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Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI) has seen accelerated growth since the beginning of the 21st century. Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research and development in manufacturing. This paper highlights applications of AI in manufacturing, ranging from production system design and planning to process modeling, optimization, quality assurance, maintenance, automated assembly and disassembly. In addition, the paper presents an overview of representative manufacturing problems and matching AI solutions, and a perspective of future research to leverage AI towards the realization of smart manufacturing. [ABSTRACT FROM AUTHOR]

• Introduced SWM 4.0 by considering Industry 4.0 technologies in waste management. • Reviewed I4.0 role in waste types, processes, and strategies of waste management. • Developed SWM 4.0 framework through smart factories, cities, businesses, and people. Smart Waste Management (SWM) discusses the waste management process for different types of waste while introducing an intelligent approach to controlling the amount of waste. This paper introduces SWM4.0, which applies Industry4.0 (I4.0) technologies in various related events. First, the paper presents a systematic literature review on the role of I4.0 technologies in SWM activities regarding waste types, waste management processes, and 5R strategies. Then, existing solutions supporting SWM4.0 are extracted to develop a framework for exploring the use of I4.0 technologies. This framework includes sharing the four main pillars that contribute to the success of SWM4.0, namely smart people, smart cities, smart enterprises, and smart factories. Furthermore, this review suggests the possibility of unifying and extending existing solutions and identifying the necessary links and interfaces for researchers. For managerial implications, the framework identifies future strategies to fulfill specific SWM tasks and to foster new technological solutions for future research. [ABSTRACT FROM AUTHOR]

In the current era of Industry 4.0, many new technologies offer manufacturing industries to achieve high productivity. Augmented Reality (AR) is one of the emerging technologies that has been adopted in industries to aid users in acquiring complex skills and carrying out many complicated tasks such product assembly and maintenance. Nevertheless, most AR applications have been developed without clear understanding of how such technology can facilitate improved learnability in terms of knowledge reusability. This paper proposed an enhanced AR-based training system that provides multimodal information with a contextualized information to improve task comprehension and knowledge reusability compared with traditional AR that presents unimodal and decontextualized information. An empirical test was carried out to assess the task performance and the task learnability aspects of this enhanced AR compared to the traditional AR and the paper-based document. The experiment consisted of a training phase where participants carried out an electrical connection task of a sensor followed by a knowledge reuse phase where participants had to wire a second sensor using their previous training. A pre-test quiz was given before the experiment followed by the post-tests phase after the training. Post-tests consist of one post-test given directly after the experiment (short-term retention test) and a second post-test quiz given one week later (long-term retention test) to measure information retention. The results indicated that AR-based approaches could enhance knowledge acquisition by around 18 % for traditional AR and almost 25 % for enhanced AR as compared to paper-based approach. While all training systems achieved relatively equivalent well for short-term retention test, trainees who used the enhanced AR training systems statistically outperformed those in the paper-based group for long term retention test. Furthermore, there was a positive correlation between the score of short-term retention test and the score in the knowledge reusability which was also shown by the higher scores in knowledge reusability for the enhanced AR training system compared to the other two approaches. These findings are discussed in relation to the Industry 5.0′s human centric core value. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and the Russian-Ukrainian war revealed vulnerabilities in supply chains, emphasizing the need for resilience in multiple industries. While Industry 4.0 is valuable, it cannot fully address complex supply chain challenges. Therefore, exploring additional drivers like Operations Management has become imperative. More clearly, the adoption of Operations Management tools and practices helps implement and use enabling technologies more effectively. In this context, the aim of the paper is to conduct a literature review to study supply chain resilience considering these two concepts. In other words, the objective of the work consists in examining the effectiveness of the mentioned combination. The paper presents a conceptual framework for enhancing supply chain resilience by exploring the potential of Industry 4.0 and Operations Management. The paper also underlines the positive impact issued from hybridizing both solutions to achieve resilience. As it discusses enhancing features of each concept aside the other. The paper provides a basic model for researchers to further detailed studies, and for managers to bringing the proposed framework from theory to practice. [ABSTRACT FROM AUTHOR]

To satisfy the rising demand for higher product quality and giga-casting requirements, the casting process is undergoing significant changes. However, current control methods rely significantly on human expertise and experience, making process availability and stability difficult to ensure. The semisolid casting process is more complicated than conventional liquid casting due to the additional casting parameters incorporated during the slurry preparation, which can have an effect on the quality of the final product. Therefore, an efficient tool is required to simplify the complete process of semisolid casting. The introduction of an AI system to aid in the supervision of the casting manufacturing procedure is one potential solution. This paper introduces a new casting system named "Smart-Cast" developed for this specific purpose. The paper describes the functions of the system and its current development process. Using an AI system as an assistant can help to achieve the goal of enhancing the efficacy of casting process control, and it can also help foundries step into the Industry 4.0 era. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of Artificial Intelligence (AI), its application in industrial settings, particularly for anomaly detection in time series data, poses unique challenges. Current methods often lack a comprehensive understanding of the trade-offs involved in achieving optimal model performance, data preparation effort, and prediction quality. To bridge this gap, this paper presents an adaptive approach to address these challenges, focusing on making conscious decisions about mentioned trade-offs. Inspired by the principles of the Iron Triangle from product engineering, our methodology introduces a novel "AI triangle" with dimensions of Speed, Effort, and Quality. We applied this methodology to a real-world case study involving anomaly detection in a constrained hardware environment in the context of a forming production process. The results highlight the effectiveness of our approach in achieving a practical balance between speed, effort, and quality constraints for implementing AI in an industrial setting. This paper provides valuable insights into the considerations and trade-offs necessary for the successful deployment of AI in manufacturing and other similar industrial applications. [ABSTRACT FROM AUTHOR]

