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Digital manufacturing has been challenged by the manufacturing industry to rationalize different ways to connect and exchange information and knowledge across different phases of manufacturing systems. One of the Industry 4.0 pillars is the horizontal and vertical integration with intelligent and self-adaptive systems. For this to be possible, the manufacturing industry applies an extensive range of software tools, such as GRAI, CIMOSA, MO2GO, ARIS, SCADA, MES, ERP, CAD, and CAM. Individually, each one performs its function to support the manufacturing process. However, when these multiple tools operate together using technical standards, some misinterpretation and mistake gaps are identified due to a lack of machine-to-machine (M2M) communication and users' interpretation. This is recognized as a semantic interoperability problem. Semantic technologies, such as ontologies, have been proven to be a promising way to overcome semantic interoperability obstacles. Based on this context, this study is proposing a conceptual framework based on semantic technologies to create a solution to the horizontal and vertical integration and semantic interoperability obstacle. MANUMATE is the framework proposed, and it consists of three artifacts, 1) reference ontologies, 2) requirements, and 3) application ontology, and two processes, 1) ontology specialization and 2) information application. The MANUMATE framework is applied to two experimental case studies to validate the conceptual solution in two different applications, in the context of a long-life package for the beverages industry. These case studies help elucidate how the application of the framework could improve the information and knowledge exchange by providing a standard way to represent information among different stakeholders in the productive process. A discussion about the results is presented, revealing the benefits and limitations of the solution. [ABSTRACT FROM AUTHOR]

In the medium- and long-term future, quantum technologies will become a megatrend shaping global industrial value creation. Quantum technologies will open novel pathways for manufacturing systems innovations, while also providing new opportunities for Sustainable Development. Manufacturing companies must already strive for a better understanding of quantum technologies and their potential applications if they are to maintain and improve their competitive advantage in the future. An overview of the theoretical background and state-of-the-art for quantum technologies and manufacturing systems is presented. Fields of application for quantum technologies are explored in the context of relevant manufacturing systems domains. Potential applications are proposed based on an analysis and synthesis of relevant literature, industrial case studies, as well as the authors' ideations. A case is laid out for how quantum technologies can potentially facilitate industrial symbiosis within limited global ecological boundaries. In other words, the case study describes how Sustainable Development can be potentially facilitated using quantum computing and quantum AI. [ABSTRACT FROM AUTHOR]

The problem of integrated production and maintenance control of unreliable manufacturing systems evolving in a stochastic and dynamic environment is studied in this paper. The considered system is subject to degradation and the produced products are perishable with random shelf-lives. The literature of operations management for perishable products reports a correlation between the shelf-life of the product and the machine degradation. In fact, the latter results in shelf-lives reduction. Ignoring this correlation effect may result in inaccurate values of the shelf-lives and inefficient control policies. The objective of this paper is to develop a joint production and maintenance control policy that minimizes the total cost composed of backlog, inventory holding, disposal, and maintenance costs. The proposed parametrized joint control policy combines a multi hedging point policy and an age-based preventive maintenance policy. The optimization of the parameters of the proposed joint control policy is obtained using a simulation-based optimization approach and sensitivity analyses are provided to confirm its robustness. The obtained results show that the correlation between machine degradation and shelf-life reduction has a major influence on the control parameters and that preventive maintenance interventions can lead to increasing the shelf-life of products and minimizing the total cost. The proposed joint control policy is then compared to three other polices for a wide range of system and cost data. The obtained results show that the proposed joint control policy outperforms the other polices in terms of total incurred costs. [ABSTRACT FROM AUTHOR]

