state machines in a manufacturing system considering different costs. Subsequently, a cost-based preventive maintenance prioritisation method for multi-state machines is proposed. Based on the buffer capacity, three maintenance policies on the machines are discussed. Different machine failures lead to different maintenance priorities. The real-time buffer capacity greatly influences the selection of machines for preventive maintenance. The set of the optimal machines for preventive maintenance is determined based on the constraints of machine running time and different preventive maintenance costs. Finally, a numerical example is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Introduction: The excessive uncertainty of in modern manufacturing systems is caused by machine failures, changes in material information, and other factors. In addition, the organizational production mode conflicts brought about by economic and technological development further exacerbate the perception of workshop interference in manufacturing systems. Method: In order to further improve the adaptability of manufacturing systems, a control technique based on recursive control structure is proposed, which introduces an immune working mechanism to design the framework network of multi-agent manufacturing systems. Meanwhile, a negative selection algorithm is used to construct an antibody training system that considers perturbation problems. Result: The results indicate that immune sensing nodes can effectively monitor manufacturing systems, reducing false alarm rates by over 4%. In the scheduling experiment, the completion time and equipment load improvement rate demonstrated by the research model were 3.29% and 12.38%, respectively. The production balance optimization rate exceeded 90%, far exceeding the results of traditional scheduling schemes, greatly improving the adaptive control capability of manufacturing system production. Discussion: The regulatory approach proposed in this study can provide reference and assistance for improving the level of industrial production intelligence and establishing a sustainable economic system. However, the research results have not been applied to actual production processes, and the autonomy and coordination of intelligent manufacturing units in actual production processes still need to be further improved. In the future, research models and algorithms will be further explored in this area. [ABSTRACT FROM AUTHOR]

In contemporary business environments, manufacturing companies must continuously enhance their performance to ensure competitiveness. Material feeding systems are of pivotal importance in the optimization of productivity, with attendant improvements in quality, reduction of costs, and minimization of delivery times. This study investigates the selection of material feeding methods, including Kanban, line-storage, call-out, and kitting systems, within a manufacturing company. The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. Utilizing a dataset comprising 2221 materials and an 8-fold cross-validation technique, the ANN model exhibits superior performance across all evaluation metrics. Shapley values analysis is employed to elucidate the influence of pivotal input parameters within the selection process for material feeding systems. This research provides a comprehensive framework for material feeding system selection, integrating advanced ML models with practical manufacturing insights. This study makes a significant contribution to the field by enhancing decision-making processes, optimizing resource utilization, and establishing the foundation for future studies on adaptive and scalable material feeding strategies in dynamic industrial environments. [ABSTRACT FROM AUTHOR]

Predictive maintenance in manufacturing industry applications is a challenging research field. Packaging machines are widely used in a large number of logistic companies' warehouses and must be working uninterruptedly. Traditionally, preventive maintenance strategies have been carried out to improve the performance of these machines. However, these kinds of policies do not take into account the information provided by the sensors implemented in the machines. This paper presents an expert system for the automatic estimation of work orders to implement predictive maintenance policies for packaging machines. The central innovation lies in a two-stage process: a classifier generates a binary decision on whether a machine requires maintenance, and an unsupervised anomaly detection module subsequently audits the classifier's probabilistic output to refine and interpret its predictions. By leveraging the classifier to condense sensor data and applying anomaly detection to its output, the system optimizes the decision reliability. Three anomaly detection methods were evaluated: One-Class Support Vector Machine (OCSVM), Minimum Covariance Determinant (MCD), and a majority (hard) voting ensemble of the two. All anomaly detection methods improved the baseline classifier's performance, with the majority voting ensemble achieving the highest F1 score. [ABSTRACT FROM AUTHOR]

