Development of the digital model of the jewellery production process for resource optimisation and prediction

ABSTRACT

Smart manufacturing is becoming one of the core tendencies in manufacturing nowadays. In the conventional production process mode, the varieties in the production process, manpower processing time, and production scheduling lead to a big challenge in production process management. In order to keep pace with the rapid technology development and market demand diversification, digital and intelligent transformation became extremely essential for manufacturing industries. It is able to evaluate and manage the production process performance in a timely and scientific manner. With the digital model, the production efficiency can be improved and the resources management can be optimised based on the prediction. In this study, the traditional labour-intensive jewellery manufacturing is used and analysed to evaluate its digital model for the resource optimisation and prediction. By evaluating the production process by functional groups separately with the manpower level classification, the digital model could provide an automatic and efficient solution to its production process management system and logistic flow. It eliminates the unnecessary time-consuming working process and enhances the working process efficiency, which is capable of optimising the entire production efficiency as well as performing the resource prediction.

KEYWORDS:

• Industry 4.0
• smart manufacturing
• production process modelling
• resource optimisation and prediction

Additional information

Funding

This work was supported by the Innovation and Technology Commission Research and Development (R&D) Cash Rebate Scheme [grant number CRP/063/17].

Notes on contributors

Fei Lin

Dr Fei Lin received her B.E. degree (1st Class Honors) in Electrical and Electronic Engineering from the University of Manchester, UK, in 2011, and M.Sc. (Distinction) in Electrical Power System engineering from the University of Manchester, UK, in 2012, and Ph.D. degree in Electrical and Electronic Engineering from The University of Hong Kong, in 2017. She is currently working in the Hong Kong Productivity Council as an Associate Consultant. Her main areas of research interest are smart manufacturing, robotics, electric machines, motor control, fault signature and fault-tolerant control.

Man Chun Wong

Mr Man Chun Wong received his BEng degree in mechanical and automation engineering from the Chinese University of Hong Kong, in 2016. Since then, he is working at the Hong Kong Productivity Council, as an Assistant Engineer.

Ming Ge

Ir Dr Ming Ge (FM’14) received the B.E. and MPhil degrees in electrical automation engineering and intelligent control engineering from the Hefei University of Technology, People's Republic of China, in 1992 and 1995, respectively, and the Ph.D. degree in mechanical and automation engineering from The Chinese University of Hong Kong in 2003. In 2003, he worked at the National Science Foundation for Intelligent Maintenance Systems, University of Wisconsin, the USA, as a Postdoctoral Researcher. Since June 2004, he has been with the Hong Kong Productivity Council, where he was a Consultant, and became a Senior Consultant in 2005, and a Principal Consultant in 2011. His current research and development interests include smart manufacturing, smart logistics, intelligent automation system, applications of industrial big-data and AI. Ir Dr Ge is a Fellow Member of The Hong Kong Institution of Engineers; a Visiting Professor of the Guizhou University, People's Republic of China; a Visiting Research Fellow of the Institute of Robotics and Intelligent Manufacturing of the Chinese University of Hong Kong (Shenzhen).