Industry 4.0 has been associated with the rise of disruptive intelligence and information technologies. These cutting-edge technologies have the potential to increase productivity while simultaneously having a significant impact on social and environmental sustainability. As a result, manufacturers must evaluate the role of these innovative technologies in sustainable development, as these technologies have the potential to address prevalent sustainability issues. A content-centric study of the implementation of these Industry 4.0 cutting-edge technologies in sustainable manufacturing is currently absent. A systematic literature study was conducted to explain the potential contribution of these novel technologies to the economic, social, and environmental dimensions of manufacturing industries. This study describes how these cutting-edge technologies are used in sustainable manufacturing. The findings of this study are particularly beneficial to practitioners who seek to apply one or more digital technologies to sustainable development. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact of rescheduling policies in the event of both rework and reconditioning in job shop manufacturing systems. Since these events occur in unplanned and disrupting manner, to address them properly, it is required to manage real-time information and to have flexible reaction capacity. These capabilities, of data acquisition and robotics, are provided by Industry 4.0 Technologies. However, to take full advantage of those capabilities, it is imperative to have efficient decision-making processes to deliver adequate corrective actions. In this sense, we propose an event-driven rescheduling approach. This approach consists of an architecture that integrates information acquisition, optimization process, and rescheduling planning. We study the performance of the system with several algorithms with two performance criteria, namely, (i) relative performance deviation (RPD) in terms of objective function and (ii) schedule stability. We also propose a hybrid policy that combines full rescheduling regeneration with stability-oriented strategies aimed to balance both criteria. We conducted extensive computational tests with instances from the literature under different scenarios. The results show that a sophisticated algorithm can obtain better quality schedules in terms of the objective function but at the expense of sacrificing stability. Finally, we analyze and discuss the results and provide insights for its use and implementation. [ABSTRACT FROM AUTHOR]

In supervisory control of Petri nets (PNs), the place-invariant ($P$ -invariant) control principle is the most typical and principal method to deal with the siphon control problem. Although it has a relatively narrow application, this principle is widely acknowledged due to its simplicity and efficiency. In this article, we first propose the extended $P$ -invariant control principle in order to extend the application of $P$ -invariants and provide a general methodology for the control of siphons. Second, three types of $P$ -invariants, from the special to the general, are developed to implicitly or explicitly invariant control the siphons. In the most general case, the virtual $P$ -invariants are constructed in the PNs. Third, the extended principle is further applied to the supervisor simplification. In the paradigm of the extended principle, it presents the redundancy from a structural perspective in contrast to several typical methods, and shows the significant importance of structural analysis in PNs, especially the important role of $P$ -invariants. As a consequence, the extended $P$ -invariant control principle can be considered as the fundamental principle of siphon control as well as its supervisor simplification. [ABSTRACT FROM AUTHOR]

The article presents research on industrial quality control system based on AI deep learning method. They are a part of larger project focusing on development of Holonic Shop Floor Control System for integration of machines, machine operators and manufacturing process monitoring with information flow in whole production process according to Industry 4.0 requirements. A system connecting together machine operators, machine control, process and machine monitoring with companywide IT systems is developed. It is an answer on manufacture of airplane industry requirements. The main aim of the system is full automation of information flow between a management level and manufacturing process level. Intelligent, flexible quality control system allowing for active manufacturing optimization on the base of achieved results as well as a historical data collection for further Big Data analysis is the main aim of the current research. During research number of selected AI algorithms were tested for assessing their suitability for performing tasks identified in real manufacturing environment. As a result of the conducted analyzes, Convolutional Neural Networks were selected for further study. Number of built Convolutional Neural Networks algorithms were tested using sets of data and photos from the production line. A further step of research will be focused on testing a system in real manufacturing process for able possible construct a fully functional quality control system based on the use of Convolutional Neural Networks. [ABSTRACT FROM AUTHOR]

A digital development method is proposed for the tool management subsystem in production processes that involve permanently joining metal components by friction stir welding. A systems approach permits the development of an operational chain consisting of simulation, design, and manufacture of the tool and auxiliary equipment in group welding of various materials, with different product geometry and joint thickness. [ABSTRACT FROM AUTHOR]

Attention mechanism has the ability to find important information in the sequence. The regions of the RNA sequence that can bind to proteins are more important than those that cannot bind to proteins. Neither conventional methods nor deep learning-based methods, they are not good at learning this information. In this study, LSTM is used to extract the correlation features between different sites in RNA sequence. We also use attention mechanism to evaluate the importance of different sites in RNA sequence. We get the optimal combination of k-mer length, k-mer stride window, k-mer sentence length, k-mer sentence stride window, and optimization function through hyper-parm experiments. The results show that the performance of our method is better than other methods. We tested the effects of changes in k-mer vector length on model performance. We show model performance changes under various k-mer related parameter settings. Furthermore, we investigate the effect of attention mechanism and RNA structure data on model performance. [ABSTRACT FROM AUTHOR]

