scholarly journals Collaborative Workplace Design: a Knowledge-Based Approach to Promote Human-Robot Integration and Multi-Objective Layout Optimization

2021 ◽  
Author(s):  
Andrea Rega ◽  
Castrese Di Marino ◽  
Agnese Pasquariello ◽  
Ferdinando Vitolo ◽  
Stanislao Patalano ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Quality Inspection ◽  
High Complexity ◽  
New Paradigm ◽  
Anova Analysis ◽  
Collaborative Robotics ◽  
Manufacturing Plants ◽  
Knowledge Based ◽  
Human Robot Collaboration ◽  
Human Centred Design

The innovation driven Industry 5.0, in agreement with Industry 4.0, leads to consider human in a prominence position as the center of manufacturing field. This pushes towards the hybridization of manufacturing plants promoting a fully collaboration between human and robot. Furthermore, the new paradigm of "human centred design" and "anthropocentric design" allows enabling a synergistic combination of human and robot skills. However, properly collaborative workplaces are currently very few. Industry is still not confident, and systems integrators hesitate to venture into Human-Robot Collaboration (HRC). Despite the effort in collaborative robotics, a general solution to overcome the current limitations in designing of collaborative workplaces still misses. In the current work, a Knowledge-Based Approach (KBA) is adopted to face collaborative workplace designing problem. The framework resulting from the KBA allows developing a modelling paradigm that enable to define a streamlined approach for the layout designing of a collaborative workplace. Finally, a what-if analysis and a ANOVA analysis are performed to generate and evaluate a set of scenarios related to a collaborative workplace for quality inspection of welded parts. Facing the high complexity and multidisciplinary of HRC can be conveyed to develop a general design approach aimed at overcoming the difficulties that limit the spread of HRC in the manufacturing field.

scholarly journals Collaborative Workplace Design: A Knowledge-Based Approach to Promote Human–Robot Collaboration and Multi-Objective Layout Optimization

2021 ◽  
Vol 11 (24) ◽  
pp. 12147
Author(s):  
Andrea Rega ◽  
Castrese Di Marino ◽  
Agnese Pasquariello ◽  
Ferdinando Vitolo ◽  
Stanislao Patalano ◽  
...  
Keyword(s):  
Complex Problem ◽  
Optimization Criterion ◽  
Layout Design ◽  
Industrial Case Study ◽  
Manufacturing Plants ◽  
Knowledge Based ◽  
Control Factors ◽  
Modeling Paradigm ◽  
Human Robot Collaboration

The innovation-driven Industry 5.0 leads us to consider humanity in a prominent position as the center of the manufacturing field even more than Industry 4.0. This pushes us towards the hybridization of manufacturing plants promoting a full collaboration between humans and robots. However, there are currently very few workplaces where effective Human–Robot Collaboration takes place. Layout designing plays a key role in assuring safe and efficient Human–Robot Collaboration. The layout design, especially in the context of collaborative robotics, is a complex problem to face, since it is related to safety, ergonomics, and productivity aspects. In the current work, a Knowledge-Based Approach (KBA) is adopted to face the complexity of the layout design problem. The framework resulting from the KBA allows for developing a modeling paradigm that enables us to define a streamlined approach for the layout design. The proposed approach allows for placing resource within the workplace according to a defined optimization criterion, and also ensures compliance with various standards. This approach is applied to an industrial case study in order to prove its feasibility. A what-if analysis is performed by applying the proposed approach. Changing three control factors (i.e., minimum distance, robot speed, logistic space configuration) on three levels, in a Design of Experiments, 27 layout configurations of the same workplace are generated. Consequently, the inputs that most affect the layout design are identified by means of an Analysis of Variance (ANOVA). The results show that only one layout is eligible to be the best configuration, and only two out of three control factors are very significant for the designing of the HRC workplace layout. Hence, the proposed approach enables the designing of standard compliant and optimized HRC workplace layouts. Therefore, several alternatives of the layout for the same workplace can be easily generated and investigated in a systematic manner.

