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Human–machine interaction towards Industry 5.0: Human-centric smart manufacturing
... Another concern is physical testing, which was done to determine the performance of the wearables under actual conditions. Test subjects wore the prototypes in both laboratory and real-life situations by having them perform their daily activities to capture information on comfort, level of strain reduction, and versatility of the designs [14]. These devices contained sensors that could monitor the user's physiological data, such as muscle movements, skin pressure, and skin temperature. ...
... For instance, although the exoskeleton provided considerable strain relief while lifting heavy objects, some individuals complained of area-specific soreness after utilizing the suit for an extended period. Likewise, bright clothing helped track physiological parameters; however, the positioning of the sensors needed further optimization to enhance accuracy [13,14]. The feedback gathered from the simulations and the prototypes' usage helped improve them. ...
Designing intelligent wearable products entails integrating human factors, engineering, and bionics to develop ergonomic, efficient and convenient products. Human-machine engineering is a discipline that addresses the integration between the user wearing a particular system and improving the relationship between the user and the system. At the same time, bionics is an approach that aims to mimic biological systems and structures to enhance the performance of a particular product. This research explores the possibility of integrating these specializations to design new and enhanced wearable technology products with improved usability, convenience, and flexibility for practical usage. A detailed analysis of human biomechanics and ergonomic requirements established design parameters to ensure that wearable devices could be seamlessly integrated into daily life without hindering user movement. Bionic principles, such as the flexibility of animal joints and the energy-efficient movements of natural organisms, were applied to optimize the mechanical and structural aspects of the devices. This approach enabled the creation of products that mimic the natural dynamics of the human body, offering improved responsiveness and functionality. Prototypes were developed based on human-centred design principles and evaluated using simulation and testing environments. Wearables such as exoskeletons, bright clothing, and health-monitoring devices were examined for their ability to adapt to various physical conditions and environmental changes. Results demonstrate a significant increase in user comfort, reduction in mechanical strain, and enhanced performance, validating the effectiveness of integrating human-machine engineering and bionics in wearable design.
... Companies are paying more and more attention to cooperation between people and machines, in particular with regard to optimizing supply chain processes, increasing efficiency, and offering the highest quality products and services. In this context, an important element of the transformation towards Industry 5.0 is the increasingly easier and more efficient cooperation between employees and robots and machines [11]. ...
Additive manufacturing is a technology that creates objects by adding successive layers of material. The 3D method is an alternative to subtractive production, in which production involves removing material from the initial solid. 3D printing requires the initial design of the manufactured object using computer design, for example, one of the following programs: CAD, 3DCrafter, Wings 3D, Cinema 3, Blender, 3ds Max, Autodesk Inventor, and others. It is also possible to scan an existing object to be manufactured using 3D printing technology. An important element of Industry 5.0 is 3D printing technology, due to its favorable environmental orientation and production flexibility. Three-dimensional printing technology uses recycled materials such as powders. Therefore, it can be part of a circular economy, contributing to environmental protection. Additive manufacturing not only complements existing technologies by enabling rapid prototyping but also plays a fundamental role in sectors such as dentistry and medicine. This article consists of seven chapters relating to various aspects of 3D printing technology in the context of the assumptions and challenges of Industry 5.0. It examines the environmental impact and recycling potential of 3D printing technology, illustrates the economic integration of this technology within various industries, and discusses its future development prospects.
... These limitations hinder the real-time adaptability required for modern smart manufacturing systems. Enhancing decision-making through artificial ISSN: 2595-5748 Brazilian Journal of Technology, Curitiba, v.7, n.4, p. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]2024 intelligence and improving interaction interfaces can overcome these barriers (Yang et al., 2024). ...
This paper introduces an advanced Digital Twin (DT) of a conveyor belt system, designed to enhance industrial automation and decision-making in the context of Industry 4.0. Built using Factory I/O, the DT simulates the physical conveyor's behavior, integrating seamlessly with a Programmable Logic Controller (PLC) for real-time control. A Convolutional Neural Network (CNN) enhances decision-making by analyzing conveyor activity, such as defect detection and sorting optimization. A Fuzzy Logic (FL) system also refines CNN outputs, improving reliability by incorporating factors like confidence scores and operational parameters. A Gradio-based Human-Machine Interface (HMI) offers an intuitive platform for real-time monitoring, interaction, and manual control. The system demonstrates robust synchronization between virtual and physical components, showcasing its ability to improve efficiency, accuracy, and adaptability in industrial processes. This work contributes to smart manufacturing advancements by combining DT technology, intelligent algorithms, and adaptive control for enhanced industrial supervision.
... As the last process of modern manufacturing, mechanical assembly has a direct impact on product quality. In the era of Industry 5.0 [1][2][3], robots are extensively utilized in modern industrial manufacturing fields, such as those of computer, communication and consumer electronics (3C) [4] manufacturing, automobile assembly [5], and reducer installation [6]. ...
Currently, research on peg-in-hole (PiH) compliant assembly is predominantly limited to circular pegs and holes, with insufficient exploration of various complex-shaped PiH tasks. Furthermore, the degree of freedom for rotation about the axis of the circular peg cannot be constrained after assembly, and few studies have covered the complete process from autonomous hole-searching to insertion. Based on the above problems, a novel cross-shaped peg and hole design has been devised. The center coordinates of the cross-hole are obtained during the hole-searching process using the three-dimensional reconstruction theory of a binocular stereo vision camera. During the insertion process, 26 contact states of the cross-peg and the cross-hole were classified, and the mapping relationship between the force-moment sensor and relative errors was established based on a backpropagation (BP) neural network, thus completing the task of autonomous PiH assembly. This system avoids hand-guiding, completely realizes the autonomous assembly task from hole-searching to insertion, and can be replaced by other structures of pegs and holes for repeated assembly after obtaining the accurate relative pose between two assembly platforms, which provides a brand-new and unified solution for complex-shaped PiH assembly.
... Най-забележителната разлика между Индустрия 4.0 и Индустрия 5.0 се крие в различните гледни точки към човешкото участие. Изгражда се среда на сътрудничество, в която хората и машините съществуват съвместно, като първите използват своите отличителни компетенции, за да насочват и подобряват производствените процеси (Yang et al., 2024). В Индустрия 5.0 човешките работници поемат ключови роли при вземането на решения, иновациите и персонализирането на производството. ...
