Latest Papers on Industry 4.0


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ABSTRACT: Changing production systems and product requirements can trace their origin in volatile customer behaviour and evolving product requirements. This dynamic nature of customer requirements has been described as a constantly moving target, thus presenting a significant challenge for several aspects of product development. To deal with this constant and sometimes unpredictable product evolution, cyber physical production systems (CPPS) that employ condition monitoring, self-awareness and reconfigurability principles, have to be designed and implemented. This research contributes a CPPS design approach that proactively provides the required CPPS design knowledge. This approach aims to minimise or avoids future consequences and disruptions on the CPPS. This knowledge needs to be provided at the right time whilst not being intrusive to the production system designer’s cognitive activity. To effectively deal with the complexity of the cyber physical production system design activity with a manual method would lead to a time consuming, and complex support tool which is hard to implement, and difficult to use. The CPPS design approach has therefore been implemented in a prototype digital factory tool. This paper describes in detail the system requirements and system architecture for this tool. In order to establish the effectiveness of the proposed approach for designing cyber physical production systems, the prototype digital factory tool has been evaluated with a case study and a number of semi-structured interviews with both industrial and scientific stakeholders. The encouraging results obtained from this research evaluation have shown that such an approach for supporting the CPPS design activity makes stakeholders aware of their decision consequences and is useful in practice. This result can lead the way for the development and integration of such knowledge-based decision-making approaches within state-of-the-art digital factory and Computer Aided Engineering Design (CAED) tools.

ABSTRACT: Modern distributed manufacturing within Industry 4.0, supported by Cyber Physical Systems (CPSs), offers many promising capabilities regarding effective and flexible manufacturing, but there remain many challenges which may hinder its exploitation fully. One major issue is how to automatically control manufacturing equipment, e.g. industrial robots and CNC-machines, in an adaptive and effective manner. For collaborative sharing and use of distributed and networked manufacturing resources, a coherent, standardised approach for systemised planning and control at different manufacturing system levels and locations is a paramount prerequisite.In this paper, the concept of feature-based manufacturing for adaptive equipment control and resource-task matching in distributed and collaborative CPS manufacturing environments is presented. The concept has a product perspective and builds on the combination of product manufacturing features and event-driven Function Blocks (FB) of the IEC 61499 standard. Distributed control is realised through the use of networked and smart FB decision modules, enabling the performance of collaborative run-time manufacturing activities according to actual manufacturing conditions. A feature-based information framework supporting the matching of manufacturing resources and tasks, as well as the feature-FB control concept, and a demonstration with a cyber-physical robot application, are presented.

ABSTRACT: As a major enabling factor for Industry 4.0, three-dimensional (3D) printing faces technical and managerial concerns that may hinder its sustainable development. In this study, four technical challenges are reviewed as follows: time-consuming 3D object design, limited types of usable materials, low precision, and low productivity. Seven managerial concerns are also discussed as follows: 3D object database management, intellectual property rights of 3D printing, business innovation, ubiquitous manufacturing, lean manufacturing, globalization and deglobalization, and feasibility evaluation and optimization. Then, this study asserts that technical challenges should be addressed to ensure the feasibility of a 3D printing application in a manufacturing context, whereas managerial concerns should be addressed to advance and optimize a 3D printing application. Based on the discussion, to maximize profit, a smart manufacturing system based on 3D printing should continually provide 3D objects of interest to customers, or join as many ubiquitous manufacturing networks as possible.

ABSTRACT: Information technologies with their strong penetration can provide effective solutions for addressing the challenges faced by the manufacturing industry. Leveraging information technologies to enhance the competitiveness of the manufacturing industry has become a prominent trend worldwide. In this context, two important concepts for manufacturing – Industry 4.0 and cloud manufacturing – have been proposed. Industry 4.0 refers to the fourth industrial revolution, which is characterized by the widespread application of cyber-physical systems (CPS) in the manufacturing environment. Cloud manufacturing is a new service-oriented business paradigm based on the cloud concept and method. Since their inception, there has been a great deal of attention from both academia and industry. However, to date, they have largely been addressed in isolation. The fact is that, although being proposed from different perspectives and embracing different ideas, they each have some key features that can benefit one another. In order to better understand these two concepts, there is a need to compare them and clarify their relationship. To this end, this paper presents basic ideas of Industry 4.0 and cloud manufacturing, gives a brief overview of their current research, and provides a detailed comparative analysis of them from different perspectives.