THE CONCEPT OF AUTONOMOUS SYSTEMS IN INDUSTRY 4.0

2019 ◽  
Vol 12 (1) ◽  
pp. 77-87
Author(s):  
György Kovács ◽  
Rabab Benotsmane ◽  
László Dudás
Keyword(s):  
Industry 4.0 ◽  
Intelligent Systems ◽  
Autonomous Systems ◽  
Essential Element ◽  
Industrial Sector ◽  
Life Cycles ◽  
Multi Agent Systems ◽  
New Paradigm ◽  
Intelligent Machines ◽  
Digital Network

Recent tendencies – such as the life-cycles of products are shorter while consumers require more complex and more unique final products – poses many challenges to the production. The industrial sector is going through a paradigm shift. The traditional centrally controlled production processes will be replaced by decentralized control, which is built on the self-regulating ability of intelligent machines, products and workpieces that communicate with each other continuously. This new paradigm known as Industry 4.0. This conception is the introduction of digital network-linked intelligent systems, in which machines and products will communicate to one another in order to establish smart factories in which self-regulating production will be established. In this article, at first the essence, main goals and basic elements of Industry 4.0 conception is described. After it the autonomous systems are introduced which are based on multi agent systems. These systems include the collaborating robots via artificial intelligence which is an essential element of Industry 4.0.

scholarly journals Semantic knowledge based reasoning framework for human robot collaboration

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 373-378
Author(s):  
Sharath Chandra Akkaladevi ◽  
Matthias Plasch ◽  
Michael Hofmann ◽  
Andreas Pichler
Keyword(s):  
Semantic Knowledge ◽  
Knowledge Based ◽  
Human Robot Collaboration

Knowledge Recommendation System for Human-Robot Collaborative Disassembly Using Knowledge Graph

2021 ◽  
Author(s):  
Yang Hu ◽  
Yiwen Ding ◽  
Feng Xu ◽  
Jiayi Liu ◽  
Wenjun Xu ◽  
...  
Keyword(s):  
Recommendation System ◽  
Industrial Robots ◽  
Knowledge Graph ◽  
Disassembly Process ◽  
Knowledge Based ◽  
Manufacturing Efficiency ◽  
Human Operators ◽  
Traditional Manufacturing ◽  
Human Robot Collaboration

Abstract In recent years, more and more attention has been paid to Human-Robot Collaborative Disassembly (HRCD) in the field of industrial remanufacturing. Compared with the traditional manufacturing, HRCD helps to improve the manufacturing flexibility with considering the manufacturing efficiency. In HRCD, knowledge could be obtained from the disassembly process and then provides useful information for the operator and robots to execute their disassembly tasks. Afterwards, a crucial point is to establish a knowledge-based system to facilitate the interaction between human operators and industrial robots. In this context, a knowledge recommendation system based on knowledge graph is proposed to effectively support Human-Robot Collaboration (HRC) in disassembly. A disassembly knowledge graph is constructed to organize and manage the knowledge in the process of HRCD. After that, based on this, a knowledge recommendation procedure is proposed to recommend disassembly knowledge for the operator. Finally, the case study demonstrates that the developed system can effectively acquire, manage and visualize the related knowledge of HRCD, and then assist the human operator to complete the disassembly task by knowledge recommendation, thus improving the efficiency of collaborative disassembly. This system could be used in the human-robot collaboration disassembly process for the operators to provide convenient knowledge recommendation service.

scholarly journals Multi-Layer Quality Inspection System Framework for Industry 4.0

2021 ◽  
Vol 15 (5) ◽  
pp. 641-650
Author(s):  
Victor Azamfirei ◽  
◽  
Anna Granlund ◽  
Yvonne Lagrosen
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Production Control ◽  
Measurement Instrument ◽  
Future Research ◽  
Inspection System ◽  
Work Piece ◽  
Quality Inspection ◽  
Industrial Case Study ◽  
Key Factor

In the era of market globalisation, the quality of products has become a key factor for success in the manufacturing industry. The growing demand for customised products requires a corresponding adjustment of processes, leading to frequent and necessary changes in production control. Quality inspection has been historically used by the manufacturing industry to detect defects before customer delivery of the end product. However, traditional quality methods, such as quality inspection, suffer from large limitations in highly customised small batch production. Frameworks for quality inspection have been proposed in the current literature. Nevertheless, full exploitation of the Industry 4.0 context for quality inspection purpose remains an open field. Vice-versa, for quality inspection to be suitable for Industry 4.0, it needs to become fast, accurate, reliable, flexible, and holistic. This paper addresses these challenges by developing a multi-layer quality inspection framework built on previous research on quality inspection in the realm of Industry 4.0. In the proposed framework, the quality inspection system consists of (a) the work-piece to be inspected, (b) the measurement instrument, (c) the actuator that manipulates the measurement instrument and possibly the work-piece, (d) an intelligent control system, and (e) a cloud-connected database to the previous resources; that interact with each other in five different layers, i.e., resources, actions, and data in both the cyber and physical world. The framework is built on the assumption that data (used and collected) need to be validated, holistic and on-line, i.e., when needed, for the system to effectively decide upon conformity to surpass the presented challenges. Future research will focus on implementing and validating the proposed framework in an industrial case study.