This research addresses complexity in manufacturing systems from an entropic perspective for production improvement. The main objective is to develop and validate a methodology that develops an entropic metric of complexity in an integral way in production environments, through simulation and programming techniques. The methodological proposal is composed of six stages: (i) Case study, (ii) Hypothesis formulation, (iii) Discrete event simulation, (iv) Measurement of entropic complexity by applying Shannon's information theory, (v) Entropy analysis, and (vi) Statistical analysis by ANOVA. The results confirm that factors such as production sequence and product volume significantly influence the structural complexity of the workstations, with station A being less complex (0.4154 to 0.9913 bits) compared to stations B and C, which reached up to 2.2084 bits. This analysis has shown that optimizing production scheduling can reduce bottlenecks and improve system efficiency. Furthermore, the developed methodology, validated in a case study of the metalworking sector, provides a quantitative framework that combines discrete event simulation and robust statistical analysis, offering an effective tool to anticipate and manage complexity in production. In synthesis, this research presents an innovative methodology to measure static and dynamic complexity in manufacturing systems, with practical application to improve efficiency and competitiveness in the industrial sector. [ABSTRACT FROM AUTHOR]

Advanced manufacturing is challenging engineering perceptions of how to innovate and compete. The need for manufacturers to rapidly respond to changing requirements and demands; obtain, store, and interpret large volumes of data and information; and positively impact society and our environment requires engineers to investigate and develop new ways of making products for flexible and competitive production. In addition to the associated operational, technological, and strategic advantages for industry, advanced manufacturing creates educational, workforce, and market opportunities. Thus, this literature review is aimed at investigating the current state and emerging trends in advanced manufacturing. Specifically, this study addresses advances in manufacturing process technologies, focusing on shaping processes (mass reducing, mass conserving, and joining) as well as non-shaping processes (heat treatment and surface finishing), and metal-based additive manufacturing. This literature review finds myriad efforts have been undertaken by researchers in industry, academia, and government labs from around the world, which have supported the development and implementation of new process technologies to improve manufacturing systems extending from unit process and shop floor operations to facility and supply chain management activities. However, as evidenced by recent and emerging global challenges in energy, critical materials, and health care, the manufacturing industry must continue the innovative development of advanced materials, manufacturing processes, and systems that ensure cost-efficient, rapidly flexible, high-quality, and responsible production of goods and services. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of Industry 4.0 and the transition towards Industry 5.0, manufacturing systems face the challenge of adapting to dynamic, hyper-customized demands. Current Simulation Optimization (SO) systems struggle with the flexibility needed for quick reconfiguration, often requiring time-consuming, resource-intensive efforts to develop custom models. To address this limitation, this study introduces an innovative SO design strategy that integrates three flexible simulation modeling techniques—template-based, structural modeling, and parameterization. The goal of this integrated design strategy is to enable the rapid adaptation of SO systems to diverse production environments without extensive re-engineering. The proposed SO versatility is validated across three manufacturing scenarios (flow shop, job shop, and open shop scheduling) using modified benchmark instances from Taillard's dataset. The results demonstrate notable effectiveness in optimizing production schedules across these diverse scenarios, enhancing decision-making processes, and reducing SO development efforts. Unlike conventional SO system design, the proposed design framework ensures real-time adaptability, making it highly relevant to the dynamic requirements of Industry 5.0. This strategic integration of flexible modeling techniques supports efficient decision support, minimizes SO development time, and reinforces manufacturing resilience, therefore sustaining competitiveness in modern industrial ecosystems. [ABSTRACT FROM AUTHOR]

The importance of sustainability in industry is dramatically rising in recent years. Controlling machine states to achieve the best trade-off between production rate and energy demand is an effective method for improving the energy efficiency of production systems. This technique is referred to as energy-efficient control (EEC) and it triggers machines in a standby state with low power requests. Reinforcement Learning (RL) algorithms can be used to successfully control production systems without the requirement of prior knowledge about system parameters. Due to the difficulty in acquiring comprehensive information about system dynamics in real-world scenarios, this is considered an important factor. The goal of this work is to create a novel RL-based model to apply EEC to multi-stage production lines with parallel machine workstations without relying on full knowledge of the system dynamics. Numerical results confirm model benefits when applied to a real line from the automotive sector. Further experiments confirm the effectiveness and generality of the approach. [ABSTRACT FROM AUTHOR]

