Prediction of Burr Formation in End Micro Milling Poly methyl Methacrylate (PMMA) and Polycarbonate (PC) Substrates Using Convolutional Neural Network (CNN)

Abstract

Polymer microfluidic device is growing in the fields of disease detection, drug synthesis, and environmental monitoring because of the benefits of the miniaturized platforms that provides rapid high-throughput analysis at small sample volumes. A machining technique called micro milling is employed in the manufacture of micro components (micro fluidic devices) such as poly methyl methacrylate (PMMA) or polycarbonate (PC). Micro milling has the advantage of being a quicker, more affordable, and more effective method for fabricating more complex structures. PMMA has been used as the substrate in this study for micro milling followed by factor analysis. This aim of this study is to understand the influence of each micro milling parameter to the surface quality. This paper includes 450 microscopic images of the micro-milling substrate by different parameters like spindle speed, depth of cut and Surface quality. The microscopic images are divided to test, train and Val dataset, using three datasets and a Convolutional Neural Network (CNN) is designed.