Applications of virtual reality in maintenance during the industrial product lifecycle: A systematic review

As a state-of-the-art computer technology, virtual reality (VR) is considered to play an important role in helping manufacturing companies stay competitive in the international market. However, despite the achievements made in the field of VR, it is still an emerging technology that lacks deeper exploration and development in industrial application scenarios, especially in the coming fourth industrial revolution (Industry 4.0). This paper aims to systematically investigate the applications of VR in industrial maintenance to discover evidence of its values, limitations, and future directions so that VR can be guided to better serve manufacturing enterprises in remaining competitive in the coming Industry 4.0. A systematic literature review (SLR) methodology is adopted to review primary studies on this topic, by which 86 studies are ultimately included. The results show that VR has proved its value in benefiting maintenance issues through the product lifecycle. However, VR is still not an indispensable element for the lifecycle management of products regarding maintenance-related issues. Several key findings are concluded based on the analysis of the 86 studies. This review is valuable for researchers who are interested in the application of VR technology in maintainability design, maintenance training or maintenance task assistance.     

Analysis of fractal manufacturing systems framework towards industry 4.0

Currently, ways to approach the design of Cyber Physical Systems in Industry 4.0 are under development. Emerging concepts of Smart Factories require the development of specialized knowledge; new working methods are also needed to manage the transition from conventional industry to industry 4.0. To achieve this objective, fractal theory could provide the appropriate knowledge and tools. Fractal systems applied to manufacturing have been widely used over the last decades to design complex adaptive systems: it allows the introduction of resilience requirements (capacity to react to changes in a turbulent environment) and to reduce the complexity of its structure, operation and management. In order to know the potential and the possibility of applying fractal theory to the design of systems in Industry 4.0, this article reviews the publications that develop fractal systems for manufacturing engineering. The review includes contributions published between 1985 (approximate date of the first works on the theory applied to manufacturing engineering) and 2019. The objective is to gather those strategies, methodologies and successful case studies that can be useful for the approaches of Industry 4.0 and to define a set of future lines of work for the adaptation of the fractal theory to the new challenges posed by Industry 4.0.     

From industry 4.0 to society 4.0, there and back

The new industrial paradigm Industry 4.0, or smart industry, is at the core of contemporary debates. The public debate on Industry 4.0 typically offers two main perspectives: the technological one and the one about industrial policies. On the contrary, the discussion on the social and organizational effects of the new paradigm is still underdeveloped. The article specifically examines this aspect, and analyzes the change that workers are subject to, along with the work organization, smart digital factories. The study originates from an empirical survey conducted by the author together with a multidisciplinary research group between 2014 and 2015 in some of the largest Italian factories.In particular, the article analyzes the links between digital society, digital culture and Industry 4.0, focusing on the issue of people’s participation in the process of change, within a specific case study from the railway sector.Many elements of the Industry 4.0 paradigm are widespread outside the factory, in society; they are not only technological elements but also cultural. One of the key aspects of the analysis is the question of participation and the “person-centered” culture. The subject is addressed critically by presenting both the RE-personalization processes (from the centrality of the users–consumers in consumption practices to the centrality of the worker in the work paradigm 4.0) and the new processes of DE personalization caused by digital automation.     

A cost benefit analysis for industry 4.0 in a job shop environment using a mixed integer linear programming model

As the manufacturing industry is approaching implementation of the 4th industrial revolution, changes will be required in terms of scheduling, production planning and control as well as cost-accounting departments. Industry 4.0 promotes decentralized production and hence, cost models are required to capture costs of products and jobs within the production network considering the utilized manufacturing system paradigm A new mathematical cost model is proposed for assessing the cost-benefit analysis of introducing Industry 4.0 elements to the manufacturing facility, specifically, integrating and connecting external suppliers as strategic partners and establishing an infrastructure for communicating information between the manufacturing company and its strategic suppliers. The mathematical model takes into consideration the bi-directional relationship between hourly rates and total hours assigned to workcentres/activities in a certain production period. A case study, from a multinational machine builder, is developed and solved using the proposed model. Results suggest that though an additional cost is required to establish infrastructure to connect suppliers, the responsiveness and agility achieved resulting from uncertainty outweighs the additional cost.     

