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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]

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]

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]

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]

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]

A cyber-physical system (CPS) is an interconnected physical system that can be monitored, controlled, and operated remotely. A systems-based analysis of CPS is critical for enhancing operational safety and security. Such analysis is particularly important in light of the evolving Fourth Industrial Revolution (Industry 4.0) which is largely driven by CPS, Internet of Things (IoT), algorithmic systems, automation, and smart manufacturing. As a result of industries' and societies' growing dependence on CPSs, their safe and secure applications need to be scrutinized to avoid any threats or harm to processes and human lives. In order to achieve this target, the current work analyzes the literature to investigate challenges and opportunities present in the CPSs. The analysis attempts to answer the following Research Questions (RQs): (RQ1) What are the prominent trends in the field of safety and security of CPSs? (RQ2) What are the opportunities in Industry 4.0 technologies related to safety? (RQ3) What characteristics of CPSs are likely to cause safety and security issues? (RQ4) What are the emerging safety and security issues introduced by CPSs? (RQ5) How were these issues addressed in the literature? CPSs can address some traditional safety issues due to characteristics like high efficiency and control. However, a CPS is challenged by heterogeneity, dependencies, complexity, unattended nature, increased machine intelligence, autonomous reconfiguration, and uncertainties. Safety and security challenges include opaque system failure, complex socio-technical system, human-machine interface, cyber-physical attacks, unsecured remote configuration, lack of standards, and resilience. The paper is structured under the following key headings: bibliometric results, opportunities from Industry 4.0 technologies, issues introduced by Industry 4.0 technologies, literature proposed solutions, and research challenges. Based on the assessment of about 900 relevant publications extracted from two databases (Web of Science (WoS) Core Collection and Engineering village (EV)), the documents were categorized into eight major divisions: (i) Safety Opportunities, (ii) Hazardous Characteristics, (iii) Fault Occurrence, (iv) Cyber-physical attacks, (v) Human factors, (vi) occupational Health and safety, (vii) standards, and (viii) Reliability and resiliency. Proactive strategies were classified under four options: identification, evaluation, prevention, and management. The paper also raises some research needs and unanswered questions that require further analysis to address how security and safety mechanisms can introduce more complexity in the system that can lead to new risks. The paper concludes by emphasizing digital process safety as part of the engineering curriculum to address the process industry's need for digital solutions and to make process safety learning a conscious choice. [ABSTRACT FROM AUTHOR]

Meat, as protein-rich food, provides essential nutrition to humans. Meat consumption is expected to increase by 15% over the next decade, requiring the meat industry to boost its capacity. However, the meat cutting industry's harsh working environment and long-term heavy workload result in a labor shortage. Low-margin and low capacity require innovative processing methods. Hence, some novel automation methods are urgently demanded to meet these challenges. Accompanied by the development of Industry 4.0, robotization and intelligent systems are progressively applied in the meat cutting industry, which can provide a flexible, adaptive, and sustainable processing approach. The paper selected 50 papers using the systematic literature review method. This paper provides an overview of the current research and commercial applications of cutting robotization from livestock, poultry, and fish in the meat industry. Additionally, intelligent sensing technologies, advanced cutting techniques, and a novel manufacturing concept called the meat factory cell, are emphasized to promote efficiency, scalability, and modularization. Finally, we discuss the potential applications of digital technologies in the meat cutting industry and the focal points of future research aiming to promote cutting robotization. The meat cutting process can be highly robotized by integrating dexterous cutting robots, advanced sensing techniques, and digital systems, driving the transformation from manual labor to robotic, efficient, and intelligent manufacturing. Consequently, robotization and intelligent digital systems can provide a brand-new manufacturing method to the meat cutting industry and advance the Meat factory 4.0. [Display omitted] • Advanced perception techniques assist robots to perform adaptive cutting. • Intelligent robotic systems in the meat cutting industry are reviewed. • Commercial applications regarding meat cutting robotization are presented. • Advanced cutting techniques are highlighted to promote cutting performance. • Potential applications of advanced digital systems are provided for sustainability. [ABSTRACT FROM AUTHOR]

Industry has entered a new age of industrial change which is revolutionising how products are manufactured. Driving this industry change are newly developing digital technologies such as robotics, artificial intelligence, advanced analytics and the industrial internet. This convergence of technology and manufacturing is driving the digital transformation of every industrial segment from operations to logistics, and from aeronautics to retail product manufacture. The technology advances evident from Industry 4.0 will result in industry disruption and build competitive advantages for the organisations who can master the technologies in their manufacturing processes. However, organisations are struggling to develop a tactical methodology to introduce these technologies while simultaneously transforming their organisation cultures and organisations and maximising the benefits. One of the key reasons for the Fourth Industrial Revolution called Industry 4.0 is the need to strengthen the competitiveness of Western European economies, which as a result of the progressing globalization process and rising labor and business costs [1]. A key benefit of the introduction of Industry 4.0 initiatives in a manufacturing plant is to help overcome current operations management limitations, such as, the lack of knowledge on how the process is performing at a specific point in time. This paper will investigate the impact of deploying a range of Industry 4.0 technologies to access machine and operations performance data, to improve equipment reliability and to reduce the costs associated with the maintenance of equipment. With the introduction of the operational data analytics as a result of data extraction from manufacturing equipment, along with integrating other data sources, the business has realised improvements in key metrics like OTIF (On Time in Full), OEE (Overall Equipment Effectiveness), MTBF (Mean Time Between Failure), MTTR (Mean Time to Return), CuC (Consumable Unit Cost), and Lead Time. This paper discusses this project and the results obtained at Zimmer Biomet while also discussing the research carried out for ZOML to begin its journey on manufacturing digitalisation. The paper starts with a background in Industry 4.0. The next section provides a background on the company where the problem statement and project have come from with some detail on their digitalisation journey. The following section details the problem and the project that the research is derived from and the final section is in relation to the results of the research and the project. [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]

