Integrated Manufacturing Systems Research Articles

Preface

The 2023 4th International Conference on Advanced Materials and Intelligent Manufacturing (ICAMIM 2023) was held in Guangzhou, China during December 1st to 3rd, 2023. The series of ICAMIM conference was created with the aim to be an interdisciplinary incubator for the worldwide exchange of information and innovative ideas about the latest research results in the fields of advanced materials and intelligent manufacturing.At ICAMIM 2023, we wished to bring together a broad community of researchers from different branches of the rapidly developing complex science to discuss the fundamental theoretical challenges as well as experimental and applied aspects of many practical problems. The agenda was conducted mainly based on three parts, covering keynote speeches, oral & poster presentations, and academic tour.There were six keynote speeches and many regular oral talks in the morning and afternoon sessions. The topics of keynote speeches cover: a) Scientific Carbon Reduction, Green Development, Manufacturing Industry to Achieve the Goal of “Double Carbon” Efforts; b) ChatGPT with Intelligent Manufacturing System; c) Challenges Facing the Industrialization of Sodium-Ion Batteries; d) Microstructure Control and Application Properties of Ultra-High Strength and Toughness Steels; e) Double Growing Carbon Background Material Base Environmental Functional Materials Development Prospects; f) Current Status and Development of the New Materials Industry in Guangdong. These speeches allowed all the participants to interact with the speakers and people around and get some new insights from each other. An academic discussion part was also held, the topic of which was related to the recent and anticipated future progress in advanced materials and intelligent manufacturing, discussing a variety of questions and opinions important for the understanding of the technology and management methods of the industry itself.The papers submitted to this Conference were carefully reviewed by expert referees, covering topics on: Composites, New feature Materials, Integrated Manufacturing Systems, Advanced Manufacturing Technology, Intelligent Mechatronics, etc. We are very grateful to the reviewers for their efficient and thoughtful actions. Various excellent papers were included here based on the referee reports.We are thankful to the members of Technical Program Committee for their time and efforts in preparing the program of ICAMIM 2023. We also appreciate the behind-the-scenes work of the members of Organizing Committee in preparing the conference site, arrangements for excursions and accommodation, handling the financial support for participants, and so on. Finally, we acknowledge the publishing support from Journal of physics: Conference SeriesThe Committee of ICAMIM 2023List of Committee Member is available in this pdf.

Requirements engineering in industry 4.0: State of the art and directions to continuous requirements engineering

AbstractThe 4th Industrial Revolution, also known as Industry 4.0, intends to transform manufacturing processes into smart factories with full digitalization and intelligent, decentralized, and flexible production. In this scenario, Industry 4.0 systems (i.e., software‐intensive systems that automate smart factories) have required rigorous and continuous development, but smart factory companies often have difficulty dealing with Requirements Engineering (RE) where requirements continuously change and emerge at runtime to support the changeability of complex production processes. Such requirements encompass engineering (e.g., mechanical, electrical, electronic, production/manufacturing) and business areas and involve the vertical and horizontal integration of heterogeneous manufacturing systems. There is also a lack of a panorama of how Industry 4.0 projects have performed with RE activities. The main goal of this paper is to present the state‐of‐the‐art research concerning RE in Industry 4.0 and draw attention to the next most urgent steps. For this, we selected and examined studies that address RE for Industry 4.0, noting that much of this literature is recent but does not fully address the complexity and dynamism of the requirements for Industry 4.0. Grounded on these studies and our academic and industry experience, we highlight the need for Continuous Requirements Engineering (CRE) for Industry 4.0.Significance and Practitioner Points: The main implications of this paper are: (i) For researchers: It offers the state of the art of RE in the context of Industry 4.0 and points out several important open issues that require an urgent investigation through new research topics; and (ii) For practitioners: It provides directions for new or even existing Industry 4.0 projects on how to deal with RE activities aiming to overcome the several challenges to perform them.

Effective integration of low-cost digital manufacturing systems: a reference architecture driven approach

ABSTRACT Integration involves the systematic linking of system components, functions and solutions. For digital systems comprising low-cost components, integration enables the sharing of resources and allows them to benefit from each others functionalities. However, in general integrating low-cost systems can be difficult, since data are heterogeneous and distributed over multiple sources and there is no common interface to interact with resources. This study proposes the use of reference architectures as a means of supporting such integration needs. Reference architectures are models that provide a structured template with common terminology and support integration through a systematic, repeatable framework. In this paper, we evaluate strategies to integrate digital systems formed from the same reference architecture. We first select three reference architectures and use them to conceptually design the integration of two low-cost digital systems based on different strategies. Then, the conceptually designed strategies are evaluated in terms of their effectiveness to be realised at low cost. Finally, the most effective strategy is deployed for an industrially relevant application, and validated by comparing its effectiveness to a system architecture driven integration. The evaluation results suggest that coordinators with a common service bus and virtual data integration are effective for integrating low-cost digital systems.