In manufacturing systems, defective items are produced for machine drift and error. Usually, an imperfect production rate is random, and if the items are not reworked, these are considered trash and harm the environment. The proposed model aims to reduce waste by reworking defective products and maximizing profit. For profit maximization or overall cost minimization of the manufacturing system, setup cost has significant. A discrete investment for each phase is introduced with an inequality investment constraint for reducing the setup cost. Selling price-dependent demand is trained for more generalized applications for various industries. The proposed model is a multi-phase manufacturing system with optimum batch size, selling price, and investment with an irregular, imperfect production rate. Defects are detected at the first inspection, and the reworked items are checked if the reworked items are all non-defective in the second inspection. The model conducts a two-stage inspection. One is for detecting defective items, and another is for checking if all items are not defective after reworking. The model is solved with the Karush–Kuhn–Tucker (KKT) method, and the global maximum profit is obtained. The model shows that all investments should be assigned to maximize the profit and the optimal solution. Reducing setup cost with the investment is better than a constant setup cost. [ABSTRACT FROM AUTHOR]

The ever-growing awareness of environmental protection has significantly influenced the method of manufacturing products. Due to the introduction of new processes, the management of sustainable manufacturing shows different characteristics to those of traditional systems. Sustainable manufacturing systems have attracted a great deal of attention in the past 20 years as an emerging manufacturing approach. Particularly in the last 10 years, the number of papers focusing on the topic of sustainable manufacturing systems' management has increased rapidly. More and more practical factors have been considered and integrated into this area which makes it more complex, but closer to reality. This paper aims to classify the mathematical problems dealing with the management of sustainable manufacturing systems. More than 100 related papers mainly from 1994 to 2015 have been selected and reviewed and divided into three categories according to the main elements in a manufacturing system: production planning and control, inventory management and control and manufacturing network design. The development of each category is summarised and the corresponding mathematical problems are discussed to provide a general overview of the relevant research fields and identify future research directions. [ABSTRACT FROM AUTHOR]

The process of redesigning production systems is usually complex, for which virtual design tools are available. These tools are used to analyse and evaluate planned changes prior to implementation, making it possible to identify and prevent costly design mistakes. Despite this, design mistakes arise during and after the implementation. A source for design mistakes is incorrect or insufficient spatial data of the production systems used in the virtual design tools. The aim of this paper is to show how to reduce the time required for planning and implementing the redesign by supporting the process with realistic visualisation, created from accurate spatial data of the real production systems. Three industrial studies were carried out to evaluate how address realistic visualisation in order to support the redesign process. The result shows terrestrial 3D laser scanning to be suitable for capturing spatial data for realistic visualisation of production systems. The realistic visualisation can be used to virtually analyse design alternatives of the production systems, by, for example, combining the 3D laser scan data with 3D CAD models. The realistic visualisation enabling effective and accurate planning, which gives the opportunity to reduce the time required for planning and implementing redesigned production systems. [ABSTRACT FROM PUBLISHER]

The contemporary manufacturing scenario witnesses the adoption of lean remanufacturing concepts in a concerted manner. Lean remanufacturing is a newly evolved manufacturing process concerned with manufacturing and remanufacturing of products to effectively utilise available energy and resources, while reducing wastes in the process and thereby increasing efficiency. The advantages include process streamlining coupled with end-of-life decisions. A structural model needs to be developed to clarify the interrelationships among factors influencing lean remanufacturing practices. In this study, interpretive structural modelling method has been used to develop the structural model depicting interrelationships and most dominant and least dominant factors. Twenty factors are being identified based on expert opinion from 35 Indian automotive component remanufacturing organisations. The identified most dominant factors include a strong top management commitment with proper strategy selection, long-term vision and participation and a strong understanding of the current product and process designs. MICMAC analysis has been conducted to categorise the factors. The inferences based on the study have been derived. The novel aspect of this study is that it presents the development of structural model to identify the most dominant factors influencing the implementation of lean remanufacturing principles. [ABSTRACT FROM AUTHOR]