Profile monitoring is a widely used tool in quality control. The rapid development of sensor technology has created unprecedented opportunities for multi-channel profile data collection, which motivates the modeling and transfer learning for multi-profile data. The Multi-output Gaussian Process (MGP) is often used for multi-profile data, due to its flexible modeling capability and elegant mathematical properties. However, two practical concerns limit the broader application of MGP for transfer learning and monitoring of multi-profile data: high computational complexity and data incompleteness. In this article, we propose a Variational Inference (VI)-based MGP framework to facilitate transfer learning and profile monitoring using incomplete profile data. The proposed framework features a specially designed convolutional structure for constructing an explicit covariance relationship between the inducing variables in VI and the MGP in multi-profile data. This structure inspires a comprehensive solution to both computational complexity and data incompleteness in modeling multi-profile data, which facilities the transfer learning for profile monitoring. Various numerical studies and one real case study are conducted to demonstrate and compare the transfer learning and monitoring performance of the proposed method. The results show the method can achieve superior monitoring performance while maintain a very low level of computational complexity. [ABSTRACT FROM AUTHOR]

Innovations and advancements in technology create new opportunities to run and maintain manufacturing plants, which we refer to as digitalised manufacturing. This development is recognised as a socio-technical system (STS) change, where a change in the production system's goals, technology, processes, people, or environment may lead to ripple effects between those sub-systems. Despite this, technology development and technology use cases account for most of the research within digitalised manufacturing, while little attention has been devoted to leadership practices considering digitalised manufacturing from a socio-technical perspective. This paper focuses on the maintenance organisation, whose mission in a company is to keep production systems functional. We aim to describe leadership in industrial maintenance from an STS perspective. This is a unique interview study where twenty maintenance managers from Swedish manufacturing industry offer their perspective on the changing leadership within maintenance, providing a unique insight into the challenges facing leaders of maintenance in digitalised manufacturing. We frame the empirical findings using an STS framework and propose an overall consideration model for leadership that supports the development of a functional maintenance organisation in the face of pervasive digitalisation. [ABSTRACT FROM AUTHOR]

Abstract This article investigates the state geometric adjustability for interval max‐plus linear systems, which means that the state vector sequence is transformed into a geometric vector sequence by using the state feedback control. It is pointed out that the geometric state vector sequence and its common ratio are closely related to the eigenvectors and eigenvalues of the special interval state matrix, respectively. Such an interval state matrix is determined by the eigen‐robust interval matrix, which has a universal eigenvector relative to a universal eigenvalue. The state geometric adjustability is characterized by the solvability of interval max‐plus linear equations, and a necessary and sufficient condition for the adjustability is given. A polynomial algorithm is provided to find the state feedback matrix. Several numerical examples and simulations are presented to demonstrate the results. At the same time, the proposed method is applied for the regulation of battery energy storage systems to optimize the start time of executing tasks for all processing units in each activity.

Storage systems and warehouse processing in factories are integral to manufacturing operations and logistics procedures. Proper factory layout, storage locations, and procedures save time and reduce cost and wastes. This study examines a real case scenario of a metal industry and sheet metal factory manufacturing commercial easy-to-install metal racks. We collected data, investigated the current situation of the factory, and specified the difficulties and problems regarding the procedures, storage system, and distribution of machines. Furthermore, several proposed solutions were presented and analyzed using simulation, followed by the selection of the best solution. Then, we applied 5S methodology to improve productivity and reduce waste. The findings reveal that reducing waste and time, enhancing productivity, and better space utilization can be achieved significantly. Moreover, this study indicates the feasibility of the proposed solutions, which can be adopted by similar factories and other industries.

Reconfigurability in manufacturing signifies a system's capacity to promptly adapt to evolving needs. This adaptability is critical for markets to maintain operations during unexpected disruptions, including weather anomalies, cyber-attacks, and physical obstructions. Concurrently, the concept of a circular economy is gaining popularity in manufacturing to mitigate waste and optimize resource utilization. Circular economy principles aim to reduce environmental impacts while maximizing economic benefits by emphasizing the reuse of goods and resource byproducts. The nexus between reconfigurability and the circular economy stems from their shared pursuit of sustainability and resilience. Interestingly, biological ecosystems also exhibit these traits, showcasing exceptional adaptability to disturbances alongside the ability to effectively utilize available resources during normal operations. This study explores various manufacturing system configurations to assess both their adaptability and connection to circular economy principles. Forty-four configurations are categorized based on layout (e.g., job shop, flow line, cellular) and analyzed using convertibility, cyclicity, and degree of system order metrics. A significant positive correlation (R² = 0.655) is found between high convertibility and ecologically similar levels of structural cycling, suggesting that effective resource utilization supports adaptability in manufacturing systems. Furthermore, this paper proposes the existence of a possible "window of vitality" for cyclicity, as it demonstrates a significant correlation (R² = 0.855) between the degree of system order and cyclicity. Identifying systems that strike a balance between redundancy, efficiency, convertibility, and cyclicity can aid manufacturing system designers and decision-makers in making choices that address increasing requirements for both sustainability and resilience. [ABSTRACT FROM AUTHOR]

