Automatizing Inspection and Defect Detection for Metal Additive Manufacturing Using Deep Learning Techniques
Metal 3D Printing, or Additive Manufacturing (AM), is a layer-by-layer manufacturing process that involves adding the material to create a structure instead of removing it. In contrast to conventional manufacturing techniques, AM allows for much faster, low-cost prototyping, the construction of the parts with complex geometries and overall reduction in material waste. However, AM is a complex process governed by many parameters, which are often inter-dependent and impacted by the manufacturing environment. Optimization of these parameters is crucial to ensure the high quality of the manufactured parts. Manual quality inspection of these parts is not only a time-consuming process but also depends on the skill of the quality control inspector. This project aims to utilize Deep Learning techniques, specifically Convolutional Neural Networks, to identify, localize and classify defects from cross-sectional image scans of parts manufactured via laser powder bed fusion to automate the defect detection and quality assessment for those parts.