В епоха на ускорени глобални промени бизнес трансформациите придобиват все по-определящо значение за бъдещето на организациите. Нарастващото влияние на технологиите, дигитализацията и изискванията за устойчивост предизвикват компаниите да преосмислят своите управленски и финансово-счетоводни стратегии. Тези трансформации не са просто адаптивни реакции, а проактивни решения, които предопределят конкурентоспособността и устойчивостта в дългосрочен план. В условията на глобализация и интензивна пазарна динамика управленските модели, ориентирани към иновации и ефективност изпълняват все по-важна роля. Финансовата отчетност и стратегическото планиране следва да отразяват новите реалности като включват екологични и социални индикатори за устойчивост. Докладите към сборника целят да представят теоретични и практически решения за интегриране на управленски, финансово-счетоводни и планови подходи, които от една страна отговарят на настоящите предизвикателства, а от друга - подготвят бизнеса за бъдещите такива. Разглеждат се глобални и национални бизнес трансформации, като се акцентира върху ролята на устойчивостта и иновациите като двигатели на растежа и адаптацията.
... Най-забележителната разлика между Индустрия 4.0 и Индустрия 5.0 се крие в различните гледни точки към човешкото участие. Изгражда се среда на сътрудничество, в която хората и машините съществуват съвместно, като първите използват своите отличителни компетенции, за да насочват и подобряват производствените процеси (Yang et al., 2024). В Индустрия 5.0 човешките работници поемат ключови роли при вземането на решения, иновациите и персонализирането на производството. ...
В епоха на ускорени глобални промени бизнес трансформациите
придобиват все по-определящо значение за бъдещето на организациите.
Нарастващото влияние на технологиите, дигитализацията и изискванията
за устойчивост предизвикват компаниите да преосмислят своите управлен-
ски и финансово-счетоводни стратегии. Тези трансформации не са просто
адаптивни реакции, а проактивни решения, които предопределят конкурен-
тоспособността и устойчивостта в дългосрочен план. В условията на глоба-
лизация и интензивна пазарна динамика управленските модели, ориен-
тирани към иновации и ефективност изпълняват все по-важна роля. Финан-
совата отчетност и стратегическото планиране следва да отразяват новите
реалности като включват екологични и социални индикатори за устой-
чивост.
Докладите към сборника целят да представят теоретични и практиче-
ски решения за интегриране на управленски, финансово-счетоводни и пла-
нови подходи, които от една страна отговарят на настоящите предизвика-
телства, а от друга - подготвят бизнеса за бъдещите такива. Разглеждат се
глобални и национални бизнес трансформации, като се акцентира върху
ролята на устойчивостта и иновациите като двигатели на растежа и
адаптацията.
The rapid urbanization and growing complexity of cities demand innovative approaches to resource management and sustainability. This study introduces an adaptive ecosystem model for intelligent urban systems, integrating Industry 5.0 principles to enhance energy and resource management. The proposed framework combines multi-agent systems, predictive analytics, and real-time optimization to address challenges in energy allocation, environmental impact, and urban resilience. The mathematical model incorporates cost and emission constraints, ensuring an optimal balance between economic and environmental objectives. Simulation results demonstrate significant improvements in energy efficiency and reductions in carbon emissions, validating the model's applicability across various urban scenarios. The study highlights the integration of IoT, AI, and big data as pivotal components in advancing the operational and decision-making capabilities of smart cities. This research contributes to bridging the gap between technology-driven solutions and human-centric urban planning, offering practical insights for policymakers and urban developers to foster sustainable growth in intelligent cities. INTRODUCTION The rapid expansion of urban areas and the inexorable search of humanity for improved resource deployment mean that the problems of cities are becoming more difficult to solve. Addressing even a handful of these resource-allocation issues demands more energy, resourcefulness, and time than most municipalities can meet on a day-today basis. Using smart technologies like the Internet of Things (IoT), big data, and artificial intelligence (AI), cities could (or should) resourcefully reorganize how they provide services to improve the quality of life for their residents. As cities grow and change, leaders in urban areas see a bigger need for new ways to manage the city's daily life systems. City management has always depended on simple automation and standardized smart technologies. However, the modern city ecosystem, with all its diverse parts connected in different ways, needs technologies that focus on people and can fit the municipality's unique atmosphere. Additionally, these technologies must meet the unique and ever-changing needs of its population. Efficiency improvement and the potential to build resilient and sustainable cities are major components of these advancements. This research aims to createe an efficient and sustainable smart city system; that is, an adaptive ecosystem based on Industry 5.0 to address some of the most critical issues currently facing cities, including resource allocation, energy management, and the reduction of environmental impacts. The work is guided by models that place humans at the center and is informed by cutting-edge technologies. The rationale for this study is to promote individual well-being and ensure that urban centers grow in an environmentally, socially, and economically sustainable manner.
Purpose
This paper aims to investigate the economic sustainability of new work environments by exploring how interior design strategies can contribute to the Organization for Economic Co-operation and Development (OECD)’s Sustainable Development Goal (SDG) 8, which focuses on decent work and economic growth. SDG 8 aims to foster sustained, inclusive and sustainable economic development, as well as full and productive employment.
Design/methodology/approach
The pilot study was conducted at a global automobile brand’s largest facility in Kocaeli/Turkey, where 102 employees participated in stakeholder meetings divided into 18 groups. Additionally, 46 employees were interviewed through focus groups and one-on-one sessions across nine encounters. The collected data were analyzed using deductive thematic analysis, interpreting codes and themes related to spatial politics, timing politics and hybrid working culture in the new work environment.
Findings
Identified themes include individually focused tasks, virtual collaboration, agile working, socialization and urban interaction. These themes are then traced through the conceptual design proposal developed by the interior design team and discussed in relation to their alignment with SDG 8.2 (diversify, innovate and upgrade for economic productivity), SDG 8.4 (improve resource efficiency in consumption and production) and SDG 8.8 (protect labor rights and promote safe and secure working environments for all workers).
Research limitations/implications
These three targets (for inclusivity and innovation, for environmentally sustainable and for employee well-being) were selected for their relevance in shaping the physical work environment.
Originality/value
The key finding of this paper is the need for a deeper understanding of economic sustainability in interior design and the contribution of SDG 8 targets on the spatial, temporal and cultural dimensions of new work environments. Brands committed to sustainability must adopt new work models and reimagine their work environments to enhance employee satisfaction and performance, through comprehensive user research and design solutions aligned with SDG 8.
Inertial motion analysis is having a growing interest during the last decades due to its advantages over classical optical systems. The technological solution based on inertial measurement units allows the measurement of movements in daily living environments, such as in everyday life, which is key for a realistic assessment and understanding of movements. This is why research in this field is still developing and different approaches are proposed. This presents a systematic review of the different proposals for inertial motion analysis found in the literature. The search strategy has been carried out in eight different platforms, including journal articles and conference proceedings, which are written in English and published until August 2022. The results are analyzed in terms of the publishers, the sensors used, the applications, the monitored units, the algorithms of use, the participants of the studies and the validation systems employed. In addition, we delve deeply into the machine learning techniques proposed in recent years and in the approaches to reduce the estimation error. In this way, we show an overview of the research carried out in this field, going into more detail in recent years, and providing some research directions for future work.