This article investigates the state geometric adjustability for interval max‐plus linear systems, which means that the state vector sequence is transformed into a geometric vector sequence by using the state feedback control. It is pointed out that the geometric state vector sequence and its common ratio are closely related to the eigenvectors and eigenvalues of the special interval state matrix, respectively. Such an interval state matrix is determined by the eigen‐robust interval matrix, which has a universal eigenvector relative to a universal eigenvalue. The state geometric adjustability is characterized by the solvability of interval max‐plus linear equations, and a necessary and sufficient condition for the adjustability is given. A polynomial algorithm is provided to find the state feedback matrix. Several numerical examples and simulations are presented to demonstrate the results. At the same time, the proposed method is applied for the regulation of battery energy storage systems to optimize the start time of executing tasks for all processing units in each activity. [ABSTRACT FROM AUTHOR]

Advanced manufacturing is challenging engineering perceptions of how to innovate and compete. The need for manufacturers to rapidly respond to changing requirements and demands; obtain, store, and interpret large volumes of data and information; and positively impact society and our environment requires engineers to investigate and develop new ways of making products for flexible and competitive production. In addition to the associated operational, technological, and strategic advantages for industry, advanced manufacturing creates educational, workforce, and market opportunities. Thus, this literature review aims to investigate the current state and emerging trends in advanced manufacturing. Specifically, this study addresses advances in manufacturing from manufacturing systems perspective, concentrating on emerging trends in process sensing and monitoring, equipment control and automation, machine tools, sustainable manufacturing, and green supply chain management. This review finds myriad efforts have been undertaken by researchers in industry, academia, and government labs from around the world, which have supported the development and implementation of new process technologies to improve manufacturing systems extending from unit process and shop floor operations to facility and supply chain management activities. However, emerging global challenges remain in various domains including energy (e.g., resource scarcities and global warming), critical materials vulnerable to supply disruptions due to crisis and rapid changes in demand, and services (e.g., healthcare supply chains during COVID-19 pandemic). Thus, manufacturing industry must continue the innovative development of advanced materials, manufacturing processes, and systems that ensure cost efficient, rapidly flexible, high quality, and responsible production of goods and services. [ABSTRACT FROM AUTHOR]

The relevance of the subject matter is conditioned by the high-efficiency use of reconfigurable manufacturing systems during construction work and the associated need to develop and implement technological machines with replaceable working bodies. The purpose of this study is to analyze the invariance of the equipment used by types of work in landscape and landscape construction. The study used theoretical methods of analysis and synthesis of information on the principles of the practical application of a universal machine with a wide range of mounted technological working bodies. The results obtained indicate the high relevance of the issue of the rapid changeability of mounted technological working bodies. Its effective solution lies in the creation of reconfigurable production machines based on a universal wheelbase with replaceable working bodies of variable layout by type of work, for which a new design of an inter-module basing and fastening device is proposed. The possibility of its practical application in the BobCat loader system is considered. It is established that, in general, the use of the BobTach mounting frame during various technological operations ensures an increase in the efficiency of this technological equipment. In addition, it significantly increases the performance of the entire model range of equipment and testifies to the versatility of the specified mounting frame. The practical significance of the results obtained is conditioned by the possibility of their application in the development and implementation of modern technological machines with rapidly replaceable working bodies in the agricultural sector and road construction. [ABSTRACT FROM AUTHOR]

Industry increasingly focuses on data-driven digital twins of production lines, especially for planning, controlling and optimising applications. However, the lack of open data on manufacturing systems presents a challenge to the development of new data-driven strategies. To fill this gap, the paper aim to introduce a strategy for generating random production lines and simulating their behaviour, thus enabling the generation of synthetic data. So far, such data can be recorded in event logs or machine status format, with the latter adopted for the use cases. To do so, the production lines are modelled using complex network concepts, with the system's behaviour simulated via an algorithm in Python. Three use cases were assessed, in order to present possible applications. Firstly, the stabilisation of working, starved and blocked machines was investigated until a steady state was reached. The system behaviour was then investigated for different model parameters and simulation intervals. Finally, the production bottleneck behaviour (a phenomenon that can harm the production capacity of manufacturing systems) was statistically studied and described. The authors anticipate that this artificial and parametric data benchmark will enable the development of data-driven techniques without prior need for a real dataset. [ABSTRACT FROM AUTHOR]

