The Evaluation Technology of Manufacturer Intelligence Regarding the Selection of the Decision Support System of Smart Manufacturing Technologies: Analysis of China–South Africa Relations

Author:

Pei Fengque12ORCID,Zhang Jiaxuan1ORCID,Yuan Minghai1,He Fei3,Yan Bingwen4ORCID

Affiliation:

1. College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China

2. Changzhou Key Laboratory of Intelligent Manufacturing Technology and Equipment, Hohai University, Changzhou 213022, China

3. School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China

4. Department of Industrial and System Engineering, Cape Peninsula University of Technology, Cape Town 7535, South Africa

Abstract

With the development of international cooperation, South Africa (SA) has been China’s largest trading partner in Africa for several consecutive years. China and SA can build the digital “Belt and Road” to modernize the manufacturing system locally and optimize process control by benchmarking with the best-in-class manufacturers in each country. In this research, an evaluation technology of manufacturer intelligence regarding the selection of decision support system (DSS) of smart manufacturing technologies, analyzing China–South Africa relations, is described. Firstly, the three keys aspects that enable the technologies of DSS are discussed in detail. Then, one key technology, the manufacturers’ intelligent evaluation system with 15 indexes, was built. The indexes and their measurements are also proposed. Finally, a fusion method based on boosting with multi-kernel function (online sequential extreme learning machine based on boosting, Boosting-OSELM) is introduced. The purpose of Boosting-OSKELM is to combine several weak learners into a strong learner (lower mean square error, MSE) through an acceptable time delay. Finally, the case study is presented to demonstrate the improvement on the MSE and process time, showing a relative MSE improvement of 96.19% and a relative time delay ratio of 31.46%. Totally, the largest contribution of the proposed evaluation method in this study is the conversion of the history data saved by the manual scoring method into knowledge in accessible MES and resealable time delay, which will free up the expert workforce in the entire process. We expect this paper will help future research in this field.

Funder

Ministry of Education of Humanities and Social Science project

Changzhou Sci&Tech Program

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering

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