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]

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]

In an era where digital transformation (DT) reshapes industries, the role of Knowledge Transfer Partnerships (KTP) in bridging academic insights with industrial innovation becomes crucial. This study conducts a comprehensive scientometric analysis of 360 academic papers spanning from 2013 to 2023 to map the evolving landscape of KTP in the context of DT. By employing advanced visualization tools including generative visual networks and keyword co-occurrence analysis through CiteSpace, critical research gaps and trends, particularly addressing how KTP can mitigate technological obsolescence and enhance innovation management within enterprises are identified. The findings reveal a significant escalation in research output related to KTP, with a pronounced focus on integrating cutting-edge technologies of Artificial Intelligence, machine learning and virtual reality and fostering market adaptability. This study charts the exponential growth of literature and highlights strategic areas of research entities, global geographical coverage, and advanced digital trends where KTPs are pivotal in enhancing organizational resilience and competitive advantage in a rapidly digitizing world. This paper contributes to the existing knowledge of KTP by identifying the patterns and trends of on-going research and offering an evolutionary model to guide theoretical development and practical business operations and policy making. [ABSTRACT FROM AUTHOR]

This comprehensive review paper investigates the possibilities and prospects of operation and maintenance (O&M) technologies in small modular reactors (SMRs). The necessity for innovation within the regulatory restrictions is a primary concern. Artificial intelligence, digital twins, and robotics are emphasized as promising technologies for autonomous operation, real-time monitoring, predictive maintenance, and maintenance automation, respectively. The paper argues that these technologies can improve safety, increase economic efficiency, and enhance the competitiveness of SMRs. In addition, the paper proposes a methodology for performance verification that employs separate effect tests (SETs) and integral effect tests (IETs) facilities. A realistic roadmap is also provided, suggesting a six-year timeframe for the implementation and licensing of these technologies. By combining innovative technologies and regulatory considerations, the study proposes a future direction for SMRs' operation and maintenance. The final objective of this work is to provide insights into the current status of SMRs' O&M technologies, to suggest potential paths forward, and to make SMRs a more competitive choice in the global energy market. The conclusions drawn from this review can serve as a valuable guide for researchers, plant operators, and policymakers in the nuclear energy sector. • There is an economic issue with small modular reactors (SMRs). • Currently, reducing operation and maintenance (O&M) costs is essential. • A comprehensive review has been conducted regarding regulations and technology. • Presents a realistically achievable cost reduction strategy for SMRs. [ABSTRACT FROM AUTHOR]

Data infrastructures are expanding rapidly across African societies, renewing the promise of modernisation, and providing a massive data resource to the dominant tech powers of the world. Google's private undersea cable, named Equiano, landed in west Africa with the intention of igniting data services, smart environments, investment opportunities and jobs. Non-governmental and multilateral agencies are busy supporting African governments in building regulatory frameworks that aim to 'ready' countries for the 4th industrial revolution. Young Africans labour in remodelled shipping containers to annotate data and train algorithms. While future promises pan out in wide-angled, utopian visions, this paper sets out an approach to understand African contexts as sites of heterogeneous experience of data infrastructures that are historically and politically contingent. The paper explores the spatial politics of extraction, surveillance, and exploitation in three examples of the Equiano pipeline, the Artificial Intelligence (AI) Readiness Index, and the AI annotative labour force. We challenge the homogenising discourses of global tech companies and transnational governance institutions in accounting for the geographical histories of colonialism and its afterlives in African societies. We further call for empirical studies that examine the granular multiplicities of data, providing nuanced understandings of AI in and from Africa. [ABSTRACT FROM AUTHOR]

The animation industry is currently undergoing significant transformations fueled by technological advancements. Emerging technologies such as Artificial Intelligence, Big Data Analytics, Cloud Computing, the Internet of Things, Virtual and Augmented Reality, and Blockchain are reshaping the animation industry's value chain. This paper explores the impact of Industry 4.0 technologies on various stages of the animation industry's value chain, including conceptualization, pre-production, production, post-production, distribution, and consumption. Through a combination of literature review and semi-structured interviews, the paper identifies key capabilities of Industry 4.0 technologies in the animation industry. Subsequently, experts from different countries were engaged to validate and evaluate these capabilities, scoring them based on their importance and effectiveness in production and marketing activities. Analysis of data from 60 experts resulted in the identification of the most critical capabilities, which were categorized into four groups based on their relation to either the production or marketing of animation products. Furthermore, the analysis of questionnaires enabled the development of four scenarios depicting the potential future trajectories of animation companies based on their adoption of Industry 4.0 capabilities. The paper delves deeper into each scenario, elucidating their underlying factors and characteristics. Ultimately, it is predicted that companies leveraging Industry 4.0 technologies for both production and marketing activities will emerge as the leaders of the animation industry. This paper offers valuable insights into the evolving landscape of the animation sector and provides a roadmap for companies seeking to harness the potential of Industry 4.0 advancements for sustained leadership and success. • Industry 4.0 can profoundly transform the animation industry through its production-oriented and marketing-oriented capabilities. • Artificial intelligence can generate facial expressions and lip-sync animations based on audio input or text transcripts. • Big Data Analytics provides the opportunity for personalizing animation products. • Cloud Computing provides new ways of delivering products such as streaming animations. • Digital twins enable animators to visualize and iterate on their designs before production begins. [ABSTRACT FROM AUTHOR]

Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems' instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support. [ABSTRACT FROM AUTHOR]

The emergence of Industry 4.0 marks a transformative era for businesses and industries, characterized by advanced technologies like automation, Internet of Things (IoT), artificial intelligence (AI), smart factories, and cyber-physical systems. This revolution promises significant advantages, including enhanced productivity, sustainable progress, and heightened resilience. However, the integration of Industry 4.0 is challenged by the need for a skilled workforce with expertise in areas such as information technology and data analytics. Higher education institutions (HEIs) play a vital role in equipping future professionals with these skills, necessitating curriculum updates and infrastructure enhancements. Simultaneously, the importance of education for sustainable development (ESD) has been underscored by global initiatives like the Sustainable Development Goals (SDGs). ESD instills a sense of responsibility for economic, ecological, and equitable well-being. As digital technologies blur the lines between industries, education faces the challenge of adapting to evolving demands. The integration of AI tools in education has emerged as a catalyst for reshaping learning experiences, fostering innovation, and preparing individuals for the digital age. AI chatbots such as ChatGPT have garnered widespread attention and possess the potential to revolutionize various aspects of education. However, their integration raises ethical concerns, necessitates curriculum redesign, requires strategies for continuous learning, and demands alignment with industry standards. While the potential of AI integration in education is promising, there is a notable gap in the existing literature when it comes to exploring the ethical implications, the influence of AI on ESD, the impact on the structure of Blooms Taxonomy, collaboration between academia and industry, strategies for continuous learning, and the effective integration of AI tools for personalized learning. This paper aims to critically examine the integration of AI tools, with a specific emphasis on ChatGPT, in education within the context of ESD. It delves into the transformative potential, ethical considerations, imperatives for continuous learning, and the role of industry partnerships. By providing insights and strategies, this paper contributes to the ongoing discussion about the evolving nature of education in a technologically driven world, equipping academic institutions to navigate the complexities and opportunities associated with AI integration in education more effectively. [ABSTRACT FROM AUTHOR]

The primary purpose of the article is to identify strategic risks from two points of view: a) general, which fundaments are the nine pillars of Industry 4.0, and b) specific - which is the context of AI. This analysis is additionally extended to include the category of scalability, which refers to the system's ability to accommodate an increasing number of elements. The evaluation of risks should be followed by the identification of their types, as well as the risk catalog, which was also presented in the paper. [ABSTRACT FROM AUTHOR]

Rapid pace of technological growth led to having more complex product/service-systems (PSS) specially when operating in a large-scale and dynamic environment. A PSS dynamically interacts with society and technological enablers, which together form a socio-technical System of Systems (SoS). However, effectiveness of technologies and development approaches is mostly analyzed without considering the possible rebound effect at SoS level happening due to interactions between systems. This paper aims to propose a vision and a structured approach to assess the environmental impacts due to application of new technology/policy at SoS level by orchestrating various methods to model/simulate the complex structure of a PSS for optimization of its lifecycle parameters. This can support decision- making and mitigate the risk that the movements toward supporting sustainable development have opposite outcomes by putting extra environmental burden on other systems. [ABSTRACT FROM AUTHOR]

The advent of Industry 4.0 and propelled the application of Artificial Intelligence in different industrial fields and contexts, such as predictive maintenance (PdM). Through its ability to assess the condition of equipment to detect signs of failure and anticipate them, PdM brings several potential benefits in terms of reliability, safety and maintenance costs among many other benefits. Different approaches are proposed in the literature. They are based on data, physic models or knowledge but several problems and limits persist, in particular, to override this dependence on a particular context, to utilize data and business knowledge considering the challenges of applying existing solutions to another context, difficulties associated with data analysis, and uncertainty management. In this context, the goal of this paper is also to highlight the challenges faced in the area of PdM, both for implementation and use-case. PdM remains a hot topic in the context of Industry 4.0 but with several challenges to be better investigated in the area of machine learning, knowledge representation and semantic reasoning applications. [ABSTRACT FROM AUTHOR]

For the last few years, one can see the emergence of a large number of intelligent products and services, their commercial availability and the socioeconomic impact, this raises the question if the present emergence of AI is just hype or does it really have the capability of transforming the world. The paper investigates the wide range of implications of artificial intelligence (AI), and delves deeper into both positive and negative impacts on governments, communities, companies, and individuals. This paper investigates the overall impact of AI - from research and innovation to deployment. The paper addresses the influential academic achievements and innovations in the field of AI; their impact on the entrepreneurial activities and thus on the global market. The paper also contributes in investigating factors responsible for the advancement of AI. For the exploration of entrepreneurial activities towards AI, two lists of top 100 AI start-ups are considered. The inferences obtained from the research will provide an improved understanding of the innovations and the impact of AI on businesses and society in general. It will also provide a better understanding of how AI can transform the business operations and thus the global economy. [ABSTRACT FROM AUTHOR]