Technodata and the Need of a Responsible Industry 4.0

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Raúl Tabarés Gutiérrez ◽  
Javier Echeverría Ezponda
Keyword(s):  
Value Chain ◽  
Industry 4.0 ◽  
Global Economy ◽  
Policy Support ◽  
Labor Rights ◽  
Competitive Advantages ◽  
New Paradigm ◽  
Trace Back ◽  
European Industry

The great transformation that will face European industry is driven by the need of digitizing the entire value chain around manufacturing for creating competitive advantages to maintain a dominant position in the global economy. This new paradigm is commonly known as Industry 4.0, and it has a significant policy support from the European Commission as well as different member states. However, this transition is full of uncertainties as the digitization of industry creates different concerns about employment, privacy, labor rights, and other issues related with this technological revolution. In this chapter, the authors trace back the origins of Industry 4.0 to the Web 2.0 phenomenon as well as they reflect upon the role of technodata and technofactories in a postindustrial society. Finally, they stress the need to reflect about developing a responsible digitization of industry that will consider societal concerns.

scholarly journals Quality Culture of Manufacturing Enterprises: A Possible Way to Adaptation to Industry 4.0

2019 ◽  
Vol 8 (4) ◽  
pp. 124 ◽  
Author(s):  
Durana ◽  
Kral ◽  
Stehel ◽  
Lazaroiu ◽  
Sroka
Keyword(s):  
Industry 4.0 ◽  
Technological Development ◽  
Organisational Culture ◽  
Individual Quality ◽  
New Paradigm ◽  
Manufacturing Enterprises ◽  
Continuous Innovation ◽  
Quality Culture ◽  
The Impact ◽  
Significant Factors

The concept of Industry 4.0 means a new paradigm of modern manufacturing. This phenomenon requires continuous innovation processes and technological development from each enterprise. Traditional concepts of quality must absorb changes and prepare themselves for new challenges. The studies linked to successful adaptation to Industry 4.0 focus mostly on technical dimensions and forget the impact of organisational culture. One should, however, remember that quality culture plays a crucial role in the organisational culture of manufacturing enterprises with elements of quality management implemented. Developed quality cultures support the innovation environment, which is why it is necessary for the enterprises to identify the current level of their quality culture and detect significant factors that differentiate individual quality cultures and focus on them. Given this fact, the aim of the paper is to analyse the typical cultures and quality concepts and to detect the factors that differentiate individual quality cultures in Slovakia. We use data from our own survey; dependences were indicated by means of correspondence analysis and the test of proportion. The improvement and assurance of quality, the use of information and the overall effectiveness are significant factors detected by the discriminant analysis. The conclusions of the survey may be used by scientific researchers but especially by manufacturing enterprises interested in quality which are coming to terms with the era of Industry 4.0.

scholarly journals TEACHING NETWORK TECHNOLOGIES THAT SUPPORT INDUSTRY 4.0

2015 ◽  
Author(s):  
Ishwar Singh ◽  
Nafia Al-Mutawaly ◽  
Tom Wanyama
Keyword(s):  
Industry 4.0 ◽  
Production Systems ◽  
Manufacturing Plants ◽  
Electrical Grid ◽  
Distributed Intelligence ◽  
Industrial Networks ◽  
Manufacturing Competitiveness ◽  
Manufacturing Productivity ◽  
Cyber Physical Production Systems ◽  
Network Technologies

Industry 4.0 is a combination of many elements, including distributed intelligence, network security, massive data, cloud computing, and analytics, among other things. Such elements are critical to the “Digital Factory”, a term that has been recently introduced by many companies indicating a comprehensive portfolio of seamlessly integrated hardware, software and technology-based services, with the aim to enhance manufacturing productivity and improving efficiency. Typically, industrial networks enable the gathering of extensive data from productionlines and plants, which are increasingly becoming distributed. The gathered data is transmitted to analysis centers where it is transformed into information and used to make better informed decisions. In addition, modern industrial networks allow plant data to be automatically filtered and transmitted to various production controllers. Ultimately, industrial networks enable Industry 4.0 to have the following benefits: improved safety, increase uptime, lower energy costs, and improved maintenance;all of which lead to manufacturing competitiveness in cyber-physical production systems supported by Smart Grid implementations. This paper presents the extent to which industrial networks are taught at the School ofEngineering Technology at McMaster University. Further, the paper covers teaching methods of industrial networks and their related applications within manufacturing plants and electrical grid.

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