The Internet of Things (IoT) is playing a significant role in the transformation of traditional factories into smart factories in Industry 4.0 by using network of interconnected devices, sensors, and software to monitor and optimize the production process. Predictive maintenance using the IoT in smart factories can also be used to prevent machine failures, reduce downtime, and extend the lifespan of equipment. To monitor and optimize energy usage during part manufacturing, manufacturers can obtain real-time insights into energy consumption patterns by deploying IoT sensors in smart factories. Also, IoT can provide a more comprehensive view of the factory environment to enhance workplace safety by identifying potential hazards and alerting workers to potential dangers. Suppliers can use IoT-enabled tracking devices to monitor shipments and provide real-time updates on delivery times and locations in order to analyze and optimize the supply chain in smart factories. Moreover, IoT is a powerful technology which can optimize inventory management in smart factories to reduce costs, improve efficiency, and provide real-time visibility into inventory levels and movements. To analyze and enhance the impact of internet of thing in smart factories of industry 4.0, a review is presented. Applications of internet of things in smart factories such as predictive maintenance, asset tracking, inventory management, quality control, production process monitoring, energy efficiency and supply chain optimization are reviewed. Thus, by analyzing the application of IoT in smart factories of Industry 4.0, new ideas and advanced methodologies can be provided to improve quality control and optimize part production processes.
This study analyzes the sustainability of the metaverse technology by adopting a responsible digitalization perspective to drive the transition to Industry 5.0. This is motivated by the current experiences of digital transformation paths, which appear careless regarding the side effects induced when adopting digital technologies—for example, the energy consumption associated with blockchain, the jobs lost due to 3D printing, and the continuous payments required by artificial intelligence systems. While very few sustainable solutions are currently available to properly address these issues, similar effects might materialize when adopting metaverse technology. Therefore, this study provides tools to undertake a responsible digital transformation path through the metaverse to properly manage the transition to Industry 5.0. Specifically, it offers a set of frameworks to analyze the metaverse either from the perspective of the triple bottom line or by adopting an environmental, social, and governance (ESG) perspective and linking it to the most impacted business strategies or by connecting the technology to the sustainable development goals (SDGs). These tools enable readers to understand how society at large can responsibly implement, adopt, and manage a metaverse. By utilizing these frameworks, businesses can identify the most impacted strategies and take action to address any potential negative impacts.
Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed.
The traditional means of monitoring the health of industrial systems involves the use of vibration and performance monitoring techniques amongst others. In these approaches, contact-type sensors, such as accelerometer, proximity probe, pressure transducer and temperature transducer, are installed on the machine to monitor its operational health parameters. However, these methods fall short when additional sensors cannot be installed on the machine due to cost, space constraint or sensor reliability concerns. On the other hand, the use of acoustic-based monitoring technique provides an improved alternative, as acoustic sensors (e.g., microphones) can be implemented quickly and cheaply in various scenarios and do not require physical contact with the machine. The collected acoustic signals contain relevant operating health information about the machine; yet they can be sensitive to background noise and changes in machine operating condition. These challenges are being addressed from the industrial applicability perspective for acoustic-based machine condition monitoring. This paper presents the development in methodology for acoustic-based fault diagnostic techniques and highlights the challenges encountered when analyzing sound for machine condition monitoring.
Human-centric smart manufacturing (HCSM) is one of the essential pillars in Industry 5.0. Hence, human-machine interaction (HMI), as the centre of the research agenda for the advances of smart manufacturing, has also become the focus of Industry 5.0. As Industry 5.0 proposed three core concepts of human-centric, sustainable and resilient, the design orientation of HMI needs to change accordingly. Through understanding the state-of-the-art of HMI research, the technology roadmap of HMI development in the smart manufacturing paradigm can be shaped. In this paper, the focus is to review how HMI has been applied in smart manufacturing and predict future opportunities and challenges when applying HMI to HCSM. In this paper, we provide an HMI framework based on the interaction process and analyse the existing research on HMI across four key aspects: 1) Sensor and Hardware, 2) Data Processing, 3) Transmission Mechanism, and 4) Interaction and Collaboration. We intend to analyse the current development and technologies of each aspect and their possible application in HCSM. Finally, potential challenges and opportunities in future research and applications of HMI are discussed and evaluated, especially considering that the focus of design in HCSM shifts from improving productivity to the well-being of workers and sustainability.
Industry 4.0 focuses on the realization of smart manufacturing based on cyber-physical systems (CPS). However, emerging Industry 5.0 and Society 5.0 reaches beyond CPS and covers the entire value chain of manufacturing, and faces economic, environmental, and social challenges. To meet such challenges, we regard Industry 5.0 as a socio-technical revolution based on the socio-cyber-physical system (SCPS), and propose a socio-technically enhanced wisdom manufacturing architecture and framework beyond CPS-based Industry 4.0/smart manufacturing with especially concerning transition enabling technologies such as artificial intelligence, social Internet of Things (SIoT), big data, machine learning, edge computing, social computing, 3D printing, blockchains, digital twins, and cobots. Finally we address the roadmap to blockchainized value-added SCPS-based Industrial Metaverse for Industry/Society 5.0, which will achieve high utilization of resources and provide products and services to satisfy experience-driven individual needs via metamanufacturing cloud services towards smart, resilient, sustainable, and human-centric solutions.
Zero defect manufacturing (ZDM) aims at eliminating defects throughout the value stream as well as the cost of rework and scrap. The ambitious goal of zero defects requires the extensive utilization of emerging technologies. Amidst the major drive for technological advancement, humans are often kept out of the loop because they are perceived as the root cause of error. The report from the European Commission on Industry 5.0 emphasizes that human-centric is a key pillar in building a more resilient industry and is vital to incorporate the human component into the manufacturing sector. However, we did not find any publications that explain what human-centric ZDM is, nor what the roles of humans are in advancing ZDM. As a contribution to bridging this gap, a systematic literature review is conducted using different databases. We collected 36 publications and categorised them into 3 different human roles which are managers, engineers, and operators. From our search, we found out that managers play a vital role in cultivating ZDM in the entire organization to prevent errors despite the fact they often do not have direct contact with the production line as operators. Operators can help advance ZDM through knowledge capturing with feedback functions to the engineer to better design a corrective action to prevent errors. Assistive technologies such as extended reality are efficient tools used by operators to eliminate human errors in production environments. Human-centric is now a goal in the future manufacturing sector, but it could face barriers such as high technological investments and resistance to changes in their work tasks. This paper can contribute to paving the roadmap of human-centric ZDM to bring defects to zero and reposition the manufacturing sector to become more resilient.
