Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.'' This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion'' in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.
A growing number of publications address the best practices to use Large Language Models (LLMs) for software engineering in recent years. However, most of this work focuses on widely-used general purpose programming languages like Python due to their widespread usage training data. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in proprietary contexts, remains underexplored. This research aims to utilize and integrate LLMs in the industrial development process, solving real-life programming tasks (e.g., generating a movement routine for a robotic arm) and accelerating the development cycles of manufacturing systems.
Marco De Luca, Domenico Amalfitano, Anna Rita Fasolino
et al.
Ensuring software quality in embedded firmware is critical, especially in safety-critical domains where compliance with functional safety standards (ISO 26262) requires strong guarantees of software reliability. While machine learning-based fault prediction models have demonstrated high accuracy, their lack of interpretability limits their adoption in industrial settings. Developers need actionable insights that can be directly employed in software quality assurance processes and guide defect mitigation strategies. In this paper, we present a structured process for defining context-specific software metric thresholds suitable for integration into fault detection workflows in industrial settings. Our approach supports cross-project fault prediction by deriving thresholds from one set of projects and applying them to independently developed firmware, thereby enabling reuse across similar software systems without retraining or domain-specific tuning. We analyze three real-world C-embedded firmware projects provided by an industrial partner, using Coverity and Understand static analysis tools to extract software metrics. Through statistical analysis and hypothesis testing, we identify discriminative metrics and derived empirical threshold values capable of distinguishing faulty from non-faulty functions. The derived thresholds are validated through an experimental evaluation, demonstrating their effectiveness in identifying fault-prone functions with high precision. The results confirm that the derived thresholds can serve as an interpretable solution for fault prediction, aligning with industry standards and SQA practices. This approach provides a practical alternative to black-box AI models, allowing developers to systematically assess software quality, take preventive actions, and integrate metric-based fault prediction into industrial development workflows to mitigate software faults.
Jannick Stranghöner, Philipp Hartmann, Marco Braun
et al.
High-mix low-volume (HMLV) industrial assembly, common in small and medium-sized enterprises (SMEs), requires the same precision, safety, and reliability as high-volume automation while remaining flexible to product variation and environmental uncertainty. Current robotic systems struggle to meet these demands. Manual programming is brittle and costly to adapt, while learning-based methods suffer from poor sample efficiency and unsafe exploration in contact-rich tasks. To address this, we present SHaRe-RL, a reinforcement learning framework that leverages multiple sources of prior knowledge. By (i) structuring skills into manipulation primitives, (ii) incorporating human demonstrations and online corrections, and (iii) bounding interaction forces with per-axis compliance, SHaRe-RL enables efficient and safe online learning for long-horizon, contact-rich industrial assembly tasks. Experiments on the insertion of industrial Harting connector modules with 0.2-0.4 mm clearance demonstrate that SHaRe-RL achieves reliable performance within practical time budgets. Our results show that process expertise, without requiring robotics or RL knowledge, can meaningfully contribute to learning, enabling safer, more robust, and more economically viable deployment of RL for industrial assembly.
The petrochemical industry faces significant technological, environmental, occupational safety, and financial challenges. Since its emergence in the 1920s, technologies that were once innovative have now become obsolete. However, factors such as the protection of trade secrets in industrial processes, limited budgets for research and development, doubts about the reliability of new technologies, and resistance to change from decision-makers have hindered the adoption of new approaches, such as the use of IoT devices. This paper addresses the challenges and opportunities presented by the research, development, and implementation of these technologies in the industry. It also analyzes the investment in research and development made by companies in the sector in recent years and provides a review of current research and implementations related to Industry 4.0.
Ali Bakhshi Movahed, Hamed Nozari, Aminmasoud Bakhshi Movahed
Technology plays an undeniable role in today's industrial world, especially in manufacturing and smart factories. Unlike previous industrial revolutions, humans are at the core of the fifth generation of the Industrial Revolution. One of the critical aspects of Industry 5.0 (I 5.0) is its emphasis on human-centricity. The integration of modern technologies can be clearly observed in smart factories, which offer enhanced comfort and professionalism. This study highlights the significance of I 5.0 and smart factory production (SFP). A total of 36 articles are reviewed and systematically categorized using the meta-synthesis methodology. The research emphasizes the influence of I 5.0 on SFP through the use of modern technologies and comprehensive policy frameworks. This new paradigm has the potential to streamline people's lives and bring a transformative shift to smart factory production lines. Enhancing the structure of factories appears feasible under this optimistic perspective.
