Real-time defect detection in 3D printing using machine learning

3D printing or additive manufacturing is one of the key aspects of industry 4.0. However, 3D printing technology has its vulnerabilities due to the defects that develop for various reasons. This project focuses to develop a Convolutional Neural Network (CNN)-Deep Learning model to detect real-time malicious defects to prevent the production losses and reduce human involvement for quality checks. The method proposed here is based on feature extraction of geometrical anomalies occurring in infill patterns due to inconsistent extrusion, weak infills, lack of supports, or sagging and compare it to the features of a perfect 3D print. This approach is built on the concepts of image classification and computer vision using machine learning, which is an extremely popular technology because of the availability of datasets, monitoring systems, and the ability to detect causal relationships of defects. To check the quality of the parts, an integrated camera with the 3D printer captures images at regular intervals and process it using the CNN model. The results of this project are a more optimized and automated 3D printing process with the potential to solve the most widespread problem of product variability in 3D printing. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This project aims to use a Convolutional Neural Network (CNN)-Deep Learning model to detect malicious defects in 3D printing, reducing production losses and human involvement in quality checks. By extracting geometric anomalies in infill patterns and comparing them to features of a perfect print, a automated quality checking system based on machine learning is established.