Interpolation-based virtual sample generation for surface roughness prediction

Wenwen Tian, Jiong Zhang, Fei Zhao, Xiaobing Feng, Xuesong Mei (), Guangde Chen and Hao Wang ()
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Wenwen Tian: Xi’an Jiaotong University
Jiong Zhang: National University of Singapore
Fei Zhao: Xi’an Jiaotong University
Xiaobing Feng: Xi’an Jiaotong University
Xuesong Mei: Xi’an Jiaotong University
Guangde Chen: Xi’an Jiaotong University
Hao Wang: National University of Singapore

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 1, No 20, 343-353

Abstract: Abstract Surface roughness is an essential technical indicator for the surface quality of machined parts and significantly affects the service performance of the products. Accurate prediction of the surface roughness in the machining process can play an important role in reducing costs and increasing efficiency. However, data-based methods often require a large sample size for model training to improve prediction accuracy. Obtaining a sufficient number of training samples is challenging due to cost and efficiency constraints. To this end, an interpolation-based virtual sample generation scheme is proposed in this article, which utilizes a broad learning system (BLS) to generate virtual samples of the cutting groove surface roughness. Experimental verification was carried out in an ultra-precision machining center, where depth of cut and cutting speed were selected as inputs to the BLS. The results reveal that the proposed virtual sample generation approach can considerably improve the surface roughness prediction accuracy. Compared to other machine learning methods, BLS has the highest error reduction rate with and without virtual samples.

Keywords: Surface roughness; Ultra-precision machining; Broad learning system; Virtual sample generation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-022-02054-4

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