Knowledge-based engineering approach for defining robotic manufacturing system architectures

Abstract

Robotic manufacturing systems have proven to be an effective solution for modern manufacturing enterprises to deal with increasing in customer demands and market competition. However, these systems may be unable to completely satisfy user requirements because of the difference between user and design perspectives. Thus, designing robotic manufacturing systems requires iterative processes that significantly increase development costs and lead time.

A user-customised design approach is needed that enables users to customise robotic manufacturing systems as well as alleviate the burden on designers of eliciting user requirements. However, most users may not be able to customise their systems because of a lack of engineering knowledge. The authors propose a knowledge-based engineering approach to aid users in customising the architectures of robotic manufacturing systems. Two models — an ontological knowledge model and a multi-attribute decision-making model — are defined and integrated in the proposed KBE architecture definition method. A rule-based reasoning process is proposed in the ontological knowledge model based on explicit semantic descriptions of users’ unstructured or semi-structured requirements and the components of robotic manufacturing systems, which infers the possible architecture of the required system. The MADM model is adopted to evaluate the architecture alternatives to determine the optimal solution.

KEYWORDS:

• Knowledge-based engineering
• decision-making
• robotic manufacturing systems
• user customisation
• system architecture

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available from the corresponding author, Zheng, C., upon reasonable request.

Notes

1 https://www.w3.org/OWL/

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 51805437]; Fundamental Research Funds for the Central Universities: [Grant Number 31020210506005]; Natural Science Foundation of Shaanxi Province: [Grant Number 2020JQ-187].

Notes on contributors

Chen Zheng

Chen Zheng is currently an associate professor at the School of Mechanical Engineering of Northwestern Polytechnical University (NPU) in Xi’an, China. Formerly, he was a post-doctoral researcher of Roberval research institute at the Université de Technologie de Compiègne (UTC), France. Previously, he received the BSc degree from Xi’an Jiaotong University in 2009. He obtained the double MSc degrees from Xi’an Jiaotong University and the UTC, in 2012. His main research topics are manufacturing system design, mechatronic systems engineering, knowledge-based engineering and PLM. He has published over 40 papers of international journals and international conferences.

Yushu An

Yushu An is currently a graduate student at the School of Mechanical Engineering of Northwestern Polytechnical University in Xi’an, China. He was born in Qingdao City, Shandong Province, China, in 1998. Previously, he received the BSc degree from Northwestern Polytechnical University, in 2020. His main research topics are manufacturing system design, industrial robot and digital twin.

Zhanxi Wang

Zhanxi Wang is an associate professor at the School of Mechanical Engineering of Northwestern Polytechnical University (NPU), Xi’an, China. Previously, he received the BSc and MSs degrees from the NPU in 2005 and 2006. Then, he studied in University of California, Santa Barbara for his PhD degree. His main research topics are robotic integrated manufacturing, mechatronic systems engineering, smart material and structure.

Xiansheng Qin

Xiansheng Qin is currently full professor at the School of Mechanical Engineering of the Northwestern Polytechinial University (NPU), Xi’an, China. He obtained the BSc, MSc and PhD degrees in the NPU in 1983, 1986 and 1991, respectively. He was invited as visiting professor at the University of Strathclyde in 2000. His main research topics are advanced manufacturing, product lifecycle management, product quality management. He has published over 200 peer-reviewed papers in international journals and conference proceedings.

Benoît Eynard

Benoît Eynard is currently the leader of Industrial Engineering team of Roberval research institute at the Université de Technologie de Compiègne – UTC, France. In 2007, he has joined UTC as a Full Professor for managing the Department of Mechanical Systems Engineering until 2012. Previously, he was Assistant Professor at the Université de Technologie de Troyes where he managed the MSc programme on Information Technology for Mechanical Engineering.

From 2013 to 2020, he was General Chairman of the division on Factories of the Future of French Society of Mechanical Engineering also known as S.mart academic group.

He is also member of the IFIP working group 5.1. dealing with ‘Global Product development for the whole life-cycle’ and of the Design Society where he led during three years the special interest group on ‘Design Methods for Cyber-Physical Systems’.

In 1999, he obtained his PhD on Computer-Integrated Manufacturing from the University of Bordeaux. Now, he is a recognised researcher in product lifecycle management, collaborative design, systems engineering, mechatronic design, digital factory, manufacturing process management, cyber-physical production systems, eco-design and sustainable manufacturing. He has published 100 papers of international journals and more than 200 international conferences. He also has been guest editor for about 20 journal special issues and academic books.

Matthieu Bricogne

Matthieu Bricogne is an assistant professor at the Mechanical Engineering Department / Roberval laboratory in Université de Technologie de Compiègne (UTC). He obtained his MSc in Mechanical Engineering and a Mechanical Engineering Degree at UTC in 2004. He has worked for 5 years for Dassault Systèmes, an industrially-leading company specialising in 3D and Product Lifecycle Management (PLM) software. He obtained his PhD degree in Mechanical Engineering from the Ecole de Technologie Supérieure in Montréal (ETS) and UTC in 2015. His main research topics are collaborative design, mechatronics system design andKnowledge Based Engineering within a PLM environment. Since 2013, he is vice director of the ANR LabCom DIMEXP (DIgital MockUp multi-EXPertises) joint laboratory UTC/DeltaCAD, whose main objective is to develop methods and models to improve the integration of multidisciplinary expertises and heterogeneous data in advanced digital environments.

Julien Le Duigou

Julien Le Duigou is associate professor at Université de Technologie de Compiègne (France) and researcher in Roberval Laboratory ‘Mechanics energy and electricity’ and excellence laboratory MS2 T ‘Control of Technological Systems-of-Systems’. He obtained his MSc in Mechatronics and a Mechanical Engineering Degree on 2006, a PhD in Mechanical Engineering from Ecole Centrale on 2010 and an accreditation to supervise research (HDR) from UTC on 2017 about the Contributions of ontologies and machine learning to the design of mechanical systems. Currently, his research interests include Product/Process Ontologies, Decision-Aided Systems for Design and Production and Reconfigurable Manufacturing Systems. He published more than 100 papers in refereed international journals, books and conferences.

Yicha Zhang

Yicha Zhang, associate professor at University of Technology Berlfort-Montbeliard (UTBM), is active in the domain of design and process planning for additive manufacturing (AM). His main research topics include design and optimisation for AM, AI-based digital design, interdisciplinary design, integrated design & manufacturing, decision-making tools, expert systems and novel hybrid AM process development. He also is interested in the AM education; especially the design & modelling for AM. He has published more than 65 papers in peer reviewed journals and conferences, e.g. CIRP Annals. He was elected as an associate member of CIRP (International Academy for Production Engineering) in 2020, and he was awarded the prestigious CRIP Taylor Medal for his outstanding contribution to the design & planning for Additive Manufacturing in 2021.