DIGITAL MANUFACTURING MODEL FOR REMOTELY OPERATED VEHICLE (ROV) IN INDONESIA: A CONCEPT STUDY

Industrial revolution 4.0 era represents a paradigm shift in manufacturing and technology, which is characterized by an integrated and interconnected system to support the operation of an industry. Many countries contributed to this era, including Indonesia. As a maritime country, maritime-related products have the potential to be offered. Remotely operated vehicles (ROV) is a form of digitalization product that is frequently used to support the operation of the maritime industry. Therefore, ROV is manufactured locally to provide easier access to industries demanding ROV. Digital manufacturing for ROV can bring further contributions towards industrial revolution 4.0, which involves the usage of digital technologies such as computer-aided design and manufacturing (CAD/CAM), internet of things (IoT), artificial intelligence (AI), and robotics to create an integrated manufacturing system. Furthermore, digital manufacturing information system provides real-time information during the manufacturing process. According to this study, digital manufacturing can significantly improve the efficiency and integration in manufacturing the ROV.

Additive manufacturing systems integration

This research explores real-time database systems’ evolution, focusing on unique features and the addressed challenges. It examines the role of multi-material additive manufacturing quality domain databases in innovation and maintaining standards. It also looks at the challenges of implementing quality manufacturing systems from a technology, organization, and people of European Manufacturing research perspective. The study offers a new perspective on reconfigurable intelligent surfaces with multi-material additive manufacturing with system integration, discussing its applications and digital products’ transformative potential. Enhancing multi-material additive manufacturing capabilities redefines the industries, creating a strong communication culture by adopting quality integration of quality and robotics while exploring the future of mathematics complexity in optimizing manufacturing education. The paper also explains the statistical classification of historical technology in manufacturing engineering education by flagging the platform‘s role of opportunities in secure research collaboration. The manufacturing horizontal is essential for effectively managing the multi-material additive manufacturing system through advanced technology to quality management integration. Certified advanced training and competency development econophysics show the multi-material additive manufacturing systems development influence on the production theories and mechanics of complex electronics. To enable, for example, communication links and cellural multi-material additive manufacturing integrations research for new technologies.

Investigation of scheduling integration of flexible manufacturing systems for mass customisation

In contemporary manufacturing and supply chain environments, there are various challenges that companies have to face. Many companies are facing challenges to respond quickly to customers’ requirements and to provide customised products cost effectively. Mass customisation (MC) can help companies to provide customised products and services quickly at a low price. Integrated decision-making can obtain performance improvement in many situations. This paper investigates the problem of scheduling integration of flexible manufacturing systems (FMSs) for mass customisation. A mathematical model is formulated for the problem. A set-based dynamic algorithm is developed. Numerical studies are made on the proposed algorithm by simulation and by statistical analysis. Conclusions, managerial implications, and future research suggestions are provided.

Cloud Computing Application for Digital Integration Between an Advanced Manufacturing Plant and a Model 4.0 Factory

Industry 4.0 is a global digitalization goal for today’s industries, and cloud computing is a solution that enables ubiquitous and convenient access to infrastructure, platforms, and services. Together with fog and edge computing, it leverages the advantages of manufacturing systems integration. This research proposes a cloud architecture to integrate an advanced manufacturing plant and a SENAI CIMATEC model factory. Before implementing this architecture, assessing whether the environment is adaptable is necessary. For this, fundamentals such as the TOE structure (Technology-Organization-Environment Framework) and the SEM model (Structural Equation Modeling) are used, which evaluate the degree of adherence for the application. As a methodological procedure for the research, a database is being modeled to serve the information flow between the systems. Therefore, adapting the environment to cloud computing is crucial to the success of digital system integration and process automation and optimization advances.