This paper is a first step to understand the role that a smart city with a distributed production system could have in changing the nature and form of supply chain design. Since the end of the Second World War, most supply chain systems for manufactured products have been based on ‘scale economies’ and ‘bigness’; in our paper we challenge this traditional view. Our fundamental research question is: how could a smart city production system change supply chain design? In answering this question, we develop an integrative framework for understanding the interplay between smart city technological initiatives (big data analytics, the industrial Internet of things) and distributed manufacturing on supply chain design. This framework illustrates synergies between manufacturing and integrative technologies within the smart city context and links with supply chain design. Considering that smart cities are based on the collaboration between firms, end-users and local stakeholders, we advance the present knowledge on production systems through case-study findings at the product level. In the conclusion, we stress there is a need for future research to empirically develop our work further and measure (beyond the product level) the extent to which new production technologies such as distributed manufacturing are indeed democratising supply chain design and transforming manufacturing from ‘global production’ to a future ‘city-oriented’ social materiality. [ABSTRACT FROM PUBLISHER]

Data-driven bottleneck identification has received an increasing interest during the recent years. This approach locates the throughput bottleneck of manufacturing systems based on indicators derived from measured machine performance metrics. However, the variability in manufacturing systems may affect the quality of bottleneck indicators, leading to possible inaccurate detection results. This paper presents a statistical framework (SF) to decrease the data-driven detection inaccuracy caused by system variability. Using several statistical tools as building blocks, the proposed SF is able to analyse the logical conditions under which a machine is detected as the bottleneck, and rejects the proposal of bottleneck when no sufficient statistical evidence is collected. A full factorial design experiment is used to study the parameter effects of the SF, and to calibrate the SF. The proposed SF was numerically verified to be effective in decreasing the wrong bottleneck detection rate in serial production lines. [ABSTRACT FROM PUBLISHER]

One of the primary factors that impact the master production scheduling performance is demand fluctuation, which leads to frequently updated decisions, thereby causing instability. Consequently, global cost deteriorates, and productivity decreases. A reactive approach based on parametric mixed-integer programming (MIP) is proposed that aims to provide a set of plans such that a compromise between production cost and production stability is ensured. Several stability measures and their corresponding MIP model are proposed. An experimental study is performed to highlight the effectiveness of the reactive approach with regard to the proposed performance measures. It is observed that an improvement in stability does not mean a significant increase in the total production cost. Furthermore, the procedure yields a set of plans that in practice would enable flexible management of production. [ABSTRACT FROM PUBLISHER]

Informational support of production management is considered for the organization of complex production schedules in machine building. At manufacturing enterprises today, the product range varies over a short time frame. Many products of diverse design will be produced at the same time, by different technologies. In planning the production processes to be employed, the goal is to maximize the load of the manufacturing equipment. The load is related to the number of production buildings simultaneously in use. To optimize building use, the manufacturing processes must be distributed appropriately over the enterprise's production system. In turn, rational distribution of the processes entails mathematical modeling of the sequence of building use and selection of the best option. [ABSTRACT FROM AUTHOR]

In manufacturing systems, there are environments where the elaboration of a product requires a series of sequential operations, involving the configuration of machines by stages, intermediate buffer capacities, definition of assembly lines, and routing of parts. The objective of this research is to develop a modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments. The methodological approach starts with the structural modeling, then the measurement of the complexity in the systems is developed, the hypotheses are proposed, and finally an experimental and factorial statistical analysis is developed. The results obtained corroborate the hypotheses proposed, where statistically the structural design factors and the variation of production time per stage have a significant influence on the response variable associated to the total complexity. Similarly, there is evidence of correlation between the performance indicators and the variable studied, in which the incidence with production costs stands out. [ABSTRACT FROM AUTHOR]

Contribution: This article discusses the use of manufacturing simulation games to study collaborative problem-solving skills in engineering students. The simulation represents the mass production paradigm in which large quantities of identical products are produced. Empirical data is collected from the simulation to evaluate the skills engineering students used in solving the problem and their group effectiveness. Background: The use of simulation games to teach problem solving in design and manufacturing is an effective approach to convey concepts to students. Simulation games engage students in experiential and collaborative learning with fun elements. Research Questions: How does hands-on simulation engage students in collaborative problem solving? How does participation in collaborative problem solving affect group effectiveness? Methodology: This work presents a study of 37 university-level engineering students in the United States. Participants worked in groups completing the simulation game and responded to surveys on their various skills used. Findings: Participants utilized analytical, metacognitive, and thinking skills in their engagement, reported that the simulation games enhanced their understanding of manufacturing concepts and active collaboration improved problem-solving effectiveness. [ABSTRACT FROM AUTHOR]