ABSTRACT: This paper reviews some of the most recently reported research into challenges and leading innovations in intelligent manufacturing for the Factories of the Future (FoF). Such research can be categorised as addressing five broad topic areas: manufacturing systems frameworks, theories and models; the pervasiveness of Cyber-Physical Systems (CPSs); the critical role of semantic technologies and interoperability; the Virtual Organisation (VO) of manufacturing systems and the servitisation of manufacturing systems. The paper analyses conceptual, theoretical, empirical and technological contributions from several leading authors in domain area. This paper identifies a wide range of research topics from the elaboration of manufacturing systems frameworks to models, from sensors to CPSs, to the application of semantic technologies and interoperability architectures of the data and information generated by manufacturing agents, how VOs are shaping manufacturing environments and the increasing challenge of deploying manufacturing systems to support servitisation. The paper concludes elaborating final considerations on the challenges and leading innovations in intelligent manufacturing for the FoF research area.

ABSTRACT: In order to make the factory of the future vision a reality, various requirements need to be met. There is a need to continuously qualify the human worker about new and changing technology trends since the human is the most flexible entity in the production system. This demands introducing novel approaches for knowledge-delivery and skill transfer. This paper introduces the design, implementation and evaluation of an advanced virtual training system, which has been developed in the EU-FP7 project VISTRA. The domain of interest is automotive manufacturing since it is one of the leading industries in adopting future factory concepts and technologies such as cyber-physical systems and internet of things. First of all, the authors motivate the topic based on the state-of-the-art concerning training systems for manual assembly and relevant technologies. Then, the main challenges and research questions are presented followed by the design and implementation of the VISTRA project including its methodologies. Furthermore, the results of experimental and technical evaluation of the system are described and discussed. In the conclusion, the authors give an outlook at the implementation and evaluation of the example application in related industries.

ABSTRACT: There have been many recent advances in wireless communication technologies, particularly in the area of wireless sensor networks, which have undergone rapid development and been successfully applied in the consumer electronics market. Therefore, wireless networks (WNs) have been attracting more attention from academic communities and other domains. From an industrial perspective, WNs present many advantages including flexibility, low cost, easy deployment and so on. Therefore, WNs can play a vital role in the Industry 4.0 framework, and can be used for smart factories and intelligent manufacturing systems. In this paper, we present an overview of industrial WNs (IWNs), discuss IWN features and related techniques, and then provide a new architecture based on quality of service and quality of data for IWNs. We also propose some applications for IWNs and IWN standards. Then, we will use a case from our previous achievements to explain how to design an IWN under Industry 4.0. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make IWNs truly ubiquitous for a wide range of applications.

ABSTRACT: Today, consistent data exchange between engineering applications such as special purpose machines, Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) systems is indispensable for efficient, error free planning and operation of plant and equipment. The approach towards Industry 4.0 Studio (I4.0)—an integration project, integrates value creation chains horizontally and processes and systems vertically. Hence the customers are indirectly benefitted with standardized and reliable better quality products within specified time at affordable cost. This paper illustrates the development of a MES system through high-level understanding process with the aid of concrete examples of functioning automation and IT delivery teams together to ensure success for the approach towards smart factory or I4.0.

ABSTRACT: This work introduces a novel perspective in the field of Holonic Manufacturing Systems, based on wireless intelligent control of production systems. The presented control approach uses isoarchic control architecture and considers wireless sensor network technology as support for its implementation. A wireless holon network (WHN) is thus proposed. WHN is an instance of CPPS (Cyber-physical production systems). This allows decision-making capacities to each physical entity of the production system constituting a set of holons. These holons are connected entities of three types (Product, Resource or Order), that interact to bring out a collective intelligence. The formal models of the various holons are described as well as their exploitation via the wireless sensor network technology. An application to the internal logistics of a job shop is presented. A discussion focuses on organizational changes resulting from the exploitation of a WHN to highlight organizational implications of the operationalization of the proposed approach.

ABSTRACT: Big data related to manufacturing applications has the traits such as great quantity, multi-sources, low value density, high complexity, and dynamic state. Traditional feature extraction methods are incapable of meeting real-time demands. Therefore, a robust incremental on-line feature extraction method based on PCA (Principal Component Analysis), RIPCA (Robust Incremental Principal Component Analysis), is proposed. RIPCA adopts a sliding window to update new coming data stream and to filter outliers. The proposed method could ensure the accuracy of data analysis and meet real-time demands of big data processing for manufacturing applications. A test data set based on a semiconductor manufacturing process containing 1567 records with 590 features is used to demonstrate the availability of the proposed method. Experimental results show that the method can effectively extract features of the data stream in real time with high accuracy.


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