Transdisciplinary systems approach to realization of digital transformation

With advancement in technology and emergence of fast networks, operation of businesses and global companies increasingly depend on the Internet and digital transformation of their infrastructures. Adaptation to Industry 4.0 paradigm gives rise to societal, technological and communication issues due to challenges in product, services, social and inter-disciplinary interactions. A more fundamental approach that can mitigate complexity of the new business is necessary. This paper adopts a system of systems model embedded into a transdisciplinary system design to describe a typical X4.0 system where X can be any industry sector migrating to Industry 4.0 paradigm. Two industry sectors: Education 4.0 and Retail 4.0 are studied under the amalgamated transdisciplinary system of systems model. Results show that the four artifacts in X4.0 can form the foundation of new sectorial 4.0 development with focus on specific elements in Cyber Physical Systems and Work4.0 artifacts. The transdisciplinary system approach has the advantage of a self-improving model that drives realization of digital transformation in evolutionary cycles.     

Industry 4.0 and the current status as well as future prospects on logistics

Industry 4.0, referred to as the “Fourth Industrial Revolution”, also known as “smart manufacturing”, “industrial internet” or “integrated industry”, is currently a much-discussed topic that supposedly has the potential to affect entire industries by transforming the way goods are designed, manufactured, delivered and payed. This paper seeks to discuss the opportunities of Industry 4.0 in the context of logistics management, since implications are expected in this field. The authors pursue the goal of shedding light on the young and mostly undiscovered topic of Industry 4.0 in the context of logistics management, thus following a conceptual research approach. At first, a logistics-oriented Industry 4.0 application model as well as the core components of Industry 4.0 are presented. Different logistics scenarios illustrate potential implications in a practice-oriented manner and are discussed with industrial experts. The studies reveal opportunities in terms of decentralisation, self-regulation and efficiency. Moreover, it becomes apparent that the concept of Industry 4.0 still lacks a clear understanding and is not fully established in practice yet. The investigations demonstrate potential Industry 4.0 implications in the context of Just-in-Time/Just-in-Sequence and cross-company Kanban systems in a precise manner. Practitioners could use the described scenarios as a reference to foster their own Industry 4.0 initiatives, with respect to logistics management.     

A Quality 4.0 Model for architecting industry 4.0 systems

The increasing importance of automation and smart capabilities for factories and other industrial systems has led to the concept of Industry 4.0 (I4.0). This concept aims at creating systems that improve the vertical and horizontal integration of production through (i) comprehensive and intelligent automation of industrial processes, (ii) informed and decentralized real-time decision making, and (iii) stringent quality requirements that can be monitored at any time. The I4.0 infrastructure, supported in many cases by robots, sensors, and algorithms, demands highly skilled workers able to continuously monitor the quality of both the items to be produced and the underlying production processes.While the first attempts to develop smart factories and enhance the digital transformation of companies are under way, we need adequate methods to support the identification and specification of quality attributes that are relevant to I4.0 systems. Our main contribution is to provide a refined version of the ISO 25010 quality model specifically tailored to those qualities demanded by I4.0 needs. This model aims to provide actionable support for I4.0 software engineers that are concerned with quality issues. We developed our model based on an exhaustive analysis of similar proposals using the design science method as well as expertise from seasoned engineers in the domain. We further evaluate our model by applying it to two important I4.0 reference architectures further clarifying its application.     

Factory Communications at the Dawn of the Fourth Industrial Revolution

The fourth industrial revolution (Industry 4.0) and its requirements impose radical changes to the underlying networking technologies that will be adopted in future factories. Most popular solutions in use today, in fact, are suitable for Industry 4.0 only in part, and new techniques and devices have to be developed to cope with demanding needs in terms of flexibility, communication bandwidth, real-time behavior, mobility, scalability, energy efficiency, reliability, availability and security. The goal of this paper is assessing the current situation of factory communication systems in the light of their evolution to support Industry 4.0 applications. The paper provides an overview of fundamental concepts in factory communication systems focusing, in particular, on prevalent wireless and wirebound communication protocols and standards. Research challenges in next generation industrial networks are also taken into account.     

Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case

Industry 4.0 is considered to be the fourth industrial revolution introducing a new paradigm of digital, autonomous, and decentralized control for manufacturing systems. Two key objectives for Industry 4.0 applications are to guarantee maximum uptime throughout the production chain and to increase productivity while reducing production cost. As the data-driven economy evolves, enterprises have started to utilize big data techniques to achieve these objectives. Big data and IoT technologies are playing a pivotal role in building data-oriented applications such as predictive maintenance.In this paper, we use a systematic methodology to review the strengths and weaknesses of existing open-source technologies for big data and stream processing to establish their usage for Industry 4.0 use cases. We identified a set of requirements for the two selected use cases of predictive maintenance in the areas of rail transportation and wind energy. We conducted a breadth-first mapping of predictive maintenance use-case requirements to the capabilities of big data streaming technologies focusing on open-source tools. Based on our research, we propose some optimal combinations of open-source big data technologies for our selected use cases.     

A survey on decision-making based on system reliability in the context of Industry 4.0

The business world is continually changing. Dynamic environments, full of uncertainties, complexities, and ambiguities, demand faster and more confident decisions. To compete in this environment, Industry 4.0 emerges as an essential alternative. In this context, the reliability of manufacturing is an essential aspect for companies to make successful decisions. In the literature, several technologies associated with Industry 4.0 have been applied to improve the availability of equipment, including the Internet of Things (IoT), Cyber-Physical Systems (CPS), blockchain, and data mining. Nevertheless, there is still no survey study that seeks to show how reliability has collaborated to support decision-making in organizations, in the context of Industry 4.0. In general, most applications still focus on the productivity and health of individual equipment. However, in today's volatile and complex businesses, local decisions are no longer sufficient; it is necessary to analyze the organization entirely. Thus, being aware of the impacts that a local failure can impose on the entire company has significant weight in the decision-making process. In this context, this article presents a survey to identify how researches on systems reliability has contributed to and supported the development of decision-making in Industry 4.0. The main contribution of this article is to highlight how reliability can be used to support different types of strategic decisions in the context of Industry 4.0. Finally, it highlights the need for research associating management decisions with the technologies of Industry 4.0.     

A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0

This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Guobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study.     

Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry

In recent years, Industry 4.0 has been introduced as a popular term to describe the trend towards digitisation and automation of the manufacturing environment. Despite its potential benefits in terms of improvements in productivity and quality, this concept has not gained much attention in the construction industry. This development is founded in the fact that the far-reaching implications of the increasingly digitised and automated manufacturing environment are still widely unknown. Against this backdrop, the primary objective of this paper is to explore the state of the art as well as the state of practice of Industry 4.0 relating technologies in the construction industry by pointing out the political, economic, social, technological, environmental and legal implications of its adoption. In this context, we present the results of our triangulation approach, which consists of a comprehensive systematic literature review and case study research, by illustrating a PESTEL framework and a value chain model. Additionally, we provide recommendations for further research within a research agenda.     

INTERVALES: INTERactive Virtual and Augmented framework for industriaL Environment and Scenarios

One of Industry 4.0’s greatest challenges for companies is the digitization of their processes and the integration of new related technologies such as virtual reality (VR) and augmented reality (AR), which can be used for training purposes, design, or assistance during industrial operations. Moreover, recent results and industrial proofs of concept show that these technologies demonstrate critical advantages in the industry. Nevertheless, the authoring and editing process of virtual and augmented content remains time-consuming, especially in complex industrial scenarios. While the use of interactive virtual environments through virtual and augmented reality presents new possibilities for many domains, a wider adoption of VR/AR is possible only if the authoring process is simplified, allowing for more rapid development and configuration without the need for advanced IT skills. To meet this goal, this study presents a new framework: INTERVALES. First, framework architecture is proposed, along with its different modules; this study then shows that the framework can be updated by not only IT workers, but also other job experts. The UML data model is presented to format and simplify the authoring processes for both VR and AR. This model takes into account virtual and augmented environments, the possible interactions, and ease operations orchestration. Finally, this paper presents the implementation of an industrial use case composed of collaborative robotic (cobotic) and manual assembly workstations in VR and AR based on INTERVALES data.     

How human factors affect operators' task evolution in Logistics 4.0

The spread of the Industry 4.0 paradigm introduced an increasing number of technological innovations into companies to improve logistics processes. During a transition to Logistics 4.0, it is necessary to consider all the issues that affect logistics, among which human factors (HF) are considered to be the most important. On the basis of the existing scientific literature, this article aimed to identify the relationships between 4.0 technologies implementation and HF concerning the evolution of human activities towards different hypothetical future scenarios (i.e., no changes, work replaced, or work assisted), analysing in detail the HF affected by 4.0 technologies. These relationships, which were summarised in a matrix, were also tested through a single longitudinal case study. The research showed that technology tends to replace logistics operators, not only for dangerous physical tasks but also for cognitive tasks that are stressful and repetitive. Moreover, for some tasks in which many different HF are involved, the evolution is towards operator assistance, rather than replacement.     