• A comprehensive investigation of laser welding and forming process as two critical parts of smart manufacturing. • An intelligent Aluminum Laser welded blanks (ALWBs) factory based on industry 4.0. • Sensor based monitoring in forming and welding of Aluminum alloys. • A novel model has been proposed as a roadmap for applying the idea of Industry 4.0 in forming of Aluminum Laser Welded Blanks (ALWB). The aim of this review paper is a comprehensive investigation of the laser welding and forming process as two critical parts of smart manufacturing and welding intelligent systems in Industry 4.0, especially for forming Aluminum Laser Welded Blanks (ALWB). In the modern era, manufacturing companies and their business models have a major impact on economic development and social relationships. Since Industry 4.0 has become a commonly accepted term for research centers and universities, both businesses and researchers are interested in the initiative. On the other side, aluminum has proven more challenging to weld than the other metals accounting for the most part for its tendency to form porosity. In this regard, monitoring using sensor-based approachesarehat centralize the information in real time is gaining ground. In addition, this ensures the accuracy and quality of the final product. There are several critical factors such as laser power, laser speed, focal point, laser scanning, and frequency that play a key role in laser technology which is important to control them toward the zero-defect manufacturing system. On the other hand, robustness accounting for geometrical defects (ex. underfill amount/angle at the toe as well as the number of admissible porosity defects in the weld about formability) are the critical parameters in the forming process. Here, a comprehensive review paper is conducted insight into the function of different sensors to signals from the welding and forming process. Finally, a novel model has been proposed as a roadmap for applying the idea of Industry 4.0 in forming Aluminum Laser Welded Blanks (ALWB). [ABSTRACT FROM AUTHOR]

This study aims to synthesize the extant literature on supply chain (SC) resilience and draw a roadmap for resilient SCs by employing a systematic literature review (SLR). The paper utilizes an SLR methodology to draw upon important themes and sub-themes. This is based on a pool of papers published in peer-reviewed journals. Major themes identified included SC networks and structures, Industry 4.0 and digitalization, innovation and entrepreneurship, capabilities and strategies, SC risk management, circular economy and sustainability. SC networks and structures, as well as the role of information technology and Industry 4.0, are an important theme of research in the literature. The literature at this juncture has mainly highlighted the role of big data analytics, blockchain technology, and artificial intelligence in SC resilience. In addition, the important capabilities highlighted by researchers include SC flexibility, SC capacity, SC robustness, SC relationship management and SC leadership for improving SC resilience. The important themes and sub-themes identified in the study would help identify the strategic fields of action for creating resilient SCs. Particularly, the conceptualization of resilience within the SCs would help to uncover the factors that play an important role in improving the SCs. Further, the study also focuses on the interplay between sustainability and resilience. The present study will help managers to understand the factors that affect resilience in SCs. [ABSTRACT FROM AUTHOR]

Industry 4.0 process fault detection and diagnosis(FDD) is built on the foundations of Industrial Internet of Things(IIoT) for sensing and artificial intelligence for recognizing patterns. Despite the performance and application of various learning-based algorithms in multiple sectors, their black-box nature makes industrial experts skeptical. So, this paper proposes an eXplainable Fault Detection, Diagnosis, and Correction(XFDDC) Framework to create best-fit FDD models that are explainable. The XFDDC framework is designed to explain the FDD model predictions using eXplainable Artificial Intelligence(XAI) techniques. The proposed framework was applied to the bench-marked Tennessee Eastman Process(TEP) dataset. On evaluation, an XGBoost model yielded better Fault Detection Rate(FDR) and F1 score against popular transparent and complex models like Naive Bayes, K-Nearest Neighbors, Random Forest and a rule-based version of XGBoost. To explain the predictions of the XGBoost model, the XFDDC framework suggests the use of feature-based XAI techniques. So, the TreeSHAP algorithm is applied on the XGBoost model to generate local and global explanations as part of fault diagnosis. The proposed framework also recommends counterfactual explanations to provide action recommendations for correcting the fault situation. Thus, a best-fit explainable XGBoost Fault Detection, Diagnosis, and Correction (XGBoost-XFDDC) model is created. [ABSTRACT FROM AUTHOR]

This keynote paper mainly focuses on advancements of machining technology and systems for enhanced performance, increased system integration and augmented machine intelligence, critically hinging on new sensors, sensor systems and sensing methodologies being robust, reconfigurable and intelligent, while providing direct adoption and plug-and-play use in industrial practice. One chief novelty is given by the key enabling technologies of Industry 4.0 where integration of sensing systems in manufacturing plants is a cornerstone for transforming conventional manufacturing concepts into digital manufacturing paradigms. Application examples to industrial processes, future challenges and coming trends in machining monitoring are shown. [ABSTRACT FROM AUTHOR]