Numerous research has for long studied the challenges and opportunities within digitalization of Small and Medium-sized Enterprises (SMEs). While SMEs are financially limited compared to larger organization, previous research has succeeded in identifying potential focus areas that allow SMEs to participate in the industrial progression. Among these, pragmatic research on exploitation of existing resources have been highlighted. Therefore, this paper intends to report the findings of a single case study in a Danish SME, where the objective is to understand how technological sub-factors influence the prototyping of digital initiatives for advancing maintenance activities. The study begins with mapping the maintenance activities, along with the IT infrastructure. From here, relevant data is analyzed to understand their shortcomings, where the most dominant relates to how technicians report service orders. The paper concludes by identifying two sets of three technological sub-factors that strongly influence the digitalization of the case company's maintenance activities. [ABSTRACT FROM AUTHOR]

In the sphere of Engineer-to-Order (ETO) manufacturing, the demand for highly customized and rapidly adaptable solutions is escalating, particularly in the context of Industry 4.0 and the burgeoning focus on hyper-customization in Industry 5.0. Traditional, rigid simulation structures often fall short in adapting to these dynamic requirements, creating a need for more flexible simulation frameworks. This paper introduces a novel auto-generative Discrete Event Simulation (DES) system specifically designed to address the unique challenges of ETO manufacturing. Unlike traditional rigid simulation architectures, the proposed system offers substantial flexibility and modularity, significantly easing the development effort. Developed in MATLAB and Simulink, this paper not only presents the foundational logic and architecture of the system but also provides a practical implementation guide. By aligning with the imperatives of Industry 4.0 and 5.0, this research fills a significant gap in the literature and offers a scalable, adaptable simulation-based Decision Support System (DSS) for ETO manufacturing. [ABSTRACT FROM AUTHOR]

This paper delves into the transformative realm of Industry 4.0, emphasizing the pivotal role of Human-Robot Collaboration (HRC) in redefining manufacturing landscapes. Central to this revolution is the integration of cyber-physical systems, automation, and the Internet of Things (IoT), culminating in the advent of smart manufacturing. We explore how HRC fosters a synergistic work environment, blurring the traditional boundaries between human workers and robotic counterparts. This research emphasizes the importance of intelligent task allocation in optimizing operational efficiency while safeguarding the ergonomic well-being of human operators. A simulation-based approach is presented, focusing on task allocation within HRC contexts to enhance both human operators' productivity and fatigue levels. The study provides an analysis of fatigue dynamics and its repercussions on industrial settings. The paper outlines various collaborative scenarios, including sole human operation, robot support, a Flexible Workstation (F-WS) support, and combined robot-FWS support, evaluated through a case study of an assembly station. The results underscore the tangible benefits of integrating robotic and F-WS support, demonstrating a balanced improvement in processing speed, operator efficiency, and a significant reduction in fatigue accumulation. In conclusion, this paper advocates for a balanced, human-centric approach to Industry 4.0, where the integration of HRC and ergonomic considerations pave the way for a sustainable, efficient, and health-preserving industrial future. [ABSTRACT FROM AUTHOR]

Industry 4.0 (I4.0) has emerged as a highly innovative response to market needs, particularly in logistics. This paper presents a structured case study on the implementation of I4.0 technologies for material flow and handling management. The study explores the contextual framework, historical development, and key characteristics of I4.0, focuses on the necessary characteristics for I4.0 certification and outlines the specific features exhibited by the company under study. Objectives include enhanced traceability of metallic materials and optimization of crane management. The paper outlines the project phases, highlighting the positive impact on lead time reduction and providing insights into operational procedures. This research contributes to understanding I4.0 practices in logistics and offers valuable insights for future implementations, driving enhanced efficiency and productivity. [ABSTRACT FROM AUTHOR]

he literature on Lean Manufacturing has predominantly emphasized mass production and batch systems, leaving a considerable knowledge gap regarding the challenges and considerations specific to implementing Lean principles in Engineer-to-Order (ETO) manufacturing contexts. This paper addresses this gap by exploring the interaction between Project Cost Deployment (PCD), a tailored method for ETO environments, and the digital technologies of Industry 4.0. The research aims to identify the synergies between Lean Manufacturing and Industry 4.0, investigating how their integration can enhance productivity, quality, and competitiveness in the manufacturing industry. While Lean Manufacturing is adept at addressing loss reduction and elimination across various industrial contexts, Industry 4.0 leverages digital technologies to revolutionize manufacturing operations and enable more efficient and intelligent production systems. The paper demonstrates the effectiveness of the PCD method through an industrial application involving an international company specializing in customized paper converting machinery and lines. By utilizing PCD in conjunction with Industry 4.0 technologies, analysts were able to uncover hidden losses and quantify them from an economic perspective, while also evaluating the potential impacts of improvement activities. The results highlight the significance of synergizing Lean Manufacturing and Industry 4.0 for ETO environments, offering valuable insights into how digital technologies can be effectively integrated into Lean principles. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (XCT) has been shown to be a reliable tool for quality inspection, material evaluation, and dimensional measurement tasks across diverse academic and industrial applications. In recent years, the integration of machine learning and deep learning techniques have ushered new advances in the industrial computed tomography domain spanning multiple facets, including image reconstruction, segmentation, and feature characterization. This review paper comprehensively surveys the current state-of-the-art machine learning and deep learning applications throughout the entire XCT workflow. Additionally, we explore relevant developments in the medical imaging domain, evaluating their implications for industrial computed tomography. In conclusion, we identify potential future research, drawing insights from existing research gaps in the domain and recent advancements in artificial intelligence. Notably, we underscore the importance of uncertainty quantification and model explainability for further acceptance of artificial intelligence techniques in the domain. [ABSTRACT FROM AUTHOR]