A well-determined manufacturing system helps organisations achieve a desired competitive advantage. So, the manufacturing system selection concerns the critical decision of manufacturing strategy deployment. In the present era of industry 4.0, there exist four traditional manufacturing systems (TMS) (i.e. job-shop, batch-shop, mass, and continuous) and additive manufacturing system (AMS). Different process choice criteria (PCC) are responsible for selecting the best-suited system from five or a hybrid (AMS + TMS) configuration. This research formulates a two-stage framework comprising Delphi and Voting analytical hierarchy process (VAHP) techniques for quantifying the congruence between PCC and manufacturing systems. Initially, extant literature is reviewed to identify the critical PCC and further validated by industry experts through Delphi. This results in retaining thirty-six PCC. The relative importance of an individual criterion concerning a particular manufacturing system is computed using VAHP in the second stage to understand the alignment of PCC in different manufacturing systems for exhibiting the congruence between PCC and manufacturing system (TPS and AMS). The findings offer critical insights about the different PCC and their level-of-fit in TMS and AMS, which can assist researchers and practitioners in evaluating a suitable manufacturing system for an organisation using identified PCC. [ABSTRACT FROM AUTHOR]

IntroductionThe excessive uncertainty of in modern manufacturing systems is caused by machine failures, changes in material information, and other factors. In addition, the organizational production mode conflicts brought about by economic and technological development further exacerbate the perception of workshop interference in manufacturing systems.MethodIn order to further improve the adaptability of manufacturing systems, a control technique based on recursive control structure is proposed, which introduces an immune working mechanism to design the framework network of multi-agent manufacturing systems. Meanwhile, a negative selection algorithm is used to construct an antibody training system that considers perturbation problems.ResultThe results indicate that immune sensing nodes can effectively monitor manufacturing systems, reducing false alarm rates by over 4%. In the scheduling experiment, the completion time and equipment load improvement rate demonstrated by the research model were 3.29% and 12.38%, respectively. The production balance optimization rate exceeded 90%, far exceeding the results of traditional scheduling schemes, greatly improving the adaptive control capability of manufacturing system production.DiscussionThe regulatory approach proposed in this study can provide reference and assistance for improving the level of industrial production intelligence and establishing a sustainable economic system. However, the research results have not been applied to actual production processes, and the autonomy and coordination of intelligent manufacturing units in actual production processes still need to be further improved. In the future, research models and algorithms will be further explored in this area.

Abstract Superalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro‐Fuzzy Inference System for forecasting WEDM parameters using grey‐based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision‐making in the production of high‐quality biomedical devices and components.

Abstract Prognostic and Health Management (PHM) is an emerging maintenance concept that is highly regarded by the scientific community and practitioners, as its adoption can bring economic, technical and environmental benefits to a company. PHM fully reflects the smart maintenance paradigm encompassing data collection, data manipulation, state detection, health assessment, prognostic assessment and advisory generation. Despite the undeniable benefits, there is still a large gap between the scientific and the real world. Several authors have investigated on the barriers to PHM implementation for companies, highlighting among them the lack of systematic approaches to its design and implementation. As a first contribution to this topic, the authors conducted a systematic literature review (SLR) to investigate the use of Decision Support Systems (DSSs) to support the PHM implementation. The SLR highlighted that few DSS had been developed and were limited to critical unit identification, maintenance strategy selection and data acquisition phase of PHM. Therefore, a conceptual framework for PHM implementation was provided as a second contribution. This framework summarises the decisions that should be addressed by a practitioner wishing to implement PHM services; moreover, it could lay the foundations for the development/improvement of the missing/existing DSSs for PHM implementation.

