Development of a Surface Roughness Evaluation Method from Light and Shade Composition using Deep Learning

Abstract

Many companies agree on the need to introduce smart factories that promote low-cost, high-efficiency operation by applying IoT technologies to manufacturing plants. To assist in efficient manufacturing, this paper proposes a system for evaluating surface roughness through deep learning AI methods from the distribution of shade on the surface of an object. This is thought to greatly relieve the meticulous and tedious process of manual quality control for precision-machined surfaces and assist in the establishment of smart manufacturing industries. To demonstrate the usefulness of the developed technique, 305 samples of paper were categorized into three classes based on Ra threshold values, and images of paper were taken using a microscope camera. Luminance values, standard deviations, mean values, and image histograms were used to train custom-designed CNN+ LSTM composite neural networks. Completely new samples of non-training data were used for validation, which showed an accuracy of 85.185%. The proposed method can be economical, efficient, and fast compared to conventional surface roughness evaluation procedures. It can be easily integrated into an assembly line and automate quality assurance processes. This method could also prove useful in reducing labor costs and streamlining quality assurance processes due to its flexible adaptability.