In a society based on data-driven, data inclusion and data access play a significant role in societal development. A called democratization of data through open access, Open Data, must be nurtured by countries to empower their citizens, entrepreneurs, companies, industries, academics, and organizations, in general. Open Data Scoring System is an evaluation system that ranks countries in 22 categories of openness in data, divided into the 3 pillars of sustainability. In this paper, we will present the importance of Industry 4.0 and its relation to sustainability and the role of Data Science in Industry 4.0 assuming an Open Design approach. Then, an analysis is made considering the Gross Domestic Product (GDP) of the most relevant countries worldwide, the USA and China, concerning the six (6) higher ranked categories of openness data of these countries, supported by the Open Data Scoring System from 2015 to 2020. Our findings reveal that in the USA and China the main categories are seven (7), five (5), and 2 (two) categories of economic, social, and environmental sustainability, respectively. Through a correlations and co-occurrences analysis of the open data scoring worldwide reveals that the most significant categories are four (4) economic, one (1) social, and two (2) environmental. [ABSTRACT FROM AUTHOR]

Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing. [ABSTRACT FROM AUTHOR]

Industry 4.0 has brought modernization to the production system through the network integration of the constituent entities which, combined with the evolution of information technology, has enabled an increase in productivity, product quality, optimization of production costs, and product customization to customer needs. Despite the complexity of human thought, artificial intelligence tries to replicate it in algorithms, creating models capable of processing databases with a high volume of information, and generating valuable information for decision making. Within this area, there are subfields, such as Machine Learning and Deep Learning, which, through mathematical models, define patterns to predict output data from known input data. In addition to this type of algorithm, there are metaheuristic models capable of optimizing the parameters required in Machine Learning and Deep Learning algorithms. These intelligent systems have applications in various areas such as industry, construction, health, logistics processes, and maintenance management, among others. This paper focuses on Artificial Intelligence models addressing Industry 4.0 approach. [ABSTRACT FROM AUTHOR]

The rise of Industry 4.0 and, therefore, the integration of Internet of Things (IoT), cloud computing, data processing and analytics into the factories infrastructures has enabled the possibility to automatically collect, transform and process data from manufacturing floors. This, complemented with the use of Process Mining algorithms, allows us to use data extracted from resources activities, either performed by humans or machines, in order to discover, compare and enhance the manufacturing processes of any industry. In this paper we present a resource allocation framework that aims to provide production managers with decision support on the best possible resource allocations for a certain business process. For this, it considers various criteria such as performance, cost, frequency and resource centrality metrics. It takes into account historical data from similar business processes of the organization, and analyses this data with Process Mining techniques, such as process discovery, in order to obtain insights about the business processes, such as patterns and bottlenecks. [ABSTRACT FROM AUTHOR]

The study explores the factors affecting the adoption of emerging technologies in warehouse management. There are limited studies in the literature on factors that affect the adoption of emerging technologies in warehouse management. The advent of Industry 4.0 and the demand for next-day delivery services has grown in unison with the rapid growth of multi-channel transactional volume in warehouse management and supply chain processes. Understanding the factors is important for the adoption of emerging technologies to be beneficial. Therefore, this study aims to fill that gap. The study adopted a systematic literature review to explore the factors that affect the adoption of emerging technologies in warehouse management. The Technological, Organizational, and Environmental (TOE) framework was used to explore the factors in the above-mentioned study. The study used quantitative content analysis to analyse data collected from published articles. The study results suggest that the technological factor (relative advantage), the organisational factor (management), and the environmental factor (market structure) were the most dominant factors affecting the adoption of emerging technologies in warehouse management. The paper contributes to the body of knowledge on factors affecting the adoption of emerging technologies in warehouse management. [ABSTRACT FROM AUTHOR]

The phenomenon of Industry 4.0 is requiring organizations to restructure their roles and upskill and reskill the workforce. In addition, Great Resignation is threatening organizational knowledge and it is essential to promote happier and more engaged workers. Learning Organizations promote learning opportunities to their workforce and furthermore foster the concept of Protean Career Attitude. This paper presents the usability evaluation of a competency management and development plan tool to support, through a technological tool, the Protean Career Attitude. Usability evaluation uses tests and makes use of the Technology Acceptance Model (TAM) scale. The results are promising regarding the acceptance and usability of the technology and show no significant discrepancies when comparing age groups or level of education. However, in some responses it was possible to conclude that the older groups (41-50 and 51-60) are more receptive to technology, despite the slight increase in the time to perform the tasks. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important enabler of the fourth and fifth industrial revolutions. It has the potential to empower intelligent production systems that are central to industry 4.0 and help place worker wellbeing and the planet at the centre of industry 5.0 processes. However, few studies have considered the different roles of AI in industry 4.0 and 5.0. This paper reports on a bibliometric review of 2887 documents examining the use of AI for industry 4.0 and industry 5.0. We found that scholarly activity increased rapidly from 2018 onwards and can be attributed to a large variety of countries, including India, South Africa and China. Our analysis reveals four clusters in the literature, with two showing particular promise for researchers notably around the themes of industry 5.0 and deep learning. Further insights are presented and discussed, followed by a future research agenda. [ABSTRACT FROM AUTHOR]

