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.     

IoT-enabled smart appliances under industry 4.0: A case study

Manufacturers expect the extra value of Industry 4.0 as the world is experiencing digital transformation. Studies have proved the potential of the Internet of Things (IoT) for reducing cost, improving efficiency, quality, and achieving data-oriented predictive maintenance services. Collecting a wide range of real-time data from products and the environment requires smart sensors, reliable communications, and seamless integration. IoT, as a critical Industry 4.0 enabler emerges smart home appliances for higher customer satisfaction, energy efficiency, personalisation, and advanced Big data analytics. However, established factories with limited resources are facing challenges to change the longstanding production lines and meet customer’s requirements. This study aims to fulfil the gaps by transforming conventional home appliances to IoT-enabled smart systems with the ability to integrate into a smart home system. An industry-led case study demonstrates how to turn conventional appliances to smart products and systems (SPS) by utilising the state-of-the-art Industry 4.0 technologies.     

Automatic machine status prediction in the era of Industry 4.0: Case study of machines in a spring factory

Recent technological developments have fueled a shift toward the computerization and automation of factories; i.e., Industry 4.0. Unfortunately, many small- and medium-sized factories cannot afford the sensor-embedded machines, cloud system, or high-performance computers required for Industry 4.0. Furthermore, the simple production processes in smaller factories do not require the level of precision found in large factories. In this study, we explored the idea of using inexpensive add-on triaxial sensors for the monitoring of machinery. We developed a dimensionality reduction method with low computational overhead to extract key information from the collected data as well as a neural network to enable automatic analysis of the obtained data. Finally, we performed an experiment at an actual spring factory to demonstrate the validity of the proposed algorithm. The system outlined in this work is meant to bring Industry 4.0 implementations within grasp of small to medium sized factories, by eliminating the need for sensors-embedded machines and high-performance computers.     

Fuzzy harmony search based optimal control strategy for wireless cyber physical system with industry 4.0

Journal of Intelligent Manufacturing - Recently, Industry 4.0 facilitates implementing several modular smart factories particularly the Cyber-Physical System. Due to enhanced growth in the...     

A control architecture for continuous production processes based on industry 4.0: water supply systems application

Journal of Intelligent Manufacturing - Industry 4.0 (I4.0) brings together new disruptive technologies, increasing future factories’ productivity. Indeed, the control of production processes...     

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.     

Digital twins-based smart manufacturing system design in Industry 4.0: A review

A smart manufacturing system (SMS) is a multi-field physical system with complex couplings among various components. Usually, designers in various fields can only design subsystems of an SMS based on the limited cognition of dynamics. Conducting SMS designs concurrently and developing a unified model to effectively imitate every interaction and behavior of manufacturing processes are challenging. As an emerging technology, digital twins can achieve semi-physical simulations to reduce the vast time and cost of physical commissioning/reconfiguration by the early detection of design errors/flaws of the SMS. However, the development of the digital twins concept in the SMS design remains vague. An innovative Function-Structure-Behavior-Control-Intelligence-Performance (FSBCIP) framework is proposed to review how digital twins technologies are integrated into and promote the SMS design based on a literature search in the Web of Science database. The definitions, frameworks, major design steps, new blueprint models, key enabling technologies, design cases, and research directions of digital twins-based SMS design are presented in this survey. It is expected that this survey will shed new light on urgent industrial concerns in developing new SMSs in the Industry 4.0 era.     

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.     

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.     

Service perspective based production control system for smart job shop under industry 4.0

Industry 4.0 describes a smart job shop as follows: it can meet individual customer requirements even if the requirements are changed at the last minute; its production control system (PCS) can rapidly respond to unexpected disruptions in production, and smart workpieces in the smart job shop can communicate with workstations to tell them what to do next. Present PCSs issue production instruction (PI) to workstation in a relatively long period such as a day, a week, even a month. And the PI is usually at process level, which means it is not sufficient to maintain smooth production flow at the operational level. Therefore, the existing PCSs cannot meet the requirements of Industry 4.0. On account of this, this article proposes a smart workpiece enabled production instruction service system for smart job shop under Industry 4.0. The PI service system in smart job shop consists of three parts such as PI sets generation, PI sets execution and PI sets update. In PI sets generation, the PI is viewed as a service requirement from the smart workpiece for the workstation, and then a PI service model is established to integrate machining actions with different kinds of manufacturing resources, processing place and processing time. Based on that, a method of converting the Gantt chart to PI sets is presented. In PI sets execution, a PI service unit is proposed for real-time issuing PIs to the radio-frequency identification (RFID) tags of smart workpieces. In PI sets update, the update of PI sets including unexecuted processes PI sets and current processes PI sets is discussed in detail. Finally, a small-scale smart job shop is taken as an example to illustrate the feasibility of the PI service system.     

