Surface Fault Detection in 3D Printed Objects Using Deep Learning

Abstract

Abstract: Robots and machines in Industry 4.0 must be able to operate efficiently and autonomously. They should work in a way that makes the work efficient and substantially error-free. A key component of Industry 4.0, additive manufacturing, or 3D printing, is bringing forth a wave of innovation in daily life by creating everything from toys to furniture to screws. The enthusiast typically uses 3D printing. To create 3D objects for private or commercial usage, various printing techniques have been developed. These techniques include fused deposition modelling (FDM), stereolithography (SLA), digital light processing (DLP), and selective laser sintering (SLS). Despite the enormous potential for AM techniques to produce unique parts on demand and with little material waste, 3D printing is not typically used in mass production. This is due to two reasons. The cost of printing is too high, and since 3D printing is typically a labour-intensive process, it would not be practical to implement it in a mass production setting. Secondly, there is no quality assurance process other than manual inspection. By applying Deep Learning to automate the QC control component of 3D printing, we hope to solve the second issue. Our research demonstrates an in-situ monitoring strategy without causing damage that can find surface flaws in 3D-printed products. We suggest a deep learning-based method in this work to assess the quality of a 3D-printed object. Deep Convolution Neural Network and Deep Convolution Neural Network with Random Forest algorithm are the first two approaches used to do this.