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In-Process Prediction of Surface Roughness for High-Precision Grinding by Utilizing Power Sensor and Artificial Neural Network

Noppawat Polpuch S. Tangjitsitcharoen
Oct 2026 · Advances in Science and Technology · 0 citations · 16 references

TL;DR

The study finds that the surface roughness can be estimated with satisfactory accuracy by utilizing the proposed method under constant cutting conditions.

Abstract

This study investigated the relation between surface roughness and power signal to realize an intelligent grinding machine by utilizing a power sensor during in-process high-precision grinding of stainless steel (DSUS70DH) with a cubic boron nitride (CBN) grinding tool. The power signal, which corresponds with the grinding surface roughness, is proposed to monitor and predict the surface roughness. The area under the power signal graph, which is calculated using the trapezoidal rule, is used as an input to predict in-process surface roughness during high-precision grinding. The surface roughness is predicted by employing the two-layer feed-forward neural networks with sigmoid hidden and linear output neurons. The neural network model is trained by using the Levenberg-Marquardt backpropagation algorithm. The study finds that the surface roughness can be estimated with satisfactory accuracy by utilizing the proposed method under constant cutting conditions.

DOI Semantic Scholar
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