PLC orchestration automation to enhance human–machine integration in adaptive manufacturing systems

Current approaches to manufacturing must evolve to respond to increasing demands for short product life cycles and customised products. Adaptive manufacturing systems integrate advanced technologies, automation, and data-driven methodologies to develop adaptable, efficient, and responsive production processes. Central to this concept is the emphasis on human involvement and fostering synergy between human operators and the manufacturing system. Significant changes to the system’s controller are required to achieve adaptivity, with programmable logic controllers (PLCs) being a common controller type. After the necessary changes to the configuration of the manufacturing system, the PLC should be reconfigured to orchestrate the new required behaviour. Automated reconfiguration is vital to rapidly responding to change, but some changes cannot be entirely achieved without human input in collaboration with automated methods. Conventional practices in PLC programming include manual, repetitive coding practices subject to errors. As a result, to ensure operational safety, the changes must be tested before being deployed to operations, ensuring it is error-free. This paper presents a methodology to automatically reconfigure the simulation environment and controller in response to a new product request. We automate the PLC code generation and testing practices to support and free up the operators when performing repetitive manufacturing reconfiguration tasks. The methodology is based on human learning, software automation, customised program development, knowledge graphs, and Graph Neural Networks (GNNs). The presented solution is a generic, vendor-agnostic, and interoperable solution that facilitates information exchange among multiple heterogeneous environments. Lastly, we have validated the methodology as a proof of concept at an adaptive assembly cell at the University of Nottingham in the United Kingdom.

An ontology for maintenance procedure documentation

In mining, manufacturing and industrial process industries, maintenance procedures are used as an aid to guide technicians through complex manual tasks. These procedures are not machine-readable, and cannot support reasoning in digitally integrated manufacturing systems. Procedure documents contain unstructured text and are stored in a variety of formats. The aim of this work is to query information held in real industrial maintenance procedures. To achieve this, we develop an ontology for maintenance procedures using the OWL 2 description language. We leverage classes and object properties from the ISO 15926 Part 14 Upper Ontology and create a domain ontology. The key contribution of this paper is a demonstration of trade-offs required when modelling an existing engineering artifact, where an abstraction of its contents is given a-priori. We provide an ontologically rigorous abstraction of notions captured in procedure documentation to a set of classes, relations and axioms that allow reasoning over the contents. Validation of the ontology is performed via a series of competency questions based on queries relevant to technicians, engineers and schedulers in industry. The ontology is applied to real world maintenance procedures from two industrial organisations.

Preface

The International Conference on Artificial Intelligence and Industrial Technology Applications originates from 2021 and has attracted various promising researchers who are developing new directions in the field of artificial intelligence and industrial technology. The 2023 3rd International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA 2023), held in Suzhou, China from March 24th to 26th, 2023, was meant to unite researchers, experts, professors and academic staff as well as a wide range of participants of professional community from China and other countries concerned.AIITA 2023, with about 150 participants, was held to further integrate artificial intelligence with industrial technologies, and promoted research and developmental activities in related fields as well as scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world.Highly eventful program of the Conference, the list of well-established organizations-participants and devoted engagement of many colleagues throughout all the stages of Conference preparation instill confidence in practical importance of mutual initiative. The framework of the Conference comprises keynote speeches, oral presentations, and academic investigation, discussing open and significant problems facing industrial technologies, proposing innovative ideas and approaches to solution of the problems, and considering the new possibilities of application and development of cutting-edge artificial intelligence with industrial technologies.The Conference has become one of the venues for demonstrating scientific achievements of international class by leading scientists and young scholars and would facilitate the strengthening of academic cooperation, meanwhile the Conference proceedings would be expressed in the form of international scientific publishing. Various topics of the papers, gone through a vigorous peer review process, are covered in the proceedings, including: Artificial Intelligence Algorithm, Information Retrieval and Fusion, Intelligent Mechatronics, Integrated Manufacturing System, Intelligent Service Machines, etc.On behalf of the Conference Organizing Committee, we would like to thank the referees for their efficient and thoughtful actions. We are grateful to the members of the Technical Program Committee for their efforts in making and shaping the program for AIITA 2023. Particularly, we acknowledge the publishing support from the members of Journal of Physics: Conference Series.We hope that the future AIITA will be as successful and stimulating, as indicated with the contributions presented in this volume.The Committee of AIITA 2023List of Committee Member are available in this pdf.