To facilitate the personalized smart manufacturing paradigm with cognitive automation capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by offering an adaptive and flexible solution. DRL takes the advantages of both Deep Neural Networks (DNN) and Reinforcement Learning (RL), by embracing the power of representation learning, to make precise and fast decisions when facing dynamic and complex situations. Ever since the first paper of DRL was published in 2013, its applications have sprung up across the manufacturing field with exponential publication growth year by year. However, there still lacks any comprehensive review of the DRL in the field of smart manufacturing. To fill this gap, a systematic review process was conducted, with 208 relevant publications selected to date (20-Oct.-2022), to gain a holistic understanding of the development, application, and challenges of DRL in smart manufacturing along the whole engineering lifecycle. First, the concept and development of DRL are summarized. Then, the typical DRL applications are analyzed in the four engineering lifecycle stages: design, manufacturing, logistics, and maintenance. Finally, the challenges and future directions are illustrated, especially emerging DRL-related technologies and solutions that can improve the manufacturing system's deployment feasibility, cognitive capability, and learning efficiency, respectively. It is expected that this work can provide an insightful guide to the research of DRL in the smart manufacturing field and shed light on its future perspectives.
The overwhelming acceptance and growing need for Internet of Things (IoT) products in each aspect of everyday living is creating a promising prospect for the involvement of humans, data, and procedures. The vast areas create opportunities from home to industry to make an automated lifecycle. Human life is involved in enormous applications such as intelligent transportation, intelligent healthcare, smart grid, smart city, etc. A thriving surface is created that can affect society, the economy, the environment, politics, and health through diverse security threats. Generally, IoT devices are susceptible to security breaches, and the development of industrial systems could pose devastating security vulnerabilities. To build a reliable security shield, the challenges encountered must be embraced. Therefore, this survey paper is primarily aimed to assist researchers by classifying attacks/vulnerabilities based on objects. The method of attacks and relevant countermeasures are provided for each kind of attack in this work. Case studies of the most important applications of the IoT are highlighted concerning security solutions. The survey of security solutions is not limited to traditional secret key-based cryptographic solutions, moreover physical unclonable functions (PUF)-based solutions and blockchain are illustrated. The pros and cons of each security solution are also discussed here. Furthermore, challenges and recommendations are presented in this work.
With the widespread and increasing use of Internet-of-Things (IoT) devices in all aspects of daily life, a hopeful future for people, data, and processes is emerging. Extensive spans allow for an integrated life cycle to be created from home to enterprise. The Internet of Medical things (IoMT) forms a flourishing surface that incorporates the sensitive information of human life being sent to doctors or hospitals. These open an enormous space for hackers to utilize flaws of the IoMT network to make a profit. This creates a demand for standardizing regulations and a secure system. Though many authorities are making standards, there are some lacking in the system which makes the product vulnerable. Although many established mechanisms are present for the IoT network, there are a number of obstacles preventing its general implementation in the IoMT network. One of the adoption challenges is the IoMT devices itself, because many IoMT networks consist of battery-powered devices with constrained processing capability. A general overview of the different security integrations with IoT applications has been presented in several papers. Therefore, this paper aims to provide an overview of the IoMT ecosystem, regulations, challenges of standards, security mechanisms using cryptographic solutions, physical unclonable functions (PUF)-based solutions, blockchain, and named data networking (NDN) as well, with pros and cons.
Bioinspired sensor system leads the development of new generation sensor technology with remarkable features like ultra‐sensitivity, low‐power consumption and self‐adaptability. With the help of bioinspired sensor systems, human perception can be quantified and machines can be endowed with specific perception. As an emerging technology, bioinspired sensor system has been widely used in various fields such as industrial, medical, food safety, military and robotic. This review summarizes the recent process of bioinspired sensor system. First, three bionic strategies are defined as bionic materials, bionic structures, and functional bionic according to the sources of bionic inspiration. Second, bioinspired sensor systems with different working mechanisms are summarized and classified into piezoresistive, capacitive, triboelectric, piezoelectric, and other types. Afterward, for applications, the representative works of bioinspired sensor system for health care and human‐machine interaction are focused and introduced, respectively. Finally, the current challenges and prospects of bioinspired sensor system are also discussed.
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The 21st century has seen rapid changes in technology, industry, and social patterns. Most
industries have moved towards automation, and human intervention has decreased, which has led
to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or
the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless
sensor networks (WSN). IoT and WSN are used in various control systems, including environmental
monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What
are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What
are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders
in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT?
(vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations
and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.
The recent shift to wellbeing, sustainability, and resilience under Industry 5.0 has prompted formal discussions that manufacturing should be human-centric-placing the wellbeing of industry workers at the center of manufacturing processes, instead of system-centric-only driven by efficiency and quality improvement and cost reduction. However, there is a lack of shared understanding of the essence of human-centric manufacturing, though significant research efforts exist in enhancing the physical and cognitive wellbeing of operators. Therefore, this position paper presents our arguments on the concept, needs, reference model, enabling technologies and system frameworks of human-centric manufacturing, providing a relatable vision and research agenda for future work in human-centric manufacturing systems. We believe human-centric manufacturing should ultimately address human needs defined in an Industrial Human Needs Pyramid-from basic needs of safety and health to the highest level of esteem and self-actualization. In parallel, human-machine relationships will change following a 5C evolution map-from current Coexistence, Cooperation and Collaboration to future Compassion and Coevolution. As such, human-centric manufacturing systems need to have bi-directional empathy, proactive communication and collaborative intelligence for establishing trustworthy human-machine coevolution relationships, thereby leading to high-performance human-machine teams. It is suggested that future research focus should be on developing transparent, trustworthy and quantifiable technologies that provide a rewarding working environment driven by real-world needs.
The fast-growing demand for effective Industry 4.0-ready systems from academics and practitioners has required an assessment of the state-of-the-art research and implementations in Manufacturing Execution Systems (MES). This paper primarily carries out a systematic literature review of the pertinent fields to identify the promising research topics with high potentials for further investigations. In this paper, we adopt a bibliometric and network analysis to create insights that have not been captured before in this domain. It identifies the key institutions, authors and countries that have the highest impact on today’s MES solutions. The trending and pioneering technologies, having high potential to be used in the new generation of MESs, are highlighted for further examinations. We subsequently reviewed the recent surveys related to these technologies and outlined the research limitations, gaps and opportunities that are discussed in the reviewed literature. Inspired by Industry 4.0 maturity steps, five intelligence levels are defined to examine the Industry 4.0 compatibility of the MES models. Besides, this paper briefly reviews the well-known MES solutions and examines their functionalities and intelligence levels. The paper additionally discusses the gaps between the degree of progress in academic and industrial MES solutions and the challenges that impede the adoption of novel MESs by practitioners. Lastly, we propose a conceptual framework, called Intelligent MES (IMES), to illustrate what an industry 4.0-ready MES should contain.