In recent decades, deadlock control for automated manufacturing systems has been an active area. Most researchers have assumed that allocated resources, such as sensors, actuators, and controllers never fail. However, this case is not prevalent in practice due to the unexpected failure of resources. Thus, the objective of robust control is presented in this article. Several methods have been developed along this direction, such as methods that combine neighborhood constraints and the modified Banker’s algorithm, as well as methods based on critical places. To explore their effectiveness and performance, we not only conduct a comparison investigation but also develop new theoretical results. According to the experimental results, critical place-based approaches are simpler, more efficient, and more comprehensive than the Banker’s algorithm-based approaches in response to resource failures. This article is motivated by the control of production Petri nets; however, the results are also applicable to other more complex systems. [ABSTRACT FROM AUTHOR]

Manufacturing systems may be subject to external attacks and failures, so it is important to deal with the recovery of the system after these situations. This article deals with the problem of recovering a manufacturing system, modeled as a discrete event system (DES) using the supervisory control theory (SCT), when the control structure, called supervisor, desynchronizes from the physical plant. The desynchronization may be seen as plant and supervisor being in uncorresponding states. The recovery of the system may be attained if there is a word, the synchronizing word, that regardless the state of each one of them, brings the system and supervisor back to a known state. The concepts of synchronizing automata are used to do so. In this article, we show under what conditions a set of synchronizing plants and specifications leads to a synchronizing supervisor obtained by the SCT. The problem is extended to cope with multiple supervisors, proposing a local recovery when possible. We also present a simple way to model problems, composed of machines and buffers, as synchronizing automata such that it is always possible do restore synchronization between the control (supervisor) and the plant. Note to Practitioners—Given the unpredictability of faults and malicious attacks occurring in industrial systems, recovery strategies are crucial for a harmonic operation of the plant. The possibility of leading the system to a known state, recovering control, is of extreme importance to the safety of industrial processes. The method proposed in this article uses well-known concepts of supervisory control theory (SCT) of discrete event systems (DESs), introducing the recovery process (using recovery events) in the modeling phase such that it is possible to isolate and fix only the part of the control system subject to the fault. The result of the proposed approach allows the implementation of such control system with the recovery procedure directly in the programmable logic controllers (PLCs). [ABSTRACT FROM AUTHOR]

This paper deals with technological and operational aspects of manufacturing process planning for multiple values of decision parameters. A discrete parametric model is proposed to generate multiple process plans for a given or proposed production system. The model is based on generating graphs for possible manufacturing process operations and production system capabilities. An automated platform-based system is built around this model to generate and analyze multiple manufacturing process plans based on the chosen priority criteria. The analyses are based on estimating distances in constructed graphs and several sets of priorities for final assembly production systems as well as parts suppliers. Both manufacturing process selection and production systems architecture used in this model are specifically adapted for the aerospace industry. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of deadlock for sequential automated manufacturing systems (AMSs) that allow for the general resource allocation and flexible routings. A class of Petri nets, systems of sequential systems with shared resources (S4PR), are used to model these considered AMSs. Our previous work has showed that deadlocks in S4PR are characterized by saturated perfect activity-circuit (PA-circuit). In this article, we divide all saturable PA-circuits into two categories: 1) dependent and 2) independent. An algorithm is proposed to compute all independent saturable PA-circuits. We prove that by adding a monitor for each independent PA-circuit to ensure that it is not saturated, all dependent PA-circuits cannot be saturated either and deadlocks in S4PR are successfully prevented. The presented method simplifies the structure of the deadlock controller without imposing tight constraints on the system. Finally, the proposed controller is illustrated by some examples. [ABSTRACT FROM AUTHOR]