The primary goal of our research was to validate a context-specific safety climate measure (the Heavy Vehicle Safety Climate Scale: HVSCS) in a sample of heavy mobile equipment operators (N = 277). An exploratory strategy was adopted, using exploratory factor analysis (EFA) to validate the items. The statistical results revealed a five-factor structure, with two factors at the organisational level and three factors at the group level. In addition, a nomological analysis showed that both organisational and supervisory safety climate factors presented distinct correlation patterns with other safety-related variables, including situational and routine violations, safety citizenship behaviour, context-specific safety behaviours and risk propensity. In this study we developed and psychometrically validated a context-specific safety climate tool for lone heavy vehicle drivers in the quarrying industry: the Heavy Vehicle Safety Climate Scale (HVSCS). It is hoped that the final 37-item HVSCS will be utilised by those managing heavy vehicle operations, particularly in the quarrying industry, to identify context-specific opportunities for safety climate improvements and in turn reduce the risk of safety incidents.
Industrial safety. Industrial accident prevention, Medicine (General)
Background: The globalization of business has significantly increased the number of international business travelers (IBTs), yet their health issues remain inadequately studied. Short-term IBTs, who travel for less than 6 months, lack mandatory health checks under Japanese law, making it difficult to assess their health risks. The COVID-19 pandemic further complicated business travel, highlighting the need for enhanced health management strategies. Methods: A cross-sectional questionnaire survey was conducted among listed companies in Japan between September and December 2021. The survey targeted general affairs and human resources departments of 3,845 companies, yielding 251 valid responses (6.5% response rate). The questionnaire covered the necessity of business travel, health concerns before and after COVID-19, and expectations for occupational health support. Statistical analyses, including Pearson’s chi-square test and text mining, were performed to evaluate trends. Results: Before COVID-19, key health concerns included medical issues during travel (82.2%), infectious disease prevention (69%), and general health management (19.7%). Post-pandemic, priorities shifted to COVID-19 prevention, infectious disease control, and mental health support. Large companies emphasized psychological care, while smaller firms focused on infectious disease management. Business travel remained crucial for 85% of respondents, particularly for on-site guidance and sales. Conclusions: The pandemic underscored the need for comprehensive health management for IBTs, incorporating infection control, psychological support, and preventive care. As global travel resumes, companies must reassess health strategies to mitigate risks and ensure traveler well-being.
Industrial safety. Industrial accident prevention, Medicine (General)
With the rapid progress of industrialization, serious accidents have frequently occurred at industrial sites. Despite the existence of several laws and regulations for preventing industrial accidents, including the Occupational Safety and Health Act, social criticism has been raised regarding their inefficiency. To address this issue, the Serious Accident Punishment Act was implemented. The Ministry of Employment and Labor and the Korea Safety and Health Agency have prepared and implemented various industrial accident prevention measures and programs; however, the number of industrial accidents has not decreased. Therefore, new evaluation criteria must be introduced when establishing policies or systems to prevent industrial accidents. This study introduces evaluation items used in Europe and the United States as industrial accident prevention items for assessing industrial accident prevention projects and proposes a plan to select priorities for these items by applying the fuzzy TOPSIS technique. Prioritization was determined by calculating four evaluation criteria and ten industrial accident prevention items using a 7-point linguistic scale. Our results allow for the simultaneous consideration of several industrial accident prevention items. Thus, various aspects of industrial accident prevention activities can be comprehensively evaluated, enhancing the effectiveness and execution of safety and health management, thereby helping to reduce industrial accidents.
