A hybrid approach of case-based reasoning and process reasoning to typical parts grinding process intelligent decision

ABSTRACT

After grinding, the machine tool spindle with high surface integrity has a significant impact on its subsequent service life. Therefore, it is necessary to create a reasonable process plan for the grinding process of the machine tool spindle. A hybrid method based on case-based reasoning (CBR) and process reasoning (PR) was proposed in this paper. The subjective and objective weights of feature attributes are determined in the CBR by the AHP method and the CRITIC method, respectively, and the similarities between the latest case and the retrieved case are calculated on the basis of the nearest neighbour algorithm. The most suitable process solution can be selected from the case database by means of CBR. PR is applied to solve the problem that the case cannot be retrieved through CBR or is unsatisfactory. In the case of the grinding machine spindle, the applicability of this technology was demonstrated. Consequently, the results revealed that the surface consistency of the spindle after grinding was substantially increased. A decision-making system based on the proposed approach was developed by using Qt 4.8.7 and SQLite 3. The results demonstrate the viability and efficacy of the hybrid CBR-PR method to rapidly generate process planning for specific parts.

KEYWORDS:

• Grinding
• process planning
• intelligent decision
• case-based reasoning
• process reasoning

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

Disclosure statement

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

Additional information

Funding

This work was supported by the Special Fund for the Construction of Hunan Innovative Province [grant number 2020GK2003], National Natural Science Foundation of China [grant number. U1809221] and CZT National Independent Innovation Demonstration Zone Special Project [grant number 2017XK2302].

Notes on contributors

Zhongyang Li

Zhongyang Li is a PhD student in Hunan University of Science and Technology, China. His main research interests are intelligent manufacturing technology, ultra-precision and high-efficiency processing technology for difficult-to-process ceramic materials, and robotic processing technology. Currently, he is carrying out research on process intelligent decision-making technology for grinding and polishing, and sapphire chemical mechanical polishing mechanism and intelligent processing technology.

Zhaohui Deng

Zhaohui Deng is the ‘Xiangjiang Scholar’ Distinguished Professor of Hunan University of Science and Technology, China. He is currently the Dean of the School of Mechanical and Electrical Engineering, Hunan University of Science and Technology. He is also the director of the Hunan Provincial Key Laboratory of High-efficiency and Precision Processing of Difficult-to-Process Materials. In 2012, he was rated as an outstanding expert in Xiangtan City. He is mainly engaged in scientific research and teaching in green and efficient precision machining and intelligent manufacturing technology, including intelligent grinding process software, intelligent grinding cloud platform, high-efficiency precision machining of difficult-to-machine materials, green manufacturing, intelligent grinding robot, new super-hard abrasive preparation and machining mechanism, etc.

Zhiguang Ge

Zhiguang Ge is a graduate student of Hunan University of Science and Technology, China. He is mainly engaged in intelligent processing technology of typical parts and industrial production cloud platform.

Lishu Lv

Lishu Lv received the B.S., M.S., and PhD degrees in mechanical engineering from Hunan University of Science and Technology, Xiangtan, China, in 2013, 2016 and 2020 respectively. His research interests are low carbon manufacturing, energy modelling and analysis and green processing parameter optimisation.

Jimin Ge

Jimin Ge is a PhD candidate in Hunan University of Science and Technology, China. His main research interest lies in the robotic grinding and processing technology of welds and other structural parts based on machine vision. He is also exploring the influence of robot grinding on the residual stress of the workpiece.