Automated surface finish inspection using convolutional neural networks

Abstract

The surface finish of a machined part has an important effect on friction, wear, and aesthetics. The surface finish became a critical quality measure since 1980s mainly due to demands from automotive industry. Visual inspection and quality control have been traditionally done by human experts. Normally, it takes a substantial amount of operators time to stop the process and compare the quality of the produced piece with a surface roughness gauge. This manual process does not guarantee a consistent quality of the surface and is subject to human error and dependent upon the subjective opinion of the expert. Current advances in image processing, computer vision, and machine learning have created a path towards an automated surface finish inspection increasing the automation level of the whole process even further than it is now. In this thesis work, we propose a deep learning approach to replicate human judgment without using a surface roughness gauge. We used a Convolutional Neural Network (CNN) to train a surface finish classifier. Because of data scarcity, we generated our own image dataset of aluminum pieces produced from turning and boring operations on a Computer Numerical Control (CNC) lathe, which consists of a total of 980 training images, 160 validation images, and 140 test images. Considering the limited dataset and the computational cost of training deep neural networks from scratch, we applied transfer learning technique to models pre-trained on the publicly available ImageNet benchmark dataset. We used PyTorch Deep Learning framework and both CPU and GPU to train ResNet18 CNN. The training on CPU took 1h21min55s with a test accuracy of 97.14% while the training on GPU took 1min47s with a test accuracy = 97.86%. We used Keras API that runs on top of TensorFlow to train a MobileNet model. The training using Colaboratory’s GPU took 1h32m14s with an accuracy of 98.57%. The deep CNN models provided surprisingly high accuracy missclassifying only a few of 140 testing images. The MobileNet model allowed to run the inference efficiently on mobile devices. The affordable and easy-to-use solution provides a viable new method of automated surface inspection systems (ASIS).