Critical factors of multi-agent technology influencing manufacturing organizations: an AHP and DEMATEL-oriented analysis

ABSTRACT The current research aims to analyse the critical factors of Multi-Agent Technology (MAT) which influence manufacturing organizations to enhance quality and reduce maintenance costs by implementing a combined approach of Analytical Hierarchical Process (AHP) and Decision-Making Trial Evaluation Laboratory (DEMATEL). A framework has been developed in which AHP is used to quantify the relative importance of each MAT variable and DEMATEL is used to capture the MAT variable interaction. The hybrid approach of AHP and DEMATEL concludes that ‘integration of manufacturing systems with maintenance’ is the highest weight factor, followed by ‘manufacturing processes’, ‘fault detection’, ‘organizational productivity’, and ‘organizational control’. This suggests that efficient MAT deployment in industrial firms could improve product quality by decreasing maintenance efforts. This study provides a tested and validated framework to motivate the decision-makers, professionals, and researchers of a variety of manufacturing organizations to implement this framework in a customised way for improving quality and cutting maintenance costs.

Alternating Additive Manufacturing and Forming—An Innovative Manufacturing Approach

In this work, an innovative manufacturing approach that includes a fully linked and integrated manufacturing system consisting of a laser-based directed energy deposition (DED-LB/M) module and a forming press is presented. The alternating additive manufacturing (AM) process is based on a combination of a DED-LB/M process using a laser power of 600 W and a feed rate of 400 mm/min and a subsequent forming process, in which the structure is upset with a hydraulic press using a constant forming force of 500 kN in order to smooth the surface and influence the accuracy of the components. For the generation of a fundamental process understanding, a cuboid, basic shape was chosen as geometry for the investigations. The aim is to improve part properties by applying the process steps to generate part properties, which are superior to solely additive manufactured material. It is shown that the geometry of additive manufactured structures can be adapted, and the top surface can be smoothed due to the forming operation. The mean roughness value Rz decreases up to 50% after the forming operation. The hardness can be increased by work hardening. Of special interest is that the higher hardness can be kept up even though a further DED-LB/M process step and forming operation are applied to the additively manufactured and formed structure again. Finally, an analysis of the new manufacturing approach regarding its potential is given.

Preface

With the rapid development of science and technology, the development and innovation in all domains become faster and faster. The field of materials science and intelligent manufacturing have also produced more research directions and new discoveries. Focusing on the latest research results in various aspects of that and other related fields, 2023 International Conference on Materials Science and Intelligent Manufacturing (MSIM 2023) was held via virtual form in Xi’an, China from January 13th to 15th, 2023.Our aim is to establish a forum to exchange and share successful experiences of innovation in the above fields and establish an effective cooperation platform for institutions and industries. Focusing on such topics as Electronic and Magnetic Materials, Ceramic and Glass Materials, Crystal Defect Engineering, Intelligent Mechatronics, Advanced Manufacturing Technology, Integrated Manufacturing System, etc., the Conference invited scientists, engineers, educators and students from all over the world to exchange their current technical application experience and research results, so as to promote the development of related fields.The entire Conference was in an online mode. First is an about 3 hour’s keynote speech and next 2.5 hour’s oral presentation. Discussion and Question and Answer happened went through out the meeting. The professors and delegates presented papers and researches from their own research domains and experiences. We had about 50 participants in the online platform, and had continuous stable internet connection, conducting the virtual conference smoothly.In the Conference, four renowned professors acting as keynote speakers shared their researches with us. Among them, Prof. Xiaofang Yuan from Hunan University, China, whose research domains cover Intelligent Control Theory and Application, Electric Vehicle Control, Robot Drive Control, first made a speech on 3D Map-based Path Planning for Autonomous Vehicles. In the traditional vehicle area, energy-based motion control technology and battery technology are usually employed to solve the energy-saving problem for vehicles. His research proposed a new solution from the perspective of path planning. For vehicles traveling on the complex 3D terrains, the energy consumption of up-slope is far greater than that of the flat road and down-slope. To realize this, a path with a good trade-off between the energy consumption and distance would be the expected route for electric vehicle and mining transportation. A novel multi-objective path planning method was investigated to solve this problem for EV and mining truck, generating an optimal path while saving much energy.We would like to express our gratitude to all the participants for their fruitful work and excellent contribution to MSIM 2023. Especially, we appreciate the enormous work of the editorial board and reviewers in the preparation of this volume. Also, our special thanks and appreciation go to the Journal of Physics: Conference Series. Hopefully, all participants and other interested readers can benefit scientifically from the proceedings and find it rewarding in the process.The Committee of MSIM 2023List of Committee Member is available in the pdf.