Natural Language Processing is the field of Computer Science that focuses on analyzing and processing natural language, mainly human text or speech. Recent trends in Natural Language Processing have led to the development of Large Language Models (LLMs): huge models trained on high amounts of data that achieve unprecedented performances in many tasks, such as answering questions, summarizing texts, or coding. These new tools have a wide range of applications and are being developed by many companies. However, Small and Medium Enterprises (SMEs) struggle to implement these new technologies, mainly because of the lack of resources. This paper aims to show the opportunities and challenges related to NLP-based solutions in SMEs based on a literature review. The main result is that NLP-based solutions have a wide range of applications in various companies, including SMEs, and may lead to many changes. However, there are still many obstacles to developing these tools in SMEs: SMEs lack specialized know-how to develop these solutions and do not often have standardized data. Moreover, there exists nearly no support for SMEs in the scientific literature to develop these tools. [ABSTRACT FROM AUTHOR]

Organizations struggle to generate more value through embracing digital transformation but are often confronted with lack of skills in this growing area. For instance, managing (digital) transformation projects requires appropriate skills to holistically plan and implement transformations. This paper outlines a set of skills that can be acquired through training programs of digital transformation projects managers. These skills are built by a working group in academia based on defined persona and then evaluated by practitioners through interviews. The outcome of the interviews indicates a general acceptance of these skills and uncovered some improvement avenues. The results of this work are expected to pave the ground for structuring training programs and building teaching content meeting actual learners and business needs. [ABSTRACT FROM AUTHOR]

Ensuring precise measurement is critical for manufacturing industries; and a coordinate measuring machine (CMM) is widely used for automated inspection of critical and complex components. But there is a lack of clarity regarding optimal timepoint for CMM probe re-calibration. Too frequent calibrations increase the cost and an uncalibrated CMM probe affects the inspection quality. When to get a CMM probe calibrated is still an open question. It is important to accurately determine the optimal probe re-calibration intervals to mitigate the adverse effects on the quality and the time. This paper presents a novel data-driven approach to predict the optimal timepoint to re-calibrate a CMM probe. The data is collected at various levels to develop, train, and validate the predictive model, which includes numerical and categorical data-frame such as probe usage metrics, probe calibration environmental condition, probe specifications, workpiece data, probe calibrated dimension data and calibration requirement. The research potentially aims to reduce the CMM downtime without compromising the product quality variations that arise from the anomalies/errors related to the probe re-calibration thereby enhancing the inspection efficiency as well as sustainability. [ABSTRACT FROM AUTHOR]

Environmental degradation and stringent business regulations require wider sustainable manufacturing practices by bringing the waste/unused resources back into mainstream production. Implementation of collaborative manufacturing – industrial symbiosis – approach is recognized as an effective strategy in the globalized industry 4.0 environment. Industry 4.0 technologies with intertwined supply chain add to the complexity but offer the possibility of breaking the geographical boundaries as the current industrial symbiosis is heavily location dependent. The paper proposes an industrial symbiosis framework based on Industry 4.0 enabling technologies and scalable to global level. A three-level framework is proposed showing the corresponding industry 4.0 technologies at each level. The proposed symbiosis framework and further development of Industry 4.0 technologies-based architecture shows the capability of using the technologies for wider collaboration and faster achievement of sustainable goals. The horizontal integration capability of industrial symbiosis is inferred from the literature. The current industrial understanding and the level of implementation maturity is comprehended from the analysis of digitalization tools and platforms discussed for industrial symbiosis. The work carries forward the industry 4.0 research and its further extension to fifth generation with human resource knowledge sharing at its core. [ABSTRACT FROM AUTHOR]

Data-driven Digital Twin approaches have gained popularity in research literature, particularly in relation to Industry 4.0 achievements. Data-driven Digital Twins process widely available real-time data in an automated manner to extract models from various systems of interest. However, these model extraction approaches commonly lack systematic integration of expert knowledge from human experts, such as engineers. Combining valuable expert knowledge with data-driven model extraction approaches is a highly complex task that can significantly contribute to more accurate models within shorter time periods. In this paper, we provide a comprehensive overview of the research done on fusion of data and expert knowledge for Digital Twins to identify and describe the current gap. Resulting from our findings, we propose an initial framework for integrating expert knowledge and data for Digital Twin model extraction. Subsequently, we provide an overview of the main challenges and opportunities in fusing data and expert knowledge in the context of Digital Twins. [ABSTRACT FROM AUTHOR]

The rapid expansion of industrialization and the increase in energy demand make energy resource management necessary in various industries. Creating digital twins from available streaming data offers a practical way to enhance the efficiency of manufacturing systems. Digital twins allow for detailed analysis of key metrics, such as the energy consumption of complete manufacturing systems. Utilizing digital twins can greatly assist in improving manufacturing processes for reduced energy consumption. In this paper, we investigate the data requirements for the generation of energy-oriented digital twins of manufacturing systems. For this purpose, we developed a case study to illustrate and outline the necessary data requirements for a manufacturing system to enable the collection of data that can be used to extract energy-oriented simulation models. We, subsequently, analyze and generalize our findings that can be applied to a broader context. [ABSTRACT FROM AUTHOR]

In the rapidly evolving domain of Industry 4.0, effective management of software updates is crucial for maintaining system continuity and security. This paper presents a novel machine learning-based approach for a prompt and effective triage of software updates, leveraging an evaluation of six release note classifiers to categorize updates by component type, release type, and security risk. Our methodology, tested on a dataset of 1,000 release notes commonly encountered in Industry 4.0 ecosystems, demonstrates Logistic Regression as the most accurate classifier. The findings not only highlight the practical applicability of our approach in real-world data but also set the foundation for future enhancements to streamline the machine learning triage process further. [ABSTRACT FROM AUTHOR]