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.     

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.     

Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model

Recent findings have shown that Digital Twin served multiple constituencies. However, the dilemma between the scope and scale needs a sophisticated reference architecture, a right set of technologies, and a suitable business model. Most studies in the Digital Twin field have only focused on manufacturing and proposed explicit frameworks and architecture, which faced challenges to support different integration levels through an agile process. Besides, no known empirical research has focused on exploring relationships between Digital Twin and mass individualization. Therefore, the principal objective of this study was to identify suitable Industry 4.0 technologies and a holistic reference architecture model to accomplish the most challenging Digital Twin enabled applications. In this study, a Digital Twin reference architecture was developed and applied in an industrial case. Also, Digital Twin as a Service (DTaaS) paradigm utilized for the digital transformation of unique wetlands with considerable advantages, including smart scheduled maintenance, real-time monitoring, remote controlling, and predicting functionalities. The findings indicate that there is a significant relationship between Digital Twin capabilities as a service and mass individualization.     

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.     

A smart process controller framework for Industry 4.0 settings

This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module—performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process -diagnosis module—performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module—performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform—an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework’s applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

     

A steel surface defect inspection approach towards smart industrial monitoring

Journal of Intelligent Manufacturing - With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely...     

A function-oriented biologically analogical approach for constructing the design concept of smart product in Industry 4.0

The development of the Industry 4.0 paradigm and the advancement of information technology have aroused new consumer requirements for smart products that are capable of context awareness and autonomous control. Nature holds huge potential for inspiring innovative design concepts that can meet the ever-growing need for smart products since biology perceive and interact with their living environment for survival. However, to date, very few studies have explored the application of natural wisdom in building innovative design concepts for smart products. This paper proposes a function-oriented design approach for smart products, by analogizing to biological prototypes. To do so, a unified functional representation, based on the Function–behavior–structure (FBS) ontology, is proposed to abstract biological prototypes, followed by a fuzzy triangular numbers-based algorithm designed to locate appropriate biological prototypes as analogical sources for smart product development. Moreover, functional innovative strategies and a hybrid design process are formulated to develop design concepts of smart products, by integrating several existing engineering design methods. Finally, an illustrative design case of a smart natural resource collecting system is used to demonstrate the workability of the proposed method.     

Human Digital Twin in the context of Industry 5.0

Human-centricity, a core value of Industry 5.0, places humans in the center of production. It leads to the prioritization of human needs, spanning from health and safety to self-actualization and personal growth. The concept of the Human Digital Twin (HDT) is proposed as a critical method to realize human-centricity in smart manufacturing systems towards Industry 5.0. HDTs are digital representations of humans, aiming to change the practice of human-system integration by coupling humans’ characteristics directly to the system design and its performance. In-depth analysis, critical insights, and application guidelines of HDT are essential to realize the concept of Industry 5.0 in practice and evolve the smart manufacturing paradigm in modern factories. However, the investigation on the development of HDT to evolve humans’ roles and develop humans to their full potential is limited to date. Recent studies are rarely geared towards designing a standardized framework and architecture of HDT for diverse real-world applications. Thus, this work aims to close this research gap by carrying out a comprehensive survey on HDT in the context of Industry 5.0, summarizing the ongoing evolution, and proposing a proper connotation of HDT, before discussing the conceptual framework and system architecture of HDT and analyzing enabling technologies and industrial applications. This work provides guidance on possible avenues as well as challenges for the further development of HDT and its related concepts, allowing humans to reach their potential and accommodating their diverse needs in the futuristic smart manufacturing systems shaped by Industry 5.0.