Product variety and its induced manufacturing complexity remains to increase and therefore greatens challenges for design of joining elements. Historically, joining element design was a paper-based process with incomplete variety documentation and is digitalized only by replacing paper for 3D space. Currently, joining element design remains an ambiguous manual task with limited automation, resulting in long iterative, error prone development trajectories and costly reworks. Thus, processes in practice conflict with required capabilities. Artificial intelligence helps to solve such conflicts by taking over repetitive tasks, preventing human errors, optimizing designs and enabling designers to focus on their core competencies. This paper proposes a novel artificial intelligence method toolbox as a foundation to automate joining element design in manufacturing industries. The methodology aims to incorporate multiple lifecycle requirements including large product variety. [ABSTRACT FROM AUTHOR]

• Revealing the concept, connotation, and state-of-the-art architecture of HSM. • A comprehensive survey on key enablers and applications of HSM. • Identification of challenges and future research directions towards HSM. • Enhancing the research, implementation, and application of HSM. As businesses started to embrace Industry 4.0, along came the Fifth Industrial Revolution. Industry 5.0 complements the existing Industry 4.0 paradigm for the not-too-distant future by highlighting research and innovation as drivers for a transition to a sustainable, human-centric, and resilient industry. The core values of Industry 5.0 including human-centricity, sustainability, and resilience have prompted formal discussions that manufacturing should be human-centric. Human-centric smart manufacturing (HSM) takes full advantage of human flexibility, machine precision, and new-generation information technologies to construct a super smart, sustainable, and resilient manufacturing system. Nowadays, extensive research on the concept, architecture, enabling technologies, and applications of smart manufacturing have been conducted. However, research on HSM is relatively lacking, yet starting to gain traction rapidly. Hence, this paper primarily carries out a systematic literature review on HSM to identify promising research topics with high potential for further investigations. Firstly, this paper reveals the concept and connotation of HSM, on which the state-of-the-art architecture of HSM is introduced. Then, several promising Industry 4.0 technologies are identified as the key enablers that can continue to support the construction and operation of HSM, where a comprehensive survey and discussion on these technologies are conducted. Thirdly, what's new in HSM applications and how HSM will reshape the entire lifecycle of industrial products are explored. Finally, this paper identifies limitations, barriers, and challenges that will be encountered during the developing, operating, and maintaining HSM, and provides valuable research directions to continuously improve HSM. [ABSTRACT FROM AUTHOR]

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. The majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance is the current trend of smart maintenance, followed by most maintenance engineers. The challenging task in predictive maintenance is to diagnose the type of fault. With Artificial Intelligence (AI) advancement, a data-driven approach for predictive maintenance is taking a new flight towards smart manufacturing. Several researchers have published work related to fault diagnosis in rotating machines, mainly exploring a single type of fault. However, a consolidated review of literature that focuses more on the "multi-fault diagnosis" aspect of rotating machines is lacking. There is a need for a study that would systematically cover all the aspects right from sensor selection, data acquisition, feature extraction, multi-sensor data fusion to the systematic review of AI techniques employed in multiple fault diagnosis. In this regard, this paper attempts to achieve the same by implementing a systematic literature review on a Data-driven approach for multi-fault diagnosis of Industrial Rotating Machines using the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) method. The PRISMA method is a collection of guidelines for the composition and structure of systematic reviews and other meta-analyses. This paper identifies the foundational work done in the field and gives a comparative study of different aspects related to multi-fault diagnosis of industrial rotating machines. The paper also identifies the major challenges, research gap. It gives solutions using recent advancements in AI in implementing multi-fault diagnosis, giving a strong base for future research in this field. [ABSTRACT FROM AUTHOR]

Artificial intelligence is a field of research that is seen as a means of realization regarding digitalization and industry 4.0. It is considered as the critical technology needed to drive the future evolution of manufacturing systems. At the same time, autonomous guided vehicles (AGV) developed as an essential part due to the flexibility they contribute to the whole manufacturing process within manufacturing systems. However, there are still open challenges in the intelligent control of these vehicles on the factory floor. Especially when considering dynamic environments where resources should be controlled in such a way, that they can be adjusted to turbulences efficiently. Therefore, this paper aimed to develop a conceptual framework for addressing a catalog of criteria that considers several machine learning algorithms to find the optimal algorithm for the intelligent control of AGVs. By applying the developed framework, an algorithm is automatically selected that is most suitable for the current operation of the AGV in order to enable efficient control within the factory environment. In future work, this decision-making framework can be transferred to even more scenarios with multiple AGV systems, including internal communication along with AGV fleets. With this study, the automatic selection of the optimal machine learning algorithm for the AGV improves the performance in such a way, that computational power is distributed within a hybrid system linking the AGV and cloud storage in an efficient manner. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Human Computer Interaction (HCI) often intersect under the purview of different technologies in a plethora of application areas, wherein it becomes pivotal to identify and interpret the exact roles of both the disciplines. Stakeholders involved in the fusion of AI and HCI range from engineers, scientists, philosophers and technologists to psychologists, cognitive scientists, graphic designers and researchers across a wide spectrum of realms. While it is important to develop intelligent interactive systems owing to the ubiquitous and pervasive nature of data ushering in the next era of AI, it is all the more important to develop these systems in-tandem with the aforementioned stakeholders, and in a way that users exercise appropriate control over the AI that is been developed. Human-in-the-loop systems and operator assistive technologies, especially in the context of Industry 4.0 are examples of a fabulous fusion of AI-HCI, wherein the technology that is being used works by keeping the humans in the loop, thereby paving the way for a robust, transparent, ethical, intelligent and interactive mechanism for the co-existence of AI and HCI systems. In this paper, we aim to bank upon the state-of-the-art literature that has been written previously in this regard and derive though provoking research questions. We also put forth challenges, case studies, application areas and come up with solutions, along with few recommendations that can be viably used in developing frameworks and mechanisms for the synergic co-existence of these two challenging, yet interesting fields of AI and HCI. [ABSTRACT FROM AUTHOR]