In the emerging world of metaverses, it is essential for speech communication systems to be aware of context to interact with virtual assets in the 3D world. This paper proposes the metaverse for aircraft maintenance training and education of Boeing-737, supplied with legacy manuals, 3D models, 3D simulators, and aircraft maintenance knowledge. Furthermore, to navigate and control operational flow in the metaverse, which is strictly followed by maintenance manuals, the context-aware speech understanding module Neuro-Symbolic Speech Executor (NSSE) is presented. Unlike conventional speech recognition methods, NSSE applies Neuro-Symbolic AI, which combines neural networks and traditional symbolic reasoning, to understand users’ requests and reply based on context and aircraft-specific knowledge. NSSE is developed with an industrially flexible approach by applying only synthetic data for training. Nevertheless, the evaluation process performed with various automatic speech recognition metrics on real users’ data showed sustainable results with an average accuracy of 94.7%, Word Error Rate (WER) of 7.5%, and the generalization ability to handle speech requests of users with the non-native pronunciation. The proposed Aircraft Maintenance Metaverse is a cheap and scalable solution for aviation colleges since it replaces expensive physical aircraft with virtual one that can be easily modified and updated. Moreover, the Neuro-Symbolic Speech Executor, playing the role of field expert, provides technical guidance and all the resources to facilitate effective training and education of aircraft maintenance.
Industry 4.0, an initiative from Germany, has become a globally adopted term in the past decade. Many countries have introduced similar strategic initiatives, and a considerable research effort has been spent on developing and implementing some of the Industry 4.0 technologies. At the ten-year mark of the introduction of Industry 4.0, the European Commission announced Industry 5.0. Industry 4.0 is considered to be technology-driven, whereas Industry 5.0 is value-driven. The coexistence of two Industrial Revolutions invites questions and hence demands discussions and clarifications. We have elected to use five of these questions to structure our arguments and tried to be unbiased for the selection of the sources of information and for the discussions around the key issues. It is our intention that this article will spark and encourage continued debate and discussion around these topics.
Industrial human-robot collaboration (HRC) aims to combine human intelligence and robotic capability to achieve higher productiveness. In industrial HRC, the communication between humans and robots is essential to enhance the understanding of the intent of each other to make a more fluent collaboration. Brain-computer interface (BCI) is a technology that could record the user’s brain activity that can be translated into interaction messages (e.g., control commands) to the outside world, which can build a direct and efficient communication channel between human and robot. However, due to lacking information feedback mechanisms, it is challenging for BCI to control robots with a high degree of freedom with a limited number of classifiable mental states. To address this problem, this paper proposes a closed-loop BCI with contextual visual feedback by an augmented reality (AR) headset. In such BCI, the electroencephalogram (EEG) patterns from the multiple voluntary eye blinks are considered the input and its online detection algorithm is proposed whose average accuracy can reach 94.31%. Moreover, an AR-enable information feedback interface is designed to achieve an interactive robotic path planning. A case study of an industrial HRC assembly task is also developed to show that the proposed closed-up BCI could shorten the time of user input in human-robot interaction.
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.
Agility is the dynamic capability of an organization which helps it to manage a change and uncertainties in the environment. The purpose of this research is to review the literature from the perspective of agility in Industry 4.0. This paper systematically reviews 381 relevant articles from peer-reviewed academic journals in the period of the last five years. The results show that agility is important for an organization to adopt Industry 4.0 technologies as it helps companies to cope with the changes that arise along with the adoption of Industry 4.0 technologies. Further, it also indicates that by adopting Industry 4.0 technologies, companies can significantly enhance their agility capability into various aspects with different technologies. The technologies which enhance the agility are: smart manufacturing, internet of things, cyber-physical system, big data and analytics and cloud computing. On the other hand, important aspects of agility include supply chain, workforce, information system, facilities, management, manufacturing and technology agility.
The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings—the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors. Despite increased research, there is a paucity of information examining the most suitable artificial neural network (ANN) for predicting gait kinematics and kinetics from IMUs. This paper compares the performance of three commonly employed ANNs used to predict gait kinematics and kinetics: multilayer perceptron (MLP); long short-term memory (LSTM); and convolutional neural networks (CNN). Overall high correlations between ground truth and predicted kinematic and kinetic data were found across all investigated ANNs. However, the optimal ANN should be based on the prediction task and the intended use-case application. For the prediction of joint angles, CNNs appear favourable, however these ANNs do not show an advantage over an MLP network for the prediction of joint moments. If real-time joint angle and joint moment prediction is desirable an LSTM network should be utilised.
Human machine interaction (HMI) is an interactive way of information exchange between human and machine. By collecting the information that can be conveyed by the person to express the intention, and then transforming and processing the information, the machine can work according to the intention of the person. However, the traditional HMI including mouse, keyboard etc. usually requires a fixed operating space, which limits people's actions and cannot directly reflect people's intentions. It requires people to learn systematically how to operate skillfully, which indirectly affects work efficiency. Hand gesture, as one of the important ways for human to convey information and express intuitive intention, has the advantages of high degree of differentiation, strong flexibility and high efficiency of information transmission, which makes hand gesture recognition (HGR) as one of the research hotspots in the field of HMI. In order to enable readers to systematically and quickly understand the research status of HGR and grasp the basic problems and development direction of HGR, this article takes the sensing method used by HGR technology as the entry point, and makes a detailed elaboration and systematic summary by referring to a large number of research achievements in recent years.
With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the amount of data from manufacturing systems has been increasing rapidly. With massive industrial data, achievements beyond expectations have been made in the product design, manufacturing, and maintain process. Big data analytics (BDA) have been a core technology to empower intelligent manufacturing systems. In order to fully report BDA for intelligent manufacturing systems, this paper provides a comprehensive review of associated topics such as the concept of big data, model driven and data driven methodologies. The framework, development, key technologies, and applications of BDA for intelligent manufacturing systems are discussed. The challenges and opportunities for future research are highlighted. Through this work, it is hoped to spark new ideas in the effort to realize the BDA for intelligent manufacturing systems.
Industry 4.0 (I4.0) technologies have been highlighted in recent literature as enablers of servitisation. Simultaneously, businesses are advised to implement a circular economy (CE) to bring new opportunities. However, it is pertinent to mention that little attention has been given to assess the role of I4.0 in adopting the CE and servitisation in a fully integrated manner. This research fills this gap by developing a conceptual framework through a systematic literature review of 139 studies investigating the relationship between the I4.0, CE, and servitisation. This study identifies the impact of these variables on a firm’s operational and financial performance (revenue stream, growth, and profitability). Our research findings advocate that adopting I4.0 technologies to the business and manufacturing model enables sustainability, energy and resource efficiency while enhancing performance and offering innovative products through smart services. Thus, firms must systematically adopt I4.0 technologies to support a CE model that creates value through servitisation. This study identifies the research gaps that are unexplored for practitioners and future researchers while providing insight into the role of I4.0 in implementing CE in the servitisation business model.