Bruno Santos, Rogério Luís C. Costa, Leonel Santos
Unlocking the potential of Industry 5.0 hinges on robust cybersecurity measures. This new Industrial Revolution prioritises human-centric values while addressing pressing societal issues such as resource conservation, climate change, and social stability. Recognising the heightened risk of cyberattacks due to the new enabling technologies in Industry 5.0, this paper analyses potential threats and corresponding countermeasures. Furthermore, it evaluates the existing industrial implementation frameworks, which reveals their inadequacy in ensuring a secure transition from Industry 4.0 to Industry 5.0. Consequently, the paper underscores the necessity of developing a new framework centred on cybersecurity to facilitate organisations' secure adoption of Industry 5.0 principles. The creation of such a framework is emphasised as a necessity for organisations.
Paulo C. Anacleto Filho, Ana Colim, Cristiano Jesus
et al.
The field of ergonomics has been significantly shaped by the advent of evolving technologies linked to new industrial paradigms, often referred to as Industry 4.0 (I4.0) and, more recently, Industry 5.0 (I5.0). Consequently, several studies have reviewed the integration of advanced technologies for improved ergonomics in different industry sectors. However, studies often evaluate specific technologies, such as extended reality (XR), wearables, artificial intelligence (AI), and collaborative robot (cobot), and their advantages and problems. In this sense, there is a lack of research exploring the state of the art of I4.0 and I5.0 virtual and digital technologies in evaluating work-related biomechanical risks. Addressing this research gap, this study presents a comprehensive review of 24 commercial tools and 10 academic studies focusing on work-related biomechanical risk assessment using digital and virtual technologies. The analysis reveals that AI and digital human modelling (DHM) are the most commonly utilised technologies in commercial tools, followed by motion capture (MoCap) and virtual reality (VR). Discrepancies were found between commercial tools and academic studies. However, the study acknowledges limitations, including potential biases in sample selection and search methodology. Future research directions include enhancing transparency in commercial tool validation processes, examining the broader impact of emerging technologies on ergonomics, and considering human-centred design principles in technology integration. These findings contribute to a deeper understanding of the evolving landscape of biomechanical risk assessment.
Industrial safety. Industrial accident prevention, Medicine (General)
Ayaka Hayase, Takeshi Onoue, Kazuki Nishida
et al.
Objectives: Metabolic syndrome (MS) is a significant health concern in the working-age population. Since 2008, Japan has mandated health insurers to implement Specific Health Checkups to identify individuals with MS and preliminary groups, making Specific Health Guidance (SHG) compulsory for these groups. People receiving SHG multiple times is increasing as it is conducted as an annual public program. Therefore, we evaluated the influence of a health guidance history on the effectiveness of subsequent guidance. Methods: Using data from 10,191 participants in the 2017 Motivational Health Guidance (a type of SHG involving a single session), this longitudinal study assessed the changes in health checkup findings from 2017 to 2018. Participants were categorized based on their previous year’s (2016) SHG eligibility and participation: Group 1 (n=3,903) met the 2016 SHG criteria and participated, Group 2 (n=2,305) met the criteria but did not participate, and Group 3 (n=3,983) had no MS risk factors and did not need to participate in the 2016 SHG. Results: The entire cohort and Groups 2 and 3 exhibited significant weight loss after 1 year. Group 1 showed a significant negative association, with a 3% (odds ratio [OR] 0.64; 95% confidence interval [CI], 0.55–0.75) and 5% body weight loss (OR 0.66; 95% CI, 0.54–0.81) than Group 3. Men in Group 1 showed a significant association with new-onset MS (OR 2.56; 95% CI, 1.93–3.40). Conclusions: The findings suggest that participants with a history of health guidance in the previous year may have low rates of achieving weight loss and a high incidence of new-onset MS after 1 year.
Industrial safety. Industrial accident prevention, Medicine (General)
Tactile skins made from textiles enhance robot-human interaction by localizing contact points and measuring contact forces. This paper presents a solution for rapidly fabricating, calibrating, and deploying these skins on industrial robot arms. The novel automated skin calibration procedure maps skin locations to robot geometry and calibrates contact force. Through experiments on a FANUC LR Mate 200id/7L industrial robot, we demonstrate that tactile skins made from textiles can be effectively used for human-robot interaction in industrial environments, and can provide unique opportunities in robot control and learning, making them a promising technology for enhancing robot perception and interaction.