The rapid growth of the Industry 4.0 paradigm is increasing the pressure to develop effective automated monitoring systems. Artificial Intelligence (AI) is a convenient tool to improve the efficiency of industrial processes while reducing errors and waste. In fact, it allows the use of real-time data to increase the effectiveness of monitoring systems, minimize errors, make the production process more sustainable, and save costs. In this paper, a novel automatic monitoring system is proposed in the context of production process of plastic consumables used in analysis laboratories, with the aim to increase the effectiveness of the control process currently performed by a human operator. In particular, we considered the problem of classifying the presence or absence of a transparent anticoagulant substance inside test tubes. Specifically, a hand-designed deep network model is used and compared with some state-of-the-art models for its ability to categorize different images of vials that can be either filled with the anticoagulant or empty. Collected results indicate that the proposed approach is competitive with state-of-the-art models in terms of accuracy. Furthermore, we increased the complexity of the task by training the models on the ability to discriminate not only the presence or absence of the anticoagulant inside the vial, but also the size of the test tube. The analysis performed in the latter scenario confirms the competitiveness of our approach. Moreover, our model is remarkably superior in terms of its generalization ability and requires significantly fewer resources. These results suggest the possibility of successfully implementing such a model in the production process of a plastic consumables company. • Using Artificial Intelligence to increase the efficiency of industrial process. • A novel automated monitoring system to enhance control processes. • Addressing the production control issue of test tubes for analysis laboratories. • The model surpasses pre-trained networks in accuracy and generalization. • The results suggest the feasibility of implementing such a model in practice. [ABSTRACT FROM AUTHOR]

Inspired by the ambitions envisioned in the Fourth Industrial Revolution for aquaculture, also known as Aquaculture 4.0, the aquaculture (marine animal farming) industry is seeking to adopt data-driven Artificial Intelligence (AI) to help significantly improve business operations. One of the major barriers is the manual annotation of animal behaviour data, which is a time-consuming task that demands high levels of concentration from biologists. To address this challenge, this paper proposes novel automatic animal behaviour monitoring tailored for industrial scenarios. Our approach introduces a real-time machine-learning-based instance segmentation system that is specialised for underwater environments, where large groups of shrimp are farmed. The implemented system achieves an accuracy rate of 89% at 30 frames per second (fps) and can accurately detect shrimp in high-density areas under poor lighting conditions and high turbidity waters, despite the challenges of occlusion and overlapping. A key innovation of our method is the implementation of a new density cluster algorithm for time series and video analysis. This approach provides a more efficient and accurate way of monitoring animal behaviour, significantly saving time and effort for biologists and advancing the capabilities of automated aquaculture systems. • Novel AI-system to enhance animal detection accuracy in high-density areas. • Enhanced DBScan algorithm for time series for density-based spatial clustering. • AI model with robustness in handling occlusion, turbidity, and overlapping. [ABSTRACT FROM AUTHOR]

In recent years, advancements in machine learning (ML) have facilitated the utilisation of big data across various academic disciplines. Nonetheless, these techniques still require a high-level of programming and data science expertise, making them inaccessible to many researchers and hindering the potential for knowledge advancements. This paper presents a framework for identifying the adoption of Industry 4.0 (I4.0) technologies among European firms that have undergone restructuring events. Existing studies on I4.0 adoption rely on diverse data sources at different levels of aggregation (e.g., countries, sectors, firms), spanning various time periods and technological domains. While this diversity often complicates result comparison, it also drives researchers and institutions to explore new data sources to assess technology adoption. Our identification methodology is based on the implementation of ML techniques using STATA, a well-established and user-friendly statistical software. We offer a step-by-step guide based on recently developed commands, allowing for comparison of model performance and analysis of model features. Our findings underscore the potential of ML algorithms as a robust tool for collecting new firm-level data on I4.0 adoption. Specifically, we observe that business restructuring events predicted as I4.0-related conform to adoption patterns identified in prior studies, across countries, sectors and over time. • This study presents machine learning models to predict Industry 4.0 adoption. • A methodology is presented to explore the application of data science techniques in social sciences. • The machine learning implementations are based on a set of recent commands for STATA. • Eurofound's data on 23,618 restructuring events were used to predict technology adoption from textual descriptions. • Predicted adoption patterns conform to those observed across European countries, sectors, and over time. [ABSTRACT FROM AUTHOR]

