Noncontact Surface Roughness Evaluation of Milling Surface Using CNN-Deep Learning Models

Abstract

Abstract This paper presents the analysis of end milled machined surfaces backed with experimental and deep learning model investigations. The effect of process parameters like spindle speed, feed rate, depth of cut, cutting speed, and machining duration were investigated to find machined surface roughness using Taguchi orthogonal array. The experiments were conducted on Aluminum A3003, a common material widely used in industries. Following standard DOE using Taguchi orthogonal array, surface roughness was recorded for each machining experiment. Surface roughnesses for the current study were categorized into four classes viz., fine, smooth, rough, and coarse based on the roughness value Ra. Images of the machined surface were used to develop CNN models for surface roughness class prediction. The prediction accuracies of the CNN models were compared for five types of optimizers. It was found that RAdam optimizer performed better among others with the training and test accuracy of 96.30% and 92.91% respectively. The accuracy of the prediction is higher than 90% thus has the potential to substitute human quality control procedures, saving time, energy, and cost. Conversely, the developed CNN model can assist in acquiring preferred machining conditions in advance. Finally, it can eliminate the dependency on expensive surface roughness measuring devices and have enormous practical applications in quality control processes.