Abstract This work focuses on detecting defects in welding seams using the most advanced You Only Look Once (YOLO) algorithms and transfer learning. To this end, the authors prepared a small dataset of images using manual welding and compared the performance of the YOLO v5, v6, v7, and v8 methods after two‐step training. Key findings reveal that YOLOv7 demonstrates superior performance, suggesting its potential as a valuable tool in automated welding quality control. The authors’ research underscores the importance of model selection. It lays the groundwork for future exploration in larger datasets and varied welding scenarios, potentially contributing to defect detection practices in manufacturing industries. The dataset and the code repository links are also provided to support our findings.

Abstract Model‐based systems engineering is now leading the way in supporting the design of complex products or systems. The integration of modelling and simulation of continuous‐discrete hybrid systems is the key of model‐based systems engineering. But the existing languages, formalisms and tools cannot support the unified modelling and simulation of hybrid systems and therefore reduces the efficiency of complex system development. To address this issue, this paper develops a design method of complex hybrid systems, which integrates modelling and simulation of the continuous‐discrete hybrid behaviour. Specifically, the authors provided a modelling method of hybrid systems based on the X language, a simulation method based on XDEVS, and a compilation algorithm to transform the hybrid model constructed with X language into XDEVS simulation files. In this way, the X language hybrid model can be automatically translated into XDEVS simulation files by a compiler. The simulation files can then be simulated by the XDEVS simulation engine. The obtained simulation results will be used to verify whether the design scheme meets the design requirements of the hybrid system. Finally, the correctness and feasibility of the proposed method are verified using a car‐driving model.

Abstract Fibre‐reinforced composites manufacturing is a large and growing industry, with much of the work carried out manually by skilled human laminators. The physical nature of the work can be significantly deleterious to these workers' health, while growing demand requires increased rates of manufacture. Human–robot collaborative manufacturing offers a potential solution but requires the human to feel confident working with the robot and trust that they will be safe. Successful human trials of two different approaches to collaborative lay‐up of fibre‐reinforced plastic composites are presented, with tasks representative of manufacturing challenges in industry. Volunteer responses are measured by questionnaires, with users reporting the processes to be safe, simple to use and allowing greater ease of manufacturing than manual‐only lay‐up.

Finite-State Automata (FSA) are foundations for modelling, synthesis, verification, and implementation of controllers for manufacturing systems. However, FSA are limited to represent emerging features in manufacturing, such as the ability to recognise and switch contexts. One option is to enrich FSA with parameters that carry details about the manufacturing, which may favour design and control. A parameter can be embedded either on transitions or states of an FSA, and each approach defines its own modelling framework, so that their comparison and integration are not straight-forward, and they may lead to different control solutions, modelled, processed and implemented distinctly. In this paper, we show how to combine advantages from parameters in manufacturing the modelling and control. We initially presenta background that allows to understand each parameterisation strategy. Then, we introduce a conversion method that translates a design-friendly model into a synthesis-efficient structure. Finally, we use the converted models is synthesis, highlighting their advantages. Examples are used throughout the paper to illustrate and compare our results and tooling support is also provided. [ABSTRACT FROM AUTHOR]

Purpose – Model-based systems engineering (MBSE) is an important approach for the transforming process from “document-centered” to “model centered” systems engineering mode in equipment development, which can effectively shorten the equipment development cycle and improve product design quality. This paper aims to understand if MBSE enables to support manufacturing and equipment development. Design/methodology/approach – The paper opted a bibliometric analysis of MBSE in domain of advanced manufacturing from different perspectives such as publication volume, research team, sources and keyword co-occurrence. Findings – Firstly, the application of MBSE in advanced manufacturing can be roughly divided into three stages. And MBSE has been widely implemented globally and has gradually formed several noteworthy teams. Secondly, this article has identified some high-quality sources, with a large number of publications and citations, the most influential publications focus on the practice or guidance of digital twins and intelligent manufacturing. Thirdly, research can be divided into six categories, including systems engineering, digitalization, intelligent manufacturing, product design, model and architecture and MBSE applications. Research limitations/implications – Because of the chosen research approach, the visualized network tends to lose certain information such as a few keywords may be inaccurately categorized. Practical implications – This paper comprehensively study the research status of MBSE in advanced manufacturing and forecasts future research trends, emphasizing the combination of intelligent manufacturing and digitization. Originality/value – This paper fulfills an identified need to understand the current application status and future development trends of MBSE.