Manufacturing is undergoing a paradigmatic shift as it assimilates and is transformed by machine learning and other cognitive technologies. A new paradigm usually necessitates a new framework to comprehend it fully, organise extant knowledge, identify gaps in knowledge, guide future research and practice, and synthesise new knowledge. Paradoxically, such a framework to guide the research and practice of ML in manufacturing remains absent. This paper attempts to fill this gap by presenting the interpretive model of manufacturing as an integrative framework for ML in manufacturing. A systematic hybrid literature review approach has been adopted to conduct both thematic and conceptual synthesis of the literature. The descriptive literature review method has been used to conduct a thematic synthesis of the literature. The framework synthesis method has been used to complete a conceptual synthesis of the literature. The resultant framework, the interpretive model of manufacturing, is articulated as consisting of scan, store, interpret, execute, and learn as its purposive components. Research questions have been identified for each of these components, as well as at their interfaces, to develop a comprehensive and systematic research agenda. Additional areas for extending research have also been identified. Implications for manufacturing operations, manufacturing strategy, and manufacturing policy have been drawn out for practitioners and policy makers.
Although the fifth generation (5G) wireless networks are yet to be fully investigated, the visionaries of the 6th generation (6G) echo systems have already come into the discussion. Therefore, in order to consolidate and solidify the security and privacy in 6G networks, we survey how security may impact the envisioned 6G wireless systems, possible challenges with different 6G technologies, and the potential solutions. We provide our vision on 6G security and security key performance indicators (KPIs) with the tentative threat landscape based on the foreseen 6G network architecture. Moreover, we discuss the security and privacy challenges that may encounter with the available 6G requirements and potential 6G applications. We also give the reader some insights into the standardization efforts and research-level projects relevant to 6G security. In particular, we discuss the security considerations with 6G enabling technologies such as distributed ledger technology (DLT), physical layer security, distributed AI/ML, visible light communication (VLC), THz, and quantum computing. All in all, this work intends to provide enlightening guidance for the subsequent research of 6G security and privacy at this initial phase of vision towards reality.
Cloud computing has become a widely exploited research area in academia and industry. Cloud computing benefits both cloud services providers (CSPs) and consumers. The security challenges associated with cloud computing have been widely studied in the literature. This systematic literature review (SLR) is aimed to review the existing research studies on cloud computing security, threats, and challenges. This SLR examined the research studies published between 2010 and 2020 within the popular digital libraries. We selected 80 papers after a meticulous screening of published works to answer the proposed research questions. The outcomes of this SLR reported seven major security threats to cloud computing services. The results showed that data tampering and leakage were among the highly discussed topics in the chosen literature. Other identified security risks were associated with the data intrusion and data storage in the cloud computing environment. This SLR’s results also indicated that consumers’ data outsourcing remains a challenge for both CSPs and cloud users. Our survey paper identified the blockchain as a partnering technology to alleviate security concerns. The SLR findings reveal some suggestions to be carried out in future works to bring data confidentiality, data integrity, and availability.
Industry 4.0 has led to paradigm shifts and changes for planning and developing manufacturing processes. To successfully embrace this revolution and confront the numerous challenges, manufacturing enterprises have to cope with the need for technological advancement and provide sustainable training and education for their workforce. This great transformation affects not only the integration of the digital and physical environments but, most importantly, also the relationships between humans and manufacturing sites. In this paper, we take into account the human-in-the-loop for digitalization challenges and present a 3I (Intellect, Interaction, and Interface) aspect for factories to increase the adoption of smart technologies toward the smart manufacturing vision. The Intellect aspect aims to add knowledge to the manufacturing equipment. The Interaction aspect targets at the collaboration between humans and manufacturing equipment. The Interface aspect explores appropriate means for humans to exploit the intelligence of technologies for the communication with the manufacturing equipment. In addition to the concept-related propositions, we also demonstrate a set of selected application examples to illustrate how the proposed aspects can drive new growth opportunities for enterprises.
Industry 4.0 revolution has brought rapid technological growth and development in manufacturing industries. Technological development enables efficient manufacturing processes and brings changes in human work, which may cause new threats to employee well-being and challenge their existing skills and knowledge. Human factors and ergonomics (HF/E) is a scientific discipline to optimize simultaneously overall system performance and human well-being in different work contexts. The aim of this scoping review is to describe the state-of-the-art of the HF/E research related to the industry 4.0 context in manufacturing. A systematic search found 336 research articles, of which 37 were analysed utilizing a human-centric work system framework presented in the HF/E literature. Challenges related to technological development were analysed in micro- and macroergonomics work system frameworks. Based on the review we frame characteristics of an organisation level maturity model to optimize overall sociotechnical work system performance in the context of rapid technological development in manufacturing industries.
Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.
Innovations introduced during the Industry 4.0 era consist in the integration of the so called “nine pillars of technologies” in manufacturing, transforming the conventional factory in a smart factory.
The aim of this study is to investigate enabling technologies of Industry 4.0, focusing on technologies that have a greater impact on safety management. Main characteristics of such technologies will be identified and described according to their use in an industrial environment. In order to do this, we chose a systematic literature review (SLR) to answer the research question in a comprehensively way. Results show that articles can be grouped according to different criteria. Moreover, we found that Industry 4.0 can increase safety levels in warehouse and logistic, as well as several solutions are available for building sector.
Voice is one of the essential mechanisms for communicating and expressing one’s intentions as a human being. There are several causes of voice inability, including disease, accident, vocal abuse, medical surgery, ageing, and environmental pollution, and the risk of voice loss continues to increase. Novel approaches should have been developed for speech recognition and production because that would seriously undermine the quality of life and sometimes leads to isolation from society. In this review, we survey mouth interface technologies which are mouth-mounted devices for speech recognition, production, and volitional control, and the corresponding research to develop artificial mouth technologies based on various sensors, including electromyography (EMG), electroencephalography (EEG), electropalatography (EPG), electromagnetic articulography (EMA), permanent magnet articulography (PMA), gyros, images and 3-axial magnetic sensors, especially with deep learning techniques. We especially research various deep learning technologies related to voice recognition, including visual speech recognition, silent speech interface, and analyze its flow, and systematize them into a taxonomy. Finally, we discuss methods to solve the communication problems of people with disabilities in speaking and future research with respect to deep learning components.