Existing wireless-inertial fusion positioning mechanisms predominantly rely on empirical propagation models of wireless signals and are often fused by filtering algorithms such as the Kalman filter or particle filter. Despite their wide usage, these empirical models, especially in relation to system and noise, often exhibit lower accuracy in practical positioning scenarios. Recently, deep learning has been widely studied to build a more robust localization system along with filter fusion, which still needs empirical noise coefficients. To mitigate these constraints, this paper puts forward a novel fusion positioning system based on an end-to-end network, namely, 'SensNet'. The proposed approach deviates from the conventional reliance on empirical models and provides an innovative solution for positioning. The validity of SensNet is underscored by a comprehensive evaluation using the fusion of Bluetooth Low Energy and Inertial Measurement Unit (BLE-IMU) positioning in an indoor scenario. The results evidence an impressive mean positioning error for the fusion network of merely 0.415m, thereby highlighting the potential of SensNet as a reliable solution for Industry 4.0 positioning systems. [ABSTRACT FROM AUTHOR]

The automation and control of industrial machines and processes is a task traditionally performed by PLCs and SCADA systems. However, with the advent of Industry 4.0 paradigm, which merges OT and IT domains, this task can also be performed inside the IT domain. This paper proposes an edge architecture where a flexible manufacturing line is led by a state machine programmed and running on the IT domain inside the Manufacturing Execution System. Each industrial asset has been modelled as a black-box, where security and hard real-time functionalities are programmed and controlled inside each black-box by traditional OT tools. Functionalities and variables required to synchronize each black-box, have been modelled as inputs and outputs. These input and output variables and methods are available using Industry 4.0 industrial communication protocols, such as OPC UA or MQTT. Then, a state machine programmed on a high-level IT language model inside the MES, automates the manufacturing line reacting to the inputs and outputs of each black-box. This architecture has been successfully validated automating a manufacturing line composed by a conveyor, a robot and a vision based quality control system. [ABSTRACT FROM AUTHOR]

Digital twins and virtual reality are pivotal technologies in the context of Industry 4.0, facilitating the design, simulation, optimization, and remote interaction with production systems. These technologies also present new prospects for developing immersive and hyper-realistic digital factories within the metaverse. This paper aims to enhance collaboration and communication in a 3D virtual world, focusing on shared reality and the metaverse's integration in digital factories. Our main contribution enables users to have full-body immersive interaction with virtual 3D assets and realistic locomotion in a 3D environment, fostering flexibility in collaboration and environment monitoring/control. To achieve this, we propose a systematic methodology for designing and implementing a digital twin-based factory with an IoT infrastructure. By replicating sensors and actuators in digital twins, real-time asset management synchronized with the physical factory is realized, empowering full-body interaction. To enable full-body interaction with virtual equipment, we employ a human motion tracking system. A proof-of-concept case study, developed using Unity3D game engine, Solace PubSub+ event streaming APIs, and Awinda Xsens wearable inertial sensors as the motion tracking system, validates our proposed methodology. The results of the case study demonstrate the successful integration of digital twins, virtual reality, and the metaverse, enabling real-time monitoring and control, full-body interaction, and immersive user experiences in the virtual environment. [ABSTRACT FROM AUTHOR]

With the evolution of Industry 4.0, the most advanced technologies have been invented and due to rapid globalisation, supply chains (SC) have become vulnerable to various risks. Risk management performs a key role in operating SC effectively in variety of uncertain circumstances. This paper aims to reveal the adaptability of digital technologies in supply chain networks, identify and analyse the various emerging supply chain risks in the industry 4.0 environment, and develop a framework to mitigate the risk level. A systematic literature review was conducted to identify and analyse the emerging supply chain risks, and Analytical Hierarchy Process (AHP) method was used to develop a conceptual risk mitigation framework. The contribution of this study lies in the taxonomy study, and findings revealed that digitalisation of supply chain design for risk mitigation shed light on future research. Additionally, it focuses on the potential to enhance supply networks' efficiency and responsiveness. [ABSTRACT FROM AUTHOR]

Industry 4.0 has initiated a data-driven transformation of production systems. With AI applications capitalizing on the surge of data availability, their introduction is reshaping workplaces and altering work tasks and profiles around the world. As AI-driven automation of work proliferates, so does the potential for the substitution of human labor. Rather than replacing human work, the concept of hybrid intelligence seeks to combine human and artificial intelligence with the effect of increasing productivity of the overall work system. As such, the concept may prove useful to support human-friendly automation of work, i.e., automation that supports human well-being and empowerment. This requires a deeper understanding of the projected effects of different automation solutions on human workers. In this context, this paper examines possible effects of AI applications in production systems based on four use-cases of coating and machining processes, thereby focussing on the changes from the perspective of workers and the resulting human-AI interactions. The potential challenges and opportunities of the workplace transformation are discussed, specifically highlighting possible implications for the workforce. [ABSTRACT FROM AUTHOR]

