Abstract
Additive manufacturing (AM), fundamentally different from traditional subtractive manufacturing techniques, is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. While convolutional neural network (CNN) has already been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes, a network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the detection of good deposition quality and defects such as lack of fusion, gas porosity