Human motion analysis plays a crucial role in industry 4.0 and, more recently, in industry 5.0 where human-centered applications are becoming increasingly important, demonstrating its potential for enhancing safety, ergonomics and productivity. Considering this opportunity, an increasing number of studies are proposing works on the analysis of human motion in an industrial context, taking advantage of the rise of artificial intelligence technologies and sensor technologies. The objective of this work is to provide a review of recent studies exploring these technologies in the analysis of human movement while specifically considering industrial context. First, a taxonomy of key human motion analysis applications is proposed, presenting statistical insights to reveal trends and highlighting lacks in current research. Furthermore, this work identifies benchmark datasets acquired in various industrial case studies and associated sensors. Many recommendations for selecting optimal sensors and valuable benchmarks are proposed. Then, the paper outlines the current trend of utilizing hybrid deep learning methodologies in human movement analysis while underscoring the performance and limitations of these proposed methods, considering industrial constraints such as real-time recognition and frugality. Finally, challenges and future works are highlighted, focusing on the opportunities to address problems related to the complex industrial environment in order to achieve reliable performances. [ABSTRACT FROM AUTHOR]

AI becomes a key enabler for Industry 4.0. Data / information quality become a real cornerstone on the overall process from user expectation to products / systems / solutions in a consistent perspective in order to ensure quality of the manufacturing production. This paper highlights some key characteristics in terms of information quality required to implement an effective AI based monitoring framework, in order to achieve operational excellence in Industry. [ABSTRACT FROM AUTHOR]

The take-off of a Fourth Industrial Revolution (4IR) is at the core of a vibrant academic debate with an increasingly larger number of studies investigating the effects of the latest generation of new technologies, namely artificial intelligence (AI), flexible automation, additive manufacturing, big data, etc., — hereinafter defined as intelligent technologies. A great deal of attention has been paid to the effects associated with the adoption of such transformative technologies, whilst the impact associated with their production, and in particular with the development of related technological knowledge, has remained almost unexplored. Using patent data at country level for a sample of industrialized economies between 1990 and 2014, this paper seeks to quantify the productivity spillovers associated with intelligent technologies. We show that the elasticity of aggregate productivity to the stock of knowledge related to intelligent technologies is statistically significant and economically important, ranging between 0.01 and 0.06. Since these innovations have increased by a factor of 3 from the early 1990s, based on our most conservative estimates, knowledge related to intelligent technologies would account for a range between 3 and 8% of observed productivity change. We also document that the time pattern of productivity spillovers associated with intelligent technology patents may conform to a productivity J-curve , corroborating the view that such innovations could behave as General Purpose Technologies (GPTs). [ABSTRACT FROM AUTHOR]

• We describe the development of Pharma 4.0, a prospective cyber–physical-based framework for drug discovery and development. Pharma 4.0, a technology ecosystem in drug development analogous to Industry 4.0 in healthcare, is transforming the traditional approach to drug discovery and development, aligning product quality with less time to market, and creating intelligent stakeholder networks through effective collaborations. The wide range of potential Pharma 4.0 networks have produced several conceptualizations, which have led to a lack of clarity and definition. The main emphasis of this paper is on the clinical trial stage of drug development in the Pharma 4.0 era. It highlights the merged computerized technologies that are currently used in clinical research, and proposes a framework for integrating Pharma 4.0 technologies. The impact of and barriers to employing the proposed framework are discussed, highlighting its potential and some future research applications. [ABSTRACT FROM AUTHOR]

The paper aims at investigation of the nexus between Industry 4.0 (I4.0) and environmental sustainability in addition to exploring the long-run and short-run effects of Industry 4.0 on CO 2 emissions. For this end, energy consumption, internet and communication technology (ICT) exports, research and development (R&D), artificial intelligence (AI), ICT technology patents and bitcoin are taken as control variables of Industry 4.0 for a panel of 9 countries that contribute to 62% of the total CO 2 emissions in the world. For this purpose, the paper follows two approaches. First, the paper proposes utilization of AI and ICT technology patents, technology-related R&Ds and ICT exports as variables of I4.0 in addition to investigating the effects of economic growth, energy consumption and bitcoin. Second, to control structural changes and nonlinearity in the cointegrating relations and existence of degenerate cointegration, Fourier panel bootstrap autoregressive distributed lag model (FPBARDL) is utilized. Afterwards, FPBARDL based long, short and strong causality analyses are conducted. The empirical findings revealed positive impacts of all I4.0-related variables on emissions in the long-run. Though I4.0 related AI and ICT innovation has no direct effect in the short-run, its effects are determined through increased energy consumption towards emissions. The strong positive effects of energy consumption and positive effects of economic growth, ICT exports and R&D are observed both in the short and long-run. In addition to positive impacts of I4.0 on environment, the findings favor insufficiency of policies focusing on lowering emissions in I4.0 context.Policy recommendations include strong commitment to energy efficiency and renewable energies and trade policies with environmental concerns. [ABSTRACT FROM AUTHOR]

Recent advancements in cloud computing and software technology have resulted in the development of powerful Artificial Intelligence (AI) tools for engineering applications. However, the impact of AI in future engineering jobs remains ambiguous. This paper discusses recent AI developments, AI applications, the influence of AI on the Engineering profession, and the productivity of engineers. In addition, ethics, and professional impacts to be considered with the introduction of AI are addressed. The results of a survey conducted among people from Engineering colleges across Ireland are also presented. [ABSTRACT FROM AUTHOR]