Customer demand and profit potential have pushed companies to offer diverse customization options in their products and move towards mass customization. To move towards mass customization reconfigurable manufacturing systems (RMS) have been prosed as a solution. However, it comes with challenges, including labor, costs, time, and complexity. Introducing intelligence, smartness and state-of-the-art industry 4.0 technologies into reconfigurable manufacturing systems (RMS) can be a solution to these challenges. Combining these concepts can be used to develop an intelligent RMS that can be automatically reconfigured without any human intervention. In this paper, a novel architecture has been proposed that explains all the details from the idealization phase to design and physical implementation. The architecture covers individual elements of the RMS, introduces the technologies that enable intelligence and follows through the steps that add up to the complete functionality of the system. In addition, we propose an intelligent process that automates the reconfiguration phase of our proposal. Based on this architecture and intelligent recon-figuration, a physical demonstration is also presented that shows how the system can be implemented. The constructed system is demonstrated as an intelligent RMS capable of mass customization without human effort. [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive study on the implementation of machine vision-enabled in-process quality inspection systems in machining operations. Our objective is to enable zero-defect manufacturing by maximizing efficiency and effectiveness in production processes. We utilize machine vision technology as a transformative tool, playing a vital role in our comparative analysis which reveals its superiority over traditional in-situ approaches in both ideal and challenging real-world scenarios. Through simulation, we demonstrate how machine vision improves the performance of in-line process systems. We also discuss substantial challenges of implementation, such as managing environmental contamination, optimizing machine coordination, accommodating a range of part sizes, and configuring effective coolant delivery systems. Our in-depth analysis of essential factors includes the robustness of machine vision equipment, operator training for machine vision technology, and cost-benefit analysis of its implementation. The research emphasizes machine vision's crucial role in transforming manufacturing setups and enhancing advanced automation systems. The study underscores the immense potential of machine vision in in-process quality inspection to significantly reduce production costs and time, fostering higher manufacturing sustainability and competitive advantage in the era of Industry 4.0. [ABSTRACT FROM AUTHOR]

Quality constitutes the cutting-edge of contemporary industry, much more sharpened with the emergence of Industry 4.0. Yet, the assurance of delivering high-quality products remains a challenging problem that requires cautious controls and timely actions through the entire phases of the production cycle. To that end, zero-defect manufacturing (ZDM) has given prominence to a comprehensive methodology that ensures quality control and defects eradication at every phase of the procedure, prioritizing the continuous supervision of each distinctive component to satisfy the corresponding quality assurance standards. Continuous supervision and detection of possible flaws are subjective to exploiting high-tech sensors for high-precision monitoring and assessment. Considering the above, adopting active vision technology can further enhance the promising capabilities of ZDM. The paper highlights the active vision's benefits to the ZDM strategy and introduces a practical framework for applying such technology through the different stages. Innovations and challenges that have to be taken into consideration are also discussed. [ABSTRACT FROM AUTHOR]

Warehouse design and planning involve complex decisions on receiving, storage, order picking and shipping products (i.e., stock-keeping units - SKUs) and can affect the performance of entire supply chains. With the advancement of Industry 4.0 and increased data availability, high-computing power, and ample storage capacity, Machine Learning (ML) has become an appealing technology to address warehouse planning challenges such as Storage Location Assignment Problems (SLAP) and Order Picking Problems (OPP) for intelligent warehousing management. This paper presents a state-of-the-art review of ML applied to Warehouse Management Systems (WMS) through the analysis of recent research application articles. A mapping to classify the scientific literature in this new research area, including ML methods, algorithms, data sources and use cases of ML-aided WMS, as well as further research perspectives and challenges, are introduced. Preliminary results suggest that the possible research areas in ML-WMS are still incipient and need to be further explored. [ABSTRACT FROM AUTHOR]

The advent of Industry 4.0 has revolutionized the manufacturing industry by incorporating interconnectivity, decentralized decision-making, resource optimization, and automation. This transformation has been made possible through the integration of advanced technologies such as Artificial Intelligence, Cyber-Physical Systems, the Internet of Things, Big Data Analytics, and Blockchain Technology. However, while Industry 4.0 has primarily focused on production efficiency and real-time transparency, the need for the transition toward a holistically sustainable environment has become increasingly urgent. As a result, the term Industry 5.0 was devised, merging the concept of Industry 4.0 with sustainability, resilience, and human-centric goals. This paper explores the potential areas of influence these technologies have on the environmental dimension of sustainability. The findings indicate that various Industry 4.0 technologies can contribute to environmental sustainability in manufacturing. These technologies offer opportunities to optimize supply chain performance, improve product traceability, reduce waste, enhance resource management, and minimize overall environmental impact. By understanding the implications of Industry 4.0 for environmental sustainability, researchers, policymakers, and industry practitioners can collaborate to develop strategies that integrate advanced technologies and sustainable practices in the manufacturing sector. The research provides insights into the potential benefits of these technologies for environmental sustainability and calls for further investigation to fully explore their environmental dimensions. [ABSTRACT FROM AUTHOR]

Automated industrial Visual Inspection Systems (VIS) are typically customized for specific applications, limiting their flexibility. They are characterized by a demanding setup, high capital investments, and significant knowledge barriers. In this paper, we propose an alternative architecture for the visual inspection of 3D printed parts or complex assemblies using a robotic arm equipped with hand-eye sensors and controllable lighting system. The core of the proposed Flexible Vision Inspection System (FVIS) is the self-extraction of 3D text annotations from STandard for the Exchange of Product model (STEP) AP242 files. The system self-selects and parametrizes the most suitable inspection algorithm, including lighting settings. Additionally, it autonomously performs self-localization, self-referencing of physical products, and self-planning of robot inspection path based on CAD information. This framework, characterized by self-X, cost-effective, non-invasive, and plug-and-play architecture has the potential to disrupt the business model of vision inspection, enabling an as-a-service solution aligned with the next generation of flexible manufacturing. [ABSTRACT FROM AUTHOR]

