S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Surface Roughness Prediction and Parameter Selection for Grinding Process with Computer Numerical Control

Cheng-Jian Lin, Jyun-Yu Jhang, Shou-Zheng Huang, and Ming-Yi Tsai

(Received December 29, 2020; Accepted March 24, 2021)

Keywords: grinding assistance system, grinding process, surface roughness prediction, differential evolution, type-2 TSK fuzzy neural network

We propose a novel intelligent grinding assistance system (IGAS) for the grinding of silicon carbide (SiC) with computer numerical control (CNC). The proposed IGAS predicts surface roughness (Ra) and suggests suitable parameters for the grinding process. To establish the Ra prediction model, a type-2 functional-link-based fuzzy neural network (T2FLFNN), which updates the network parameter by Lévy-based dynamic group differential evolution (LDGDE), is developed. The LDGDE includes the Lévy flight and dynamic group mechanism to improve the shortcomings of the traditional differential evolution (DE) algorithm. Subsequently, DE is adopted to optimize the grinding parameters according to user requirements. Experimental results of practical machining show that the mean absolute percentage error (MAPE) using the IGAS is as low as 1.62%. Therefore, the proposed IGAS can provide suitable grinding parameters according to the requirements of users.

Corresponding author: Cheng-Jian Lin




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