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.     

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 human-in-the-loop manufacturing control architecture for the next generation of production systems

In recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.     

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.     

Mass customized/personalized manufacturing in Industry 4.0 and blockchain: Research challenges,main problems,and the design of an information architecture

Mass customized and mass personalized production has become facilitated by the fourth industrial revolution. The resulting industrial environments require the development of information systems able to take the specifications of customers and convey them to the production system in such a way as to contribute to the coordination of all the stakeholders and activities required to fulfill the orders of the customers. This is beyond the capabilities of traditional systems based on MRP and ERP, since the information should be managed in a flexible and decentralized way to exploit the Smart Manufacturing facilities of Industry 4.0. Blockchain, instead, is a technology that provides those features constituting a sound information supporting basis for mass customized/personalized production. Consequently, we explore the potential of blockchain as an information technology able to support industries that base their business models on mass customized/personalized production processes. This survey allows us to identify important challenges for further developments, highlighting three issues in the production setting: (i) to deepen the interoperability of systems, (ii) to generate more implementations, and (iii) to develop efficient consensus protocols. As a response to these insights we provide a conceptual design of how blockchain contributes to managing efficiently mass customized production systems. In our design the information of customer specifications can be fused with data from the production process to generate a plan to fulfill the demand. This design arises as a solution approach to three stated problem, which are faced by mass customized production systems.     

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...     

Development of Industry 4.0 predictive maintenance architecture for broadcasting chain

Industry 4.0 Predictive Maintenance (PdM 4.0) architecture in the broadcasting chain is one of the taxonomy challenges for deploying Industry 4.0 frameworks. This paper proposes a novel PdM framework based on advanced Reference Architecture Model Industry 4.0 (RAMI 4.0) to reduce operation and maintenance costs. This framework includes real-time production monitoring, business processes, and integration based on Design Science Research (DSR) to generate an innovative Business Process Model and Notation (BPMN) meta-model. The addressed model visualizes sub-processes based on experts' and stakeholders' knowledge to reduce the cost of maintenance of audiovisual services including satellite TV, cable TV, and live audio and video broadcast services. Based on the recommendation and the concept of Industry 4.0, the proposed framework tolerates the predictable failures and further concerns in similar related industries. Some empirical experiments have been conducted by using the Islamic Republic of Iran Broadcasting’s (IRIB) high-power station (located near the capital city of Iran, Tehran) to evaluate the functionality and efficiency of the proposed predictive maintenance framework. Practical outcomes demonstrate that interval times between data collection should be increased in audio and video broadcasting predictive maintenance because of the limitation of the internal processing performance of equipment. The framework also indicates the role of the Frequency Modulation (FM) transmitters’ data clearance to reduce the instability and untrustworthy data during data mining. The proposed DSR method endorses using a customized RAMI 4.0 meta-model framework to adapt distributed broadcasting and communication with PdM 4.0, which increases the stability as well as decreasing maintenance costs of the broadcasting chain in comparison to state-of-the-art methodologies. Furthermore, it is shown that the proposed framework outperforms the best-evaluated methods in terms of acceptance.     

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.     

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.     

Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios

Smart manufacturing is arriving. It promises a future of mass-producing highly personalized products via responsive autonomous manufacturing operations at a competitive cost. Of utmost importance, smart manufacturing requires end-to-end integration of intra-business and inter-business manufacturing processes and systems. Such end-to-end integration relies on standards-compliant and interoperable interfaces between different manufacturing stages and systems. In this paper, we present a comprehensive review of the current landscape of manufacturing automation standards, with a focus on end-to-end integrated manufacturing processes and systems towards mass personalization and responsive factory automation. First, we present an authentic vision of smart manufacturing and the unique needs for next-generation manufacturing automation. A comprehensive review of existing standards for enabling manufacturing process automation and manufacturing system automation is presented. Subsequently, focusing on meeting changing demands of efficient production of highly personalized products, we detail several future-proofing manufacturing automation scenarios via integrating various existing standards. We believe that existing automation standards have provided a solid foundation for developing smart manufacturing solutions. Faster, broader and deeper implementation of smart manufacturing automation can be anticipated via the dissemination, adoption, and improvement of relevant standards in a need-driven approach.     

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.     