DESIGNING ARM ROBOT BASED ON INTERNET OF THINGS AS A LEARNING MEDIA

Technology is evolving rapidly push the manufacturing industries to achieve an optimal production, among of them is using automated machines to convenience their production. To synergize with those evolution, the education system also needs to evolve to make sure that the educational material contains follow the trends of automation and increase the level of understanding about technological developments. Based on a survey given to Industrial Engineering students at Diponegoro University who were studying the Integrated Manufacturing System courses the class said that automation tools are very important in processes in industry, but their understanding about the description of the automation in the industrial world have a different understanding level, their level of understanding was 35%. understand, 35% quite understand and 30% do not understand. Therefore, the researcher designed a learning media based on student requirement and Curricullum of Intregated Manufacturing System Subjects to support an understanding of automation learning, especially Industrial Robotics. The learning Media use the Engineering Design Process method, to obtain a teaching tool that is in accordance with existing requirements, its called Arm Manipulator Robot whose using Internet of Things to do material handling work. This tool is handling with a rate of accuracy 74%, which is classified as very feasible as a learning media

Research on Driving Factors of Collaborative Integration Implementation of Lean-Green Manufacturing System with Industry 4.0 Based on Fuzzy AHP-DEMATEL-ISM: From the Perspective of Enterprise Stakeholders

The existing research and practices have shown that the coordinated implementation of lean-green manufacturing can have a positive impact on the economic and environmental benefits, which is an effective means to ensure the environmental protection of the production process of manufacturing without damaging their profitability. Within the field of lean-green research, there is still a lack of research to analyze the driving factors for the collaborative implementation of integrated lean and green integration. Although, some scholars and researchers have studied lean and green integration paradigms, their research has mostly focused on lean-green integration practices and their impact on environmental performance and their respective operations. In the context of Industry 4.0, this article investigates the driving forces behind the collaborative integration implementation of a lean-green manufacturing system from the viewpoint of stakeholders. Specifically addressing the issues of correlation and ambiguity in the identification of driving factors, this manuscript proposes an Interpretation Structure Model (ISM) of fuzzy comprehensive Analytic Hierarchy Process (AHP), based on Decision-Making Trial and Evaluation Laboratory (DEMATEL), to determine the importance of the driving factors. Combined with the complex network theory, the evaluation index system is divided into four levels from eight factor categories, including endogenous lean-green driving factors and exogenous driving factors. The fuzzy AHP-DEMATEL-ISM is used to analyze the relationship between indicators and the structure of the indicator system. The complex network which is composed of the indicator system is divided into different levels. The importance of indicators is analyzed from the perspective of the global network, and key factors affecting the driving of lean-green system is analyzed. The integration of the lean-green manufacturing system and organizational synergy are promoted to jointly lead the enterprise toward sustainable development by paying particular attention to the primary impact indicators and aggressively cultivating the key impact indicators.

ISSUES OF CYBERSECURITY OF THE PRODUCTION SYSTEM

Cybersecurity of production systems is a very topical issue in response to the dangers associated with the very high number of cyber-attacks in Europe. The current digital transformation of the industry associated with the digitalisation of manufacturing brings both the positives associated with increased flexibility and productivity of production, and the negatives associated with the risk of data loss, data alteration or blocking of control systems. Cybersecurity issues are also reflected in the revision of EU harmonisation legislation governing the requirements for placing products on the market or in service. It appears that the issue of cyber security needs to be addressed comprehensively across the entire infrastructure of a manufacturing plant. The present paper focuses on the relationship between machinery security and OT/IT security and presents the results of a study aimed at identifying potential sources of threats in an integrated manufacturing system.

Machining feature recognition based on a novel multi-task deep learning network

• Propose a novel learning-based machining feature recognition method that can effectively determine the faces that constitute machining features. • Construct a novel multi-task deep learning network based on point cloud data that can realize machining feature segmentation, machining feature identification, and bottom face identification simultaneously. • Propose a novel loss function that can be applied to the tasks of multi-label classification in deep learning. • Promote the application of deep learning in the manufacturing industry. Machining feature recognition is an essential task to realize the integration of computer-aided design (CAD), process planning (CAPP), and manufacturing (CAM) systems. In general, traditional rule-based machining feature recognition approaches are inflexible and computationally expensive. Moreover, it's not easy to design rules for intersecting machining features. To this end, various learning-based approaches were proposed in recent years. However, some of the existing learning-based approaches are complicated, time-consuming, and hard to deal with intersecting machining features, while some only approximately locate the position of the machining features rather than accurately segmenting them from the parts, which is not conducive to subsequent process planning. To address the above issues, a novel multi-task network named Associatively Segmenting and Identifying Network (ASIN) based on point cloud data is proposed for machining feature recognition. The proposed network performs three tasks: clusters the part faces with high similarities into machining features whose type are unidentified, predicts the semantic class (e.g. hole, pocket) for each face to identify the type of each machining feature, and identifies the bottom faces of the machining features. Through the three tasks, ASIN can realize machining feature segmentation, machining feature identification, and bottom face identification simultaneously. The final machining feature recognition results are obtained by combining the results of the above three tasks. After being trained with a large labeled point cloud dataset of 3D CAD models and a novel loss function, ASIN can be used to recognize machining features. Experimental results demonstrate that the proposed approach can effectively segment the machining features by determining the faces that compose them, and performs well in recognizing intersecting machining features.