工业数据可视分析综述

自 2013 年工业 4.0 的概念被提出以来,全世界的工业进程都飞速奔向智能制造时代。而数据感知技术的发展进一步帮助收集海量工业数据,工业信息化革新正是机遇。但是,工业数据具有规模大、维度高、结构多变和内容复杂的特性,对其进行分析是一项严峻的挑战。多变的应用场景又导致分析的灵活度要求提高,往往需要专家参与分析循环,因此可视化在工业数据分析中有了更广泛的应用。该综述首先按生产阶段及属性分别总结了工业场景下常用的数据类型;其次,根据数据属性,按时间、空间、时空结合分类,介绍了对应的可视化方法;再次,总结了可视分析在工业场景下的应用,并讨论如何合理地集成自动化分析方法以提高分析能力;最后,展望了工业数据可视分析的发展前景,并提出未来的研究方向。     

Securing IIoT using Defence-in-Depth: Towards an End-to-End secure Industry 4.0

Industry 4.0 uses a subset of the IoT, called Industrial IoT (IIoT) to achieve connectivity, interoperability and decentralisation. The deployment of industrial networks rarely considers security by design, but this becomes imperative in smart manufacturing as connectivity increases. The combination of OT and IT infrastructures in Industry 4.0 adds new security threats beyond those of traditional industrial networks. Defence-in-Depth (DiD) strategies tackle the complexity of this problem by providing multiple defence layers, each of these focusing on a particular set of threats. Additionally, the severe requirements of IIoT networks demand lightweight encryption algorithms. Nevertheless, these ciphers must provide E2E (End-to-End) security, as data pass through intermediate entities, or middleboxes, before reaching its destination. If compromised, middleboxes could expose vulnerable information to potential attackers if it is not encrypted throughout this path. This paper presents an analysis of the most relevant security strategies in Industry 4.0, focusing primarily on DiD. With these in mind, it proposes a combination of DiD, a lightweight E2E encryption algorithm called Attribute-Based-Encryption (ABE) and object security (i.e., OSCORE) to get a full E2E security approach. This analysis is a critical first step to develop more complex and lightweight security frameworks suitable for Industry 4.0.     

Cyber Security and Privacy Issues in Industrial Internet of Things

The emergence of industry 4.0 stems from research that has received a great deal of attention in the last few decades. Consequently, there has been a huge paradigm shift in the manufacturing and production sectors. However, this poses a challenge for cybersecurity and highlights the need to address the possible threats targeting (various pillars of) industry 4.0. However, before providing a concrete solution certain aspect need to be researched, for instance, cybersecurity threats and privacy issues in the industry. To fill this gap, this paper discusses potential solutions to cybersecurity targeting this industry and highlights the consequences of possible attacks and countermeasures (in detail). In particular, the focus of the paper is on investigating the possible cyber-attacks targeting 4 layers of IIoT that is one of the key pillars of Industry 4.0. Based on a detailed review of existing literature, in this study, we have identified possible cyber threats, their consequences, and countermeasures. Further, we have provided a comprehensive framework based on an analysis of cybersecurity and privacy challenges. The suggested framework provides for a deeper understanding of the current state of cybersecurity and sets out directions for future research and applications.     

Smart Innovation Engineering: Toward Intelligent Industries of the Future

ABSTRACT

Knowledge-based engineering systems are founded upon integration of knowledge into computer systems and are one of the core requirements for the future Industry 4.0. This paper presents a system called smart innovation engineering (SIE) capable of facilitating product innovation process semi-automatically. It enhances decision-making processes using the explicit knowledge of formal decision events. The SIE system carries the promise to support the innovation processes of manufactured products in a quick and efficient way. It stores and reuses past decisional events or sets of experiences related to innovation issues, which significantly enhances innovation progression. The analysis of basic concepts and implementation method proves that SIE system is an advanced form of cyber physical systems. It is flexible, systematic, fast, and supports customization. It can play a vital role toward Industry 4.0 development.     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.