Stein Tore Johansen, Bjørn Tore Løvfall, Tamara Rodriguez Duran
et al.
A methodology for building pragmatic physics based models (Zoric et al., 2015b) is here adapted to a use-case in the steel industry. The challenge is to predict the erosion of steel ladle linings, such that the model can support operators to decide if the lade lining can be used one more time or not. If the ladle has too thin lining 140 tons of hot liquid steel may escape out of the ladle, with huge consequences for workers and plant. The development was done with a very small core team (two developers), which is typical for many industrial developments. The adopted workflow for the development, challenges that were faced, and some model results are presented. One key learning is that development of models should allow time for maturing the process understanding, and time should be given for many iterations by "questions-responses and actions" at the various levels in the model development. The good interactions between development team and industry case owner is an important success factor. In this case the results of using the PPBM (Pragmatism in physics-based modelling) were good thanks to very successful interaction between development team and industry case owner. Combining or extending the model with use of ML methods and cognition-related methods, such as knowledge graphs and self-adaptive algorithms is discussed.
With the growing need for automation and the ongoing merge of OT and IT, industrial networks have to transport a high amount of heterogeneous data with mixed criticality such as control traffic, sensor data, and configuration messages. Current advances in IT technologies furthermore enable a new set of automation scenarios under the roof of Industry 4.0 and IIoT where industrial networks now have to meet new requirements in flexibility and reliability. The necessary real-time guarantees will place significant demands on the networks. In this paper, we identify IIoT use cases and infer real-time requirements along several axes before bridging the gap between real-time network technologies and the identified scenarios. We review real-time networking technologies and present peer-reviewed works from the past 5 years for industrial environments. We investigate how these can be applied to controllers, systems, and embedded devices. Finally, we discuss open challenges for real-time communication technologies to enable the identified scenarios. The review shows academic interest in the field of real-time communication technologies but also highlights a lack of a fixed set of standards important for trust in safety and reliability, especially where wireless technologies are concerned.
Industry 4.0 operates based on IoT devices, sensors, and actuators, transforming the use of computing resources and software solutions in diverse sectors. Various Industry 4.0 latency-sensitive applications function based on machine learning to process sensor data for automation and other industrial activities. Sending sensor data to cloud systems is time consuming and detrimental to the latency constraints of the applications, thus, fog computing is often deployed. Executing these applications across heterogeneous fog systems demonstrates stochastic execution time behavior that affects the task completion time. We investigate and model various Industry 4.0 ML-based applications' stochastic executions and analyze them. Industries like oil and gas are prone to disasters requiring coordination of various latency-sensitive activities. Hence, fog computing resources can get oversubscribed due to the surge in the computing demands during a disaster. We propose federating nearby fog computing systems and forming a fog federation to make remote Industry 4.0 sites resilient against the surge in computing demands. We propose a statistical resource allocation method across fog federation for latency-sensitive tasks. Many of the modern Industry 4.0 applications operate based on a workflow of micro-services that are used alone within an industrial site. As such, industry 4.0 solutions need to be aware of applications' architecture, particularly monolithic vs. micro-service. Therefore, we propose a probability-based resource allocation method that can partition micro-service workflows across fog federation to meet their latency constraints. Another concern in Industry 4.0 is the data privacy of the federated fog. As such, we propose a solution based on federated learning to train industrial ML applications across federated fog systems without compromising the data confidentiality.
Jobish John, Md. Noor-A-Rahim, Aswathi Vijayan
et al.
This paper explores the role that 5G, WiFi-7, and Time-Sensitive Networking (TSN) can play in driving smart manufacturing as a fundamental part of the Industry 4.0 vision. The paper provides an in-depth analysis of each technology's application in industrial communications, with a focus on TSN and its key elements that enable reliable and secure communication in industrial networks. In addition, the paper includes a comparative study of these technologies, analyzing them based on a number of industrial use-cases, supported secondary applications, industry adoption, and current market trends. The paper concludes by highlighting the challenges and future directions for the adoption of these technologies in industrial networks and emphasizes their importance in realizing the Industry 4.0 vision within the context of smart manufacturing.