With the trends arising with Industry 4.0, there is an urgent need to reinvent the operations of industrial systems. The area of Facility Management (FM) is no exception and has been undergoing an evolution with the introduction of new information management tools. This paper comprises a study on the impact of lean maintenance in FM, supported by a project developed at a company dedicated to the supply of residential hot water solutions. The study was conducted in the Department of Infrastructure Management and aimed to reduce the waste of information in the area of Operations & Maintenance. Particularly in the practical component, the Lean Maintenance methodology was used to define processes, identify inefficiencies, and select LM tools for waste reduction. In terms of results, it was suggested that LM tools and the reconfiguration of the process can improve the process considering its level of automation, with an increase of 33%, its real time capability, which increased 5%, its first pass yield of information, which increased 11% and its media disruption, which decreased its value in 21%. Thus, it was demonstrated that there is an opportunity for enhancing the processes by taking advantage of Industry 4.0. Based on the contributions of this project, it was possible to conclude that the Lean Maintenance approach applied to the Infrastructure Management sector can have a positive impact on improving the information flow related to the work order management process, and in the process of defining preventive maintenance strategies, thus reducing information waste. [ABSTRACT FROM AUTHOR]

Addressing privacy concerns is critical in smart manufacturing where sensitive data is used for machine learning. Data protection is essential to ensure model accuracy while upholding data privacy. Homeomorphic encryption, an algorithm for privacy-preserving machine learning, achieves this by transforming data using a neural network with secret key weights. This process conceals private data while retaining the potential to learn classification models from the anonymized data. This paper introduces a comprehensive quality metric to assess homeomorphic encryption across conflicting criteria: security (regarding private data), machine learning adaptability (tolerance), and efficiency (regarding needed extra resources). Through experiments on various datasets, the metric proves its effectiveness in guiding optimal encryption parameter selection. Our findings highlight homeomorphic encryption's strong overall quality, positioning it as a valuable Industry 4.0 solution. By offering a simpler alternative to fully homomorphic encryption, it effectively addresses privacy concerns and enhances data usability in the context of smart manufacturing. [ABSTRACT FROM AUTHOR]

The transition from Industry 4.0 towards Industry 5.0 marks a paradigm shift, emphasising human-centricity in industrial settings. Industry 5.0 focuses on improving the future role of people in addition to merely technological progress. While "human centricity" gains recognition, ambiguity surrounds its definition and application. The literature lacks clear consensus on the concept and its industrial implications. This paper provides clarity on human-centricity by analysing viewpoints and public opinions based on posts published on LinkedIn in the last five years regarding human-centricity. The analysis involved text mining techniques, including semantic clustering to discover distinct clusters of discussions related to human-centricity and keywords extraction to tag the different clusters. The findings reveal that public opinion predominantly centres on the skills required by future workers, encompassing both hard and soft skills, as well as social themes such as gender equity and workplace comfort. This research underscores the critical relevance of these components in the transition towards Industry 5.0, offering valuable insights for industrial practitioners and researchers alike. [ABSTRACT FROM AUTHOR]

As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide considerable advantages for smart robotized applications in the industry. Workflow efficiency, collective decision enrichment even for executives, as well as a natural, resilient, and sustainable robotized assistance for the workforce are potential advantages. Hence, the Metaverse could consolidate the connection between Industry 4.0 and Industry 5.0. This paper identifies and puts forward potential advantages of the Metaverse for robotized applications and highlights how these advantages support goals pursued by the Industry 4.0 and Industry 5.0 visions. [ABSTRACT FROM AUTHOR]

Industry 4.0 has supported and changed the way companies operate over the last decade. Today, the increasing focus on sustainability in all its pillars is initiating a critical process and revision about the Fourth Industrial Revolution. A new scenario characterized by human-centricity, resilience and social sustainability, named Industry 5.0, is emerging. In this transition process, new and old enabling technologies are appearing, among them the Metaverse. Although its actual functions are not yet fully investigated, from different perspective this tool could adequately support the definition of Industry 5.0 by creating a symbiotic ecosystem between companies, workers and consumers. Against this backdrop, the aim of this paper is to investigate, through a systematic literature review, the role of the Metaverse in the approach to sustainability in order to understand in which way this technology can be useful for the transition. The results may support policy makers, managers and practitioners in understanding the role of Metaverse in sustainability and in the new wave of Industrial Revolution. Limits and future research steps are also discussed. [ABSTRACT FROM AUTHOR]

The pressure of the global sustainability problem is growing faster than ever after the social and economic havoc wreaked by the pandemic, as well as the present time nearing to the 2050 net zero carbon goal. In 2021 the steel industry had a turnover of around €125 billion and was directly responsible for providing at least 300000 jobs. In 2022, it was responsible for roughly 5% of CO 2 emissions in the EU and 7% globally, and was responsible for at least 20278 injuries and 90 deaths. Environmental solutions for mitigating negative consequences of the steel industry exist such as hydrogen-based steelmaking, carbon capture, utilisation and storage and electrolysis, whilst safety regulations and personal protective equipment have been used to improve safety. However, little has been done to combat the problem using the most powerful emerging technology of the fourth industrial revolution, artificial intelligence. In particular, computer vision has already shown great competence in a range of applications related to steelmaking, but without seriously considering sustainability, resulting in limited awareness of the potential benefits computer vision can bring to industry. A lack of this awareness leads to missed opportunities for sustainable development. This paper aims to address the gap in research that discusses computer vision capabilities for enhancing the sustainability of steel production by providing a literature review covering recent advances in computer vision, as well as an industry 4.0 approach for integrating computer vision systems with steelworks. Research presented here successfully exposes the untapped potential of computer vision in the steel industry and paves the way for future developments by exhibiting a blueprint for simultaneously elevating sustainability and technological advancement. [ABSTRACT FROM AUTHOR]