Industrial big data and artificial intelligence are propelling a new era of manufacturing, smart manufacturing. Although these driving technologies have the capacity to advance the state of the art in manufacturing, it is not trivial to do so. Current benchmarks of quality, conformance, productivity, and innovation in industrial manufacturing have set a very high bar for machine learning algorithms. A new concept has recently appeared to address this challenge: Quality 4.0. This name was derived from the pursuit of performance excellence during these times of potentially disruptive digital transformation. The hype surrounding artificial intelligence has influenced many quality leaders take an interest in deploying a Quality 4.0 initiative. According to recent surveys, however, 80–87% of the big data projects never generate a sustainable solution. Moreover, surveys have indicated that most quality leaders do not have a clear vision about how to create value of out these technologies. In this manuscript, the process monitoring for quality initiative, Quality 4.0, is reviewed. Then four relevant issues are identified (paradigm, project selection, process redesign and relearning problems) that must be understood and addressed for successful implementation. Based on this study, a novel 7-step problem solving strategy is introduced. The proposed strategy increases the likelihood of successfully deploying this Quality 4.0 initiative. [ABSTRACT FROM AUTHOR]

Manufacturing system digitalization has recently surged due to Industry 4.0 developments. The interaction between manufacturing and computer science disciplines has driven a new generation of cyber-physical production systems. Smart factories can collect and process a large amount of data, and this data must be used meaningfully to raise manufacturing efficiency and sustainability. We posit, that in the future, improvements can be achieved through biointelligent manufacturing systems. These systems combine technological innovations, digitization, and artificial intelligence with principles derived from nature and biotechnology. Biological transformations of manufacturing systems hold many future possibilities. We provide a general overview of manufacturing system biological transformation and propose key areas for practical application in organizations. [ABSTRACT FROM AUTHOR]

Due to the fourth industrial revolution, manufacturing companies are looking to implement digital twins in their factories to be more competitive. However, the implementation of digital twins in manufacturing systems is a complex task. Factories need a framework that can guide them in the development of digital twins. Hence, this article proposes a small-scale digital twin implementation framework for manufacturing systems. To build this framework, the authors gathered several concepts from the literature and designed a digital twin subsystem model using a model-based systems engineering (MBSE) approach and the systems engineering “Vee” model. The systems modelling defines the digital twin components, functionalities, and structure. The authors distribute most of these concepts throughout the framework configuration and some concepts next to this general configuration. This configuration presents three spaces: physical, virtual, and information. The physical space presents a physical layer and a perception layer. The information space has a single layer called middleware. Finally, the virtual space presents two layers: application and model. In addition to these layers, this framework includes other concepts such as digital thread, data, ontology, and enabling technologies. This framework could help researchers and practitioners to learn more about digital twins and apply it to different domains.

There are numerous hazards to be found in almost any workplace. Annually, millions of workers die, are injured or become ill as a result of these occupational hazards. Industrial machines are often involved in these occupational accidents. Because of the demands of regulatory compliance, and the potentially high cost in terms of human suffering and lost production, businesses should place particular emphasis on safety measures. Risk is defined as a combination of the probability of harm and the severity of its consequences. Generally, risk estimation involves examining the hazards associated with a situation or with the use of a machine. A large number of techniques have been proposed for risk estimation, and recent studies have revealed that some of them have serious flaws. The main objective of this paper is to develop a proposed risk assessment tool based on the findings of an earlier study. Our research results constitute a first step towards the integration of occupational health and safety (OHS) concerns into facility planning models which traditionally do not consider OHS. The proposed risk estimation tool is developed based on the characteristics, strengths and weaknesses of 31 existing risk estimation tools, and is then applied to 20 scenarios representing different hazardous situations. To evaluate the performance of the proposed tool, the results were compared with those of other risk estimation tools and confirmed its proposed ability to estimate risk relative to other risk estimation tools. [ABSTRACT FROM AUTHOR]

Scheduling of assembly manufacturing systems has received much attention in recent years, but the scheduling problem of deadlock-prone flexible assembly systems (FASs) is rarely studied. Based on the Petri net models of FASs, this article studies the scheduling problem of deadlock-prone FASs, and the heuristic beam search (HBS) algorithm is proposed to minimize the makespan. In HBS, the state space of the FAS is searched level by level, and two types of evaluation functions are proposed to ensure that only the most promising states are explored. To find a better solution in an acceptable time, the HBS algorithm is integrated with the iterative policy, and the iterative widen heuristic beam search (IWHBS) algorithm is obtained. Experimental and comparison results validate the effectiveness and superiority of the IWHBS algorithm over other heuristic search algorithms. [ABSTRACT FROM AUTHOR]