Digital twin technology can be considered as an innovation accelerator. By providing a live copy of physical systems, digital twins bring to the table numerous advantages such as accelerated business processes, enhanced productivity, and faster innovation with reduced costs. For these numerous advantages digital twin is an ideal solution for several problems in domains such as Industry 4.0, education, healthcare and smart cities. However, to make sure the digital twin contributes effectively to these domains by representing a synchronized real-time copy of the physical system, the network connecting the physical and digital twins should fulfill a set of requirements such as low latency of real-time communication, data security and quality. This paper provides an overview on the technology of digital twin and its application domains with a detailed discussion on its networking requirements and proposed enabling technologies to fulfill them. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup. This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets' specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers. [ABSTRACT FROM AUTHOR]

In the context of industry 4.0, management of knowledge represents a real challenge, since the tacit knowledge acquired by the expert operators is not transferred quickly and easily to newly arrived operators. This sharing of knowledge could help in the faster adaptation of humans to workstations and could bring the more agile accommodation of artificial intelligence techniques to allow the self-learning. The Business Process Management (BPM) is a technique which enables the representation and analysis of processes, has been already mention in the literature as a useful tool that can facilitate the Knowledge Management. A process repository can be accomplished with BPM, thus promoting agile and fast knowledge transfer in a context where new skills emerge and must be quickly taken up. This paper intends to show the development of the working instructions maps, with workers' tacit knowledge, using the BPMN 2.0, in a chemical industry. This representation allowed the creation of a knowledge's repository which will help the company (in a I4.0 environment) to deal with the most existing workforce rotation, thus preserving most of the knowledge within the company itself. [ABSTRACT FROM AUTHOR]

Taking into account the technological evolution of the last decades and the proliferation of information systems in society, today we see the vast majority of services provided by companies and institutions as digital services. Industry 4.0 is the fourth industrial revolution where technologies and automation are asserting themselves as major changes. Robotic Process Automation (RPA) has numerous advantages in terms of automating organizational and business processes. Allied to these advantages, the complementary use of Artificial Intelligence (AI) algorithms and techniques allows to improve the accuracy and execution of RPA processes in the extraction of information, in the recognition, classification, forecasting and optimization of processes. In this context, this paper aims to present a study of the RPA tools associated with AI that can contribute to the improvement of the organizational processes associated with Industry 4.0. It appears that the RPA tools enhance their functionality with the objectives of AI being extended with the use of Artificial Neural Network algorithms, Text Mining techniques and Natural Language Processing techniques for the extraction of information and consequent process of optimization and of forecasting scenarios in improving the operational and business processes of organizations. [ABSTRACT FROM AUTHOR]

Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation), which guarantees privacy of the data and allows neural networks to learn successfully. We made several experiments to show how much the performance of the trained neural nets will suffer from the deepening of the anonymization power. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution introduced a new paradigm in manufacturing systems. The digital network is at the basis of the smart manufacturing and the physical context is strictly related to the artificial intelligence. This new manufacturing context drastically changed the role of the operator since the increasing adoption of innovative devices in manufacturing process modified the work activities and the operator is employed in more cognitive than physical tasks. Therefore, the purpose of this paper consists in developing an analytical framework to assess the human cognitive capacity occupancy and the human processing time of correct information known as the quality performance. The analytical framework presented allows to assess the human mental workload imposed by the task and how the processing speed of correct information changes when quality performance varies. [ABSTRACT FROM AUTHOR]

The Industry 4.0 vision proposes a seamless integration of several modern concepts and technologies, such as the Internet of Things (IoT), artificial intelligence, or robotics, to capture and contextualize data to improve the manufacturing processes. In this respect, Industry 4.0 is all about information and connectivity. Mixed reality (MR) takes the data collected by IoT systems and helps workers by visualizing contextualized data in real time. Human-Computer Interaction research has shown that MR can be advantageous in various application scenarios. However, there is a gap between research and practice when it comes to real world scenarios. So the question arises whether MR is already suitable for efficiently supporting employees in their daily work in industrial settings. To answer this question, we examined two maintenance scenarios in the field of intralogistics: (i) the maintenance of roller conveyors and (ii) the alignments of containers in shuttle warehouses using the Microsoft HoloLens. This paper (i) describes the scenarios and the specific challenges they pose, (ii) presents the prototypes that have been developed, and (iii) discusses the results of user studies that have been conducted to evaluate the practical applicability by presenting inhibiting and facilitating factors. In summary, although MR technology itself seems to be very promising in the scenarios presented, further research on hardware ergonomics and intuitive interaction design is needed. [ABSTRACT FROM AUTHOR]

Production activities are generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacturing to the maximum, make the production more flexible and improving the quality of making smart decisions, however, implementing the Smart Manufacturing(SM) concept provides this opportunity supported by the new generation of the technologies. Internet Of Things (IoT) for more connectivity and getting data in real time, Big Data to store the huge volume of data and Deep Learning algorithms(DL) to learn from the historical and real time data to generate knowledge, that can be used, predict all the risks, problem solving, and better decision-making. In this paper, we will introduce SM and the main technologies to success the implementation, the benefits, and the challenges. [ABSTRACT FROM AUTHOR]