Innovation in the Agri‐Food sector: Exploiting opportunities for Industry 4.0

Agri‐Food producers have a responsibility to provide safe, secure and sustainable food in a world characterized by disruption and increasing intolerance of waste along supply chains. As such, it is critical that they adopt new technologies to ensure efficient and effective management of their responsibility. While Industry 4.0 (I4.0) technologies can underpin process innovation opportunities, there is a gap in research‐based understanding of how they influence innovation practice and outcomes in Agri‐Food. In this paper, we investigate how I4.0, as a set of enabling technologies, influences core process innovation practice and product innovation outcomes in Agri‐Food firms. We present case studies of two Spanish firms processing fresh food products, competing in two important subsectors of the industry, meat and fruit and vegetables. We used secondary material and semi‐structured interviews as data sources. The findings describe how, in the two cases, I4.0 has enabled responses to new customers requirements through process innovations resulting in enhanced functionality, aesthetics and meaning of the delivered products. Our paper contributes a framework identifying for researchers and managers how I4.0 technologies act as enablers of the core innovation processes and competitive outcomes.     

Characteristics of microelectronics automation and the role of production engineers—The case of Japan based on a questionnaire survey

This article clarifies that the main distinctive characteristics of microelectronics (ME) automation differentiating it from mechanical automation are knowledge requirements because of the difficulty of the new technology and resulting changes in the content of work and the size and structure of the work force. Thus, the key success factors to introduce ME automation are extensive involvement of production engineers as well as shop floor workers, and training essentially almost all workers to become knowledge workers. The article further discusses the differences by types of industry, types of processes, sizes of factories, levels of automation, and timing of automation. ©1999 John Wiley & Sons, Inc.     

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.     

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.     

Edge-cloud orchestration driven industrial smart product-service systems solution design based on CPS and IIoT

The rapid booming of advanced information and communication technologies (ICT) has promoted an encouraging smart, connected product (SCP) market that further triggers the development of manufacturing towards the servitization proposition, viz. smart product-service systems (PSS). Smart PSS aims to provide a solution (product-service) with high satisfaction and less environmental influence by leveraging SCP as the media tool. Its solution design should not just focus on the physical world nor only be enabled by the cloud side, while the cyber world and the edge side must be included in the Industry 4.0. However, only few current researches investigate about the smart PSS, let alone an overall cyber-physical and edge-cloud discussion to support its solution design. In order to fill this gap, this work proposes an edge-cloud orchestration driven solution design based on the cyber-physical systems (CPS) and industrial Internet of Things (IIoT). To make our ideas concrete, a real-life milling process was conducted as an illustrative example. It is hoped that this study can furnish industrial enterprises with meaningful sights in the process of servitization and value co-creation.     

Data-driven cyber-physical system framework for connected resistance spot welding weldability certification

A cyber-physical system is one of the integral parts of the development endeavor of the smart manufacturing domain and the Industry 4.0 wave. With the advances in data analytics, smart manufacturing is gradually transforming the global manufacturing landscape. In the Resistance Spot Welding (RSW) domain, the focus has been more on the physical systems, compared to the virtual systems. The cyber-physical system facilitates the integrated analysis of the design and manufacturing processes by converging the physical and virtual stages to improve product quality in real-time. However, a cyber-physical system integrated RSW weldability certification is still an unmet need. This research is to realize a real-time data-driven cyber-physical system framework with integrated analytics and parameter optimization capabilities for connected RSW weldability certification. The framework is based on the conceptualization of the layers of the cyber-physical system and can incorporate the design and machine changes. It integrates data from the analytics lifecycle phases, starting from the data collection operation, to the predictive analytics operation, and to the visualization of the design. This integrated framework aims to support decision-makers to understand product design and its manufacturing implications. In addition to data analytics, the proposed framework implements a closed-loop machine parameter optimization considering the target product design. The framework visualizes the target product assembly with predicted response parameters along with displaying the process parameters and material design parameters simultaneously. This layer should help the designers in their decision-making process and the engineers to gain knowledge about the manufacturing processes. A case study on the basis of a real industrial case and data is presented in detail to illustrate the application of the envisioned cyber-physical systems framework.     

An approach to support Industry 4.0 adoption in SMEs using a core-metamodel

Despite the recent growing interest in the “factory smartness”, still there are only few small and medium enterprises (SMEs) that adopt effective Industry 4.0 (I4.0) solutions. The main reasons can be related to the lack of formalized processes, lack of ICT knowledgeas well as low-cost commercial systems.To cope with these issues, this work focuses on the development and the application of an approach to provide SMEs with a multi-purpose, modular, knowledge-based system: the main aim is to provide a modular and extensible system that can be incrementally implemented without requiring huge initial investments.This system is based on a core design-knowledge meta-model. From this core meta-model, multi-purposes modules can be built: in this paper, we present modules for the traceability support, the AR-powered assembly support, the machine-to-machine control and the data analysis support.