Paper
15 February 2022 Model of grinding-induced line/area roughness and subsurface damage in brittle material based on genetic algorithm and deep neural network
Author Affiliations +
Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 121664D (2022) https://doi.org/10.1117/12.2617309
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong
Abstract
Post processes are usually needed to improve the quality and performance of ground brittle materials, and their low efficiency and high cost are greatly determined by grinding-induced roughness and subsurface damage (SSD). This raises an urgent demand to accurately predict various roughness and SSD depth. In this paper, grinding experiments are conducted on K9 glass samples with different processing parameters, including abrasive grain diameter, grinding depth, wheel speed, and feed speed. The line roughness Ra, area roughness Sa, and SSD depth are measured. Based on genetic algorithm (GA) and deep neural network, a relationship model among processing parameters, Ra, Sa, and SSD depth, is established. The model is accurate and reliable with a mean absolute percentage error MAPE < 10% and a correlation coefficient R > 0.94. The research is valuable in the evaluation of surface and subsurface integrity for ground brittle materials.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shenxin Yin and Huapan Xiao "Model of grinding-induced line/area roughness and subsurface damage in brittle material based on genetic algorithm and deep neural network", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 121664D (15 February 2022); https://doi.org/10.1117/12.2617309
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neural networks

Glasses

Genetic algorithms

Abrasives

Statistical analysis

Surface finishing

RELATED CONTENT

Results of a polishing study for SCHOTT XLD glasses
Proceedings of SPIE (September 24 2015)
On the fractal model of a rough surface
Proceedings of SPIE (June 14 2006)
Noncontact estimate of grinding-induced subsurface damage
Proceedings of SPIE (November 11 1999)
Thermal shock testing of lapped optical glass
Proceedings of SPIE (September 14 2007)

Back to Top