Digital twins, which are considered an effective approach to realize the fusion between virtual and physical spaces, have attracted a substantial amount of attention in the past decade. With their rapid development in recent years, digital twins have been applied in various fields, particularly in industry. However, there are still some gaps to be filled and some limitations to be addressed. Here we provide a brief overview of digital twin advancements in industry and highlight the main pitfalls to avoid and challenges to overcome, to improve the maturity of digital twins and facilitate large-scale industrial applications in the future.
With the rapid growth of new technological paradigms such as the Internet of Things (IoT), it opens new doors for many applications in the modern era for the betterment of human life. One of the recent applications of the IoT is the Internet of Vehicles (IoV) which helps to see unprecedented growth of connected vehicles on the roads. The IoV is gaining attention due to enhancing traffic safety and providing low route information. One of the most important and major requirements of the IoV is preserving security and privacy under strict latency. Moreover, vehicles are required to be authenticated frequently and fast considering limited bandwidth, high mobility, and density of the vehicles. To address the security vulnerabilities and data integrity, an ultralight authentication scheme has been proposed in this article. Physical Unclonable Function (PUF) and XOR function are used to authenticate both server and vehicle in two message flow which makes the proposed scheme ultralight, and less computation is required. The proposed Easy-Sec can authenticate vehicles maintaining low latency and resisting known security threats. Furthermore, the proposed Easy-Sec needs low overhead so that it does not increase the burden of the IoV network. Computational (around 4 ms) and Communication (32 bytes) overhead shows the feasibility, efficiency, and also security features are depicted using formal analysis, Burrows, Abadi, and Needham (BAN) logic, and informal analysis to show the robustness of the proposed mechanisms against security threats.
A revolution in advanced manufacturing has been driven by digital technology in the fourth industrial revolution, also known as Industry 4.0, and has resulted in a substantial increase in profits for the industry. In a new paradigm of Industry 5.0, advanced manufacturing will step further and be capable of offering customized products and a better user experience. A number of key enabling technologies are expected to play crucial roles in assisting Industry 5.0 in meeting higher demands of data acquisition and processing, communications, and collaborative robots in the advanced manufacturing process. The aim of this survey is to provide novel insights into advanced manufacturing in Industry 5.0 by summarizing the latest progress of key enabling technologies such as Artificial Intelligence of Things (AIoT), beyond 5G communications, and collaborative robotics. Finally, key directions for future research to enable this vision to become a reality, such as the industrial metaverse, are outlined.
Metaverse expands the cyberspace with more emphasis on human-in-loop interaction, value definition of digital assets and real-virtual reflection, which facilitates the organic fusion of man, machine and material in both physical industry and digital factory. The concept of Industrial Metaverse is proposed as a new man-in-loop digital twin system of the real industrial economy which is capable of man-machine natural interaction, industrial process simulation and industrial value transaction. With the comparison with Metaverse and Digital Twin, the key features of Industrial Metaverse are summarized, which are man-in-loop, real-virtual interaction, process asserts and social network. Key technologies of Industrial Metaverse are surveyed including natural interaction, industrial process simulation, industrial value transaction and large-scale information processing and transmission technologies, etc. Potential application modes of Industrial Metaverse are given at the end as well as the challenges from technology, industry and application.KeywordsIndustrial MetaverseMetaverseMan-in-loop simulationDigital twinXRNatural interactionNFT
Contact lenses have traditionally been used for vision correction applications. Recent advances in microelectronics and nanofabrication on flexible substrates have now enabled sensors, circuits, and other essential components to be integrated on a small contact lens platform. This has opened up the possibility of using contact lenses for a range of human–machine interaction (HMI) applications, including vision assistance, eye tracking, displays, and healthcare. In this article, we systematically review the range of smart contact lens materials, device architectures, and components that facilitate this interaction for different applications. In fact, evidence from our systematic review demonstrates that these lenses can be used to display information, detect eye movements, restore vision, and detect certain biomarkers in tear fluid. Consequently, whereas previous state-of the-art reviews in contact lenses focused exclusively on biosensing, our systematic review covers a wider range of smart contact lens applications in HMI. Moreover, we present a new method of classifying the literature on smart contact lenses according to their six constituent building blocks, which are the sensing, energy management, driver electronics, communications, substrate, and the input/output interfacing modules. Based on recent developments in each of these categories, we speculate the challenges and opportunities of smart contact lenses for HMI. Moreover, based on our analysis of the state-of-the-art, we develop guidelines for the future design of a self-powered smart contact lens concept with integrated energy harvesters, sensors, and communications modules. Therefore, our review is a critical evaluation of current data and is presented with the aim of guiding researchers to new research directions in smart contact lenses.
Planners are supported in the fulfilment of their tasks by so-called PPC systems. Among other things, planners are dissatisfied with the way PPC systems present information and results. As a result, there is a risk that the number of errors due to misinterpretations will increase. These are primarily caused by Cognitive Biases. Even increasing automation will not change this, since according to experts, humans will remain central, especially in decision-making. The European Union points out that Industry 4.0 focuses more on technical feasibility and implementation and less on human needs. In response, it proposes the human-centered approach of Industry 5.0. Here, the human being should move to the center of the process. In this regard, there is still little research activity in the field of PPC and in the context of Cognitive Biases. Therefore, we want to take a first step towards the human-centered approach with this work. To this end, we conduct a systematic literature review and develop a questionnaire to determine the human-centered maturity level. The results will then be compared with already researched Cognitive Biases.
Manufacturing systems have undergone many changes with regard to their structure, organization, and operation. This chapter charts the technical developments in manufacturing systems over the last 70 years. The basic building blocks of Computer-Aided Manufacturing and their evolution to Computer-Integrated Manufacturing systems are discussed. We describe today’s Smart Manufacturing paradigm, which is central to Industry 4.0. We identify the key components of the Smart Factory and highlight its technical challenges through the presentation and discussion of frameworks and architectures. In order to create smart factories capable of autonomous optimization of
interconnected processes, disparate computer and communication systems have to operate with many machines, devices, and manufacturing infrastructure, requiring the development of advanced protocols and standards. Interoperability is a major challenge that needs continued research to achieve the integration of many devices in industrial environments and to develop global standards. The harnessing and use of data to create Digital Twins of manufacturing systems shows great promise in diverse applications, including for predictive maintenance. The whole of the product lifecycle from design through to manufacture and use can now be captured digitally in Product Lifecycle Management systems. The vision of interoperable, smart manufacturing ecosystems is slowly becoming a reality.