The integration of Industry 4.0 and the circular economy has gained significant attention to achieve sustainable and innovative practices in the manufacturing industry. This paper presents a conceptual framework for integrating Industry 4.0 and the circular economy, based on a systematic literature review. The review synthesizes and critically analyzes existing research to identify key elements of a circular and sustainable production system and demonstrates how Industry 4.0 technologies can be leveraged to support these elements. The framework encompasses four essential components: Desired Environmental Performance, Circular Economy Strategies, Technology Enablers, and Organizational Factors. These components provide guidance for manufacturing firms in adopting Industry 4.0 technologies to promote circular economy principles and achieve environmental sustainability. The findings of this study contribute to a comprehensive understanding of the interactions between Industry 4.0 and the circular economy, paving the way for future research and practical implementation in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Manufacturing processes of the future will rely on standards for asset interoperability and service orchestration. The Asset Administration Shell (AAS) facilitates information exchange among Industry 4.0 assets, while standardized Business Processes enable workflow execution in manufacturing systems. Combining these technologies provides agility and scalability to manufacturing systems by incorporating asset services within business processes. Service orchestration involves coordinating multiple services, which must be dynamic during runtime to manage unforeseen situations that may arise during the manufacturing process. Context information plays a crucial role in identifying such scenarios and selecting the most suitable devices/services in response, and the Semantic Web accurately represents this information. This paper proposes a context-aware approach for service orchestration using industrial asset services. Our contributions include (1) a component for Context-Aware Service Re-Selection. (2) a domain-specific ontology (DeviceServiceOnt) for Semantic Web-based context representation. And, (3) validation of our proposal in a manufacturing setting where robots are responsible for dispatching and distributing materials within a warehouse. Opportunities for future work are also highlighted, with a primary focus on enhancing workflow dynamicity with context-aware capabilities. [ABSTRACT FROM AUTHOR]

Industry 4.0 uses artificial intelligence and machine learning algorithms to optimize processes. The main goal of the manuscript is to analyze the possibilities of applying automated machine learning to predict company bankruptcy. The data sample consists of financial data of 9,771 manufacturing companies for the years 2020 and 2021 collected from the Finstat database. Two methods of automated machine learning, AutoML and H2O, were tested. The results were compared with five other methods - linear discriminant analysis, logistic regression, naive Bayes classifier, CatBoost and XGBoost. The resulting model was cross-validated through the 10-fold approach. The best results were achieved by H2O automated machine learning algorithm with an AUC of 90.13%, followed by the gradient boosting methods CatBoost with AUC of 90.05% and XGBoost (AUC of 88.61%) and another automated learning algorithm AutoML with AUC of 81.17%. The findings of this paper indicate possibilities to apply automated machine learning methods in predicting bankruptcy. However, it is necessary to distinguish between individual automated machine learning algorithms since they provide a different range of results. [ABSTRACT FROM AUTHOR]

A neural network (NN) is known to be an efficient and learnable tool supporting decision-making processes particularly in Industry 4.0. The majority of NNs are data-driven and, therefore, depend on training data quantity and quality. The current trend in enhancing data-driven models with knowledge-based models promises to enable effective NNs with less data. So-called physics-informed NNs use additional knowledge from computational science to improve NN training. Quite much of the knowledge is available as logical constraints from domain ontologies, and NNs may benefit from using it. In this paper, we study the concept of Taxonomy-Informed NN (TINN), which combines data-driven training of NNs with ontological knowledge. We study different patterns of NN training with additional knowledge on class-subclass hierarchies and instance-class relationships with potential for federated learning. Our experiments show that additional knowledge, which influences TINNs' training process through the loss function at backpropagation, improves the quality of trained models. [ABSTRACT FROM AUTHOR]

Industry 4.0 is demanding reskilling of the workforce and, at the same time, the phenomenon of Great Resignation characterized by a wave of layoffs is threatening organizational knowledge. It is essential to build a culture where employees are engaged, more committed, and where the sharing and generation of knowledge is promoted. This paper presents a collaborative platform (co-created with 3 multi-sector organizations- 2 multinationals) to support the establishment of a Learning Organization to face the challenges of human resources in the digital paradigm. This tool integrates 5 modules: discussion forum, event creation, mentoring, suggestion system and recognition system. The 5 modules act in a combined way to achieve features that offer participation in decision making, a climate of learning opportunities, through the sharing and rise of knowledge (Learning Organization) and that encourage peer relationships and recognition at the corporate level. [ABSTRACT FROM AUTHOR]

The need for a shipyard to improve the quality of its final products is crucial to be competitive in the shipbuilding market. In particular, in the latest years the attention has moved to reducing the presence and occurrence of dimensional non-conformities throughout the entire construction process, in order to facilitate and speed up subsequent phases and ship blocks assembly. This necessity has led shipyards management to evaluate the introduction of dimensional detection systems for the manufactured articles. As an innovative solution, laser scanning technologies have been already tested in extensive ways at industrial level and have recently found application in the shipbuilding sector. Specifically, among the various available technologies, laser scanner systems and laser trackers proved to be the most suitable ones for the targeted purposes. Within the paper, the authors firstly describe the evolution of shipbuilding process and highlight its peculiarities. Then, they focuse on the quality assessment process with a specific reference to both dimensional controls and laser scanning technologies that may support such operation. The application of the latest-generation tools is validated through the presentation and discussion of a real case study related to a ship block. Finally, the authors present the potential future developments and integration of laser scanning technologies within Industry 4.0 framework. [ABSTRACT FROM AUTHOR]