An Efficient IIoT Gateway for Cloud–Edge Collaboration in Cloud Manufacturing

The cloud manufacturing system can provide consumers with on-demand manufacturing services, which significantly improve the utilization rate of distributed manufacturing resources and the response speed of personalized product needs. In the cloud manufacturing platform, the successful implementation of various industrial applications relies on the uploading and streaming of related field-level manufacturing data. For example, the realization of manufacturing service composition application should match the manufacturing tasks with distributed manufacturing resources according to their working state data and performance measurement data. Therefore, this paper proposes a data integration and analysis framework of a cloud manufacturing system based on cloud–edge collaboration and the Industrial Internet of Things (IIoT). A service-oriented information model is established to uniformly describe the related operational data and functional attributes of heterogeneous manufacturing resources. Secondly, a real-time transmission and integration method of high-volume operational field and sensor data based on message middleware is proposed to realize the remote monitoring of distributed manufacturing resources and efficient distribution of related data. Finally, a cloud–edge collaboration mechanism is put forward to train and update the parameters of various artificial intelligence models deployed at edge gateways. In the experiment, taking the computer numerical control (CNC) lathe as an example, the effectiveness of the proposed manufacturing resource access method is verified. Taking the fault diagnosis model of the CNC lathe as an example, the efficiency of the proposed cloud–edge collaboration mechanism for model updating is verified.

A proposed integrated manufacturing system of a workshop producing brass accessories in the context of industry 4.0

Industry 4.0 is a basic step in the upgrade of manufacturing companies into digital enterprises. It allows more flexibility in manufacturing, as well as mass customization, provides better quality, and enhances productivity. As a result, it empowers companies to meet the challenges of smart manufacturing which is increasingly individualizing products with shorter time-to-market span and improving quality. Smart manufacturing has an important part in Industry 4.0. This study aims to characterize and analyze a smart manufacturing process designed for a company specialized in the production of brass accessories: spherical bushels. We basically set up a simulation tool to develop a numerical production platform for Industry 4.0 which is able to efficiently operate and manage the production and procurement through material requirement planning (MRP, master production program), logistics warehouse, and cyber-physical production system (CPPS). The findings have been optimized by a new redesigned approach of MRP 2: load-capacity adjustment for smart workshop and Industry 4.0 manufacturing planning. It is the setup of an integrated manufacturing system process, which resulted in reducing the spherical bushels’ assembly time and controlling the production and the assembling process. It allowed us to increase the equipment utilization rate by comparing it with the company’s former equipment running time prior to switching to smart manufacturing. The proposed model proves that we can successfully optimize the practical application of Industry 4.0 and therefore significantly increase the production efficiency. We are confident that this is the first work addressing the implementation of a simulation platform controlled by a dedicated cyber-physical production system (CPPS) and a master production program (MPP). A case study of a company manufacturing brass accessories is presented in this paper. The developed simulation platform offers the possibility to create a future digital twin of the company.

Smart Manufacturing and Digitalization of Metrology: A Systematic Literature Review and a Research Agenda.

Smart manufacturing comprises fully integrated manufacturing systems that respond in real time to meet the changing demands and conditions in industrial activities, supply networks and customer needs. A smart manufacturing environment will face new challenges, including those concerning metrological issues, i.e., analysis of large quantities of data; communication systems for digitalization; measurement standards for automated process control; digital transformation of metrological services; and simulations and virtual measurement processes for the automatic assessment of measured data. Based on the assumption that the interplay between smart manufacturing and digitalization of metrology is an emerging research field, this paper aims to present a systematic literature review (SLR) based on a bibliographic data collection of 160 scientific articles retrieved from the Web of Science and Scopus databases over the 2016–2022 time frame. The findings presented in this review and recommendations for building a research agenda can help policy makers, researchers and practitioners by providing directions for the evolution of digital metrology and its role in the digitalization of the economy and society.