This review paper provides an overview of data pre-processing in Machine learning, focusing on all types of problems while building the machine learning problems. It deals with two significant issues in the pre-processing process (i). issues with data and (ii). Steps to follow to do data analysis with its best approach. As raw data are vulnerable to noise, corruption, missing, and inconsistent data, it is necessary to perform pre-processing steps, which is done using classification, clustering, and association and many other pre-processing techniques available. Poor data can primarily affect the accuracy and lead to false prediction, so it is necessary to improve the dataset's quality. So, data pre-processing is the best way to deal with such problems. It makes the knowledge extraction from the data set much easier with cleaning, Integration, transformation, and reduction methods. The issue with Data missing and significant differences in the variety of data always exists as the information is collected through multiple sources and from a real-world application. So, the data augmentation approach generates data for machine learning models. To decrease the dependency on training data and to improve the performance of the machine learning model. This paper discusses flipping, rotating with slight degrees and others to augment the image data and shows how to perform data augmentation methods without distorting the original data.
Digital solutions (including Extended Reality as well as web, mobile and AI technologies), among others, are entering a broad variety of industries and modifying the capabilities of operators through (close to) real-time display of context-dependent information. However, little is known with respect to two major issues: (i) how these technologies can be seamlessly integrated for product/process and overall manufacturing system management providing of syntactic, semantic and functional interoperability and reusability among the different manufacturing systems departments; (ii) how they can be conveyed to operators also taking into account the cognitive load that is incurred by the operators.
To this end, starting from a previous article (Longo et al., 2017), this article designs and proposes the KNOW4I approach and its practical implementation in an ICT platform (the KNOW4I platform) to further empower the Smart Operators concept.
Two major objectives are pursued. The former is to set a standard referred to as the KNOW4I methodological approach for knowledge representation, knowledge management and digital contents management within the Smart Operator domain. The latter is the implementation of the aforementioned approach as part of the KNOW4I platform that includes a suite of Smart Utilities and Objects intelligently and interactively linked with a newly released version of the Sophos-MS digital and intelligent Assistant. Experimentations and results based on multiple KPIs are carried out to account for the effectiveness of the proposed framework.
OCCUPATIONAL APPLICATIONS
We conducted a study to evaluate fatigue and workload among workers performing complex assembly tasks. We investigate several predictors of fatigue, including subjective workload estimates, sleep duration, the shift being worked, and production levels. High levels of fatigue were reported in one-third of the shifts evaluated. The main predictors of high fatigue were workload estimates, working evening shifts, and baseline fatigue. Among the six dimensions of workload, only mental demand and frustration were predictors of high fatigue. Mental demand was also rated highest. Participants reported less than seven hours of sleep in 60% of the nights evaluated. These results suggest that managers and supervisors should consider cognitive workload as a key contributing factor to fatigue in complex manual assembly. Similarly, work schedule planning should consider shift duration, start times, and end times, because of the negative influence on fatigue and the potential disruptions on sleep among workers.
A smart factory is a form of manufacturing in the era of Industry 4.0 that has adopted new integrated manufacturing technologies. The importance of human–machine interfaces (HMIs) has been increasing due to the complexity of the current manufacturing context. Therefore, it is necessary to identify and understand HMIs in smart factories from a holistic perspective, which enables us to understand the overall picture. In this study, we conducted a systematic literature review (SLR) of HMIs to identify smart factory functions, tasks, information types, interaction modalities, and their impact on human operators from the perspectives of human factors and human–computer interaction. Seventy-seven articles were selected for SLR and analysed based on three research questions. We found four smart factory functions (operation and supervision, management, maintenance service, and cybersecurity) consisting of 18 tasks, including subtasks and functions. The effects of these functions, tasks, and HMIs on human operators are also discussed. Five interaction modalities (gaze, voice, gesture, tactile, and haptic) were used to perform these functions and tasks. This paper also discusses the practical use context of HMIs in smart factories and offers HMI recommendations for users, designers, and researchers. These findings provide insights into the design of HMIs in smart factories.
The Internet of Things (IoT) allows communication between devices, things, and any digital assets that send and receive data over a network without requiring interaction with a human. The main characteristic of IoT is the enormous quantity of data created by end-user's devices that needs to be processed in a short time in the cloud. The current cloud-computing concept is not efficient to analyze very large data in a very short time and satisfy the users' requirements. Analyzing the enormous quantity of data by the cloud will take a lot of time, which affects the quality of service (QoS) and negatively influences the IoT applications and the overall network performance. To overcome such challenges, a new architecture called edge computing - that allows to decentralize the process of data from the cloud to the network edge has been proposed to solve the problems occurred by using the cloud computing approach. Furthermore, edge computing supports IoT applications that require a short response time and consequently enhances the consumption of energy, resource utilization, etc. Motivated by the extensive research efforts in the edge computing and IoT applications, in this paper, we present a comprehensive review of edge and fog computing research in the IoT. We investigate the role of cloud, fog, and edge computing in the IoT environment. Subsequently, we cover in detail, different IoT use cases with edge and fog computing, the task scheduling in edge computing, the merger of software-defined networks (SDN) and network function virtualization (NFV) with edge computing, security and privacy efforts. Furthermore, the Blockchain in edge computing. Finally, we identify open research challenges and highlight future research directions.
In the production of cold-rolled strip products, strip breakage is one of the most common failures during the cold rolling process. However, the existing prediction models on strip breakage use the conventional sliding window algorithm to process the time series data collected from the actual production, resulting in a massive amount of non-informative data, which increases the computational cost for data-driven modelling. In order to tackle this issue, this article proposed a sliding window filter method to optimise the data pre-processing of the strip breakage. Firstly, based on the existing research and understanding of strip breakage, the data characteristics in the process of strip breakage was analysed. Based on the analysis, sample variance (VAR) and length normalised complexity estimate (LNCE) were chosen to determine how informative the time window was related to strip breakage. Secondly, compared with the conventional sliding window approach, the sliding windows were classified through a filter using VAR and LNCE. Thirdly, the filtered data was fed into the Recurrent Neural Network (RNN) for strip breakage modelling. An experimental study based on actual production data collected by a cold-rolled strip manufacturer was conducted to verify this method's effectiveness. The results show that pre-processing data using the sliding window filter decreases the model's computational cost.
Work-related fatigue is a multidimensional phenomenon with significant effects on operational performance. Our work focuses on how the literature of operational research measures and models fatigue and its effects on operational performance, and on how it mitigates those effects. We position the literature of fatigue relative to that of work-rest scheduling, shift scheduling, multitasking, ergonomics, deterioration scheduling, and occupational health and safety. We classify the literature of fatigue across multiple dimensions: the methods by which it is identified and measured; the operational research methodology applied for fatigue prevention or mitigation; the flexibility allowed in work-rest scheduling and in shift scheduling; applications within manufacturing, construction, transportation, hospitals, and services; and the extent to which real data is used and results are implemented. Our work shows that operational research has contributed numerous effective algorithms and heuristic solution procedures to fatigue mitigation. We also identify several important research directions for operational research, to promote its broader and more effective use to identify and mitigate the effects of fatigue on operational performance.