Storage systems and warehouse processing in factories are integral to manufacturing operations and logistics procedures. Proper factory layout, storage locations, and procedures save time and reduce cost and wastes. This study examines a real case scenario of a metal industry and sheet metal factory manufacturing commercial easy-to-install metal racks. We collected data, investigated the current situation of the factory, and specified the difficulties and problems regarding the procedures, storage system, and distribution of machines. Furthermore, several proposed solutions were presented and analyzed using simulation, followed by the selection of the best solution. Then, we applied 5S methodology to improve productivity and reduce waste. The findings reveal that reducing waste and time, enhancing productivity, and better space utilization can be achieved significantly. Moreover, this study indicates the feasibility of the proposed solutions, which can be adopted by similar factories and other industries. [ABSTRACT FROM AUTHOR]

With the integration and advancement of new generation information technology and manufacturing processes, big data in manufacturing presents solutions for transforming manufacturing model today. In order to mine the hidden knowledge value and potential capabilities of big data and facilitate the intelligent decision-making of business managers in complex dynamic manufacturing environments, a comprehensive study of intelligent manufacturing management driven by big data technologies was hereby carried out. Firstly, sources and characteristics of big data in the workshop were explored, providing an overview of the enabled big data technology including data collection, transmission, storage, computation, processing, visualization, and big data mining algorithms. Then, big data application scenarios were reviewed using the intelligent manufacturing workshop as the research context, and applications of big data in furniture workshops were discussed from four major aspects, job shop scheduling, quality control, prediction maintenance, energy management, and supply chain planning. Afterwards, data challenges for furniture intelligent manufacturing in the workshop were clarified, and a conceptual framework for furniture intelligent manufacturing was finally proposed based on big data technologies. Overall, the present research offers valuable insights and ideas for furniture workshops and future research directions. [ABSTRACT FROM AUTHOR]

Teaching in higher education is often challenging for the lack of practical implementation and difficulties in student involvement. In engineering classes, students are often deeply involved in computer laboratories and projects in which they are challenged with decision-making problems. The lack of the real system that is being modelled may hinder the effectiveness of the teaching activities. In this paper, we propose a new teaching approach based on the student's interaction with lab-scale models of manufacturing systems. Students have the possibility to make observations, collect data, and implement improvements to a system, all within a course duration. The flexibility of the proposed approach enables its application to a wide range of courses, for instance manufacturing system engineering, production management, Industry 4.0. As case study, we target a course on simulation of manufacturing systems for industrial and mechanical engineering, in which students are asked to build, validate, and use a discrete event simulation model of a production system. The application of this project methodology changed the way of teaching simulation in the course and significantly improved students' evaluation and satisfaction. [ABSTRACT FROM AUTHOR]

Under the auspice of further developing 5G mobile communication technology and integrating it with the latest advancements in the field of Industrial Internet-of-Things, this study conducts in-depth research and detailed analysis on the combination of 5G with industrial systems based on composite structures, communication network architectures, wireless application scenarios, and communication protocols. The status quo, development trend, and necessity of 5G mobile communication technology are explored and its potential in industrial applications is analyzed. Based on the current practical development level of 5G technology, by considering different requirements for bandwidth, real-time performance, and reliability in communication networks of industrial systems, this study proposes three feasible paths for the integration between 5G and industrial systems, including the method to use 5G in place of field buses. Finally, by introducing real-world cases, this study has successfully demonstrated the integration of 5G and industrial systems by extending 5G terminals as field bus gateways. This study provides valuable references for research and practice in related fields. [ABSTRACT FROM AUTHOR]

The business environment is becoming increasingly fast-paced, competitive and demanding, making it vital for manufacturing processes to create agile, flexible and simplified environments. The objective of this research is to apply a new entropic and hybrid metric to measure complexity in manufacturing systems with flow shop and job shop environments. The methodological approach is based on equations that facilitate entropic analysis in different types of scenarios and provide quantitative support for decision making. The model is defined in two vectors, one of a classical subjective type based on the complexity index method (CXI), and the other of an objective type focused on Shannon's entropy metric. In both situations a new entropic metric of complexity measurement is applied. For its application, the following are used two particular case studies are used for its application and finally the results and discussions are presented. The findings allow us to solve, with the new method, the shortcomings found in the classical methods and to propose improvement bets in the areas detected in the different processes. [ABSTRACT FROM AUTHOR]