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
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Design of an Intelligent Grinding Parameter Selection Assistance System

Jyun-Yu Jhang and Cheng-Jian Lin

(Received September 6, 2021; Accepted December 20, 2021)

Keywords: grinding, convolutional neural network, surface roughness, Taguchi method, differential evolution

In this study, an intelligent grinding parameter selection assistance system (IGPSAS) was designed that can be used by operators for the grinding process. In the data collection stage, an ESG-1020 surface grinder and aluminum were used for grinding experiments. The proposed IGPSAS consists of two parts: a Taguchi-based convolutional neural network (TCNN) and a differential evolution algorithm. First, the proposed TCNN was used to establish a surface roughness prediction model. Then, the proposed differential evolution algorithm was used to determine the best processing parameters. To achieve better surface smoothness prediction capabilities in the CNN model, the Taguchi method was used to optimize the parameters of the network model architecture. The influence of each factor was analyzed, and a network with stable parameters was selected for machine processing. The performance of the proposed TCNN was verified experimentally. The mean average percentage error (MAPE) of the proposed TCNN’s surface roughness prediction in the measurement of a NewView 8300 optical surface profile was 15.65%. In addition, the differential evolution algorithm was used to select the best processing parameters and perform actual processing. The MAPE of surface roughness prediction of the proposed IGPSAS was experimentally determined to be 10.97%, demonstrating that the system effectively provides the user with the ability to operate the machine with the parameters set according to the desired processing quality.

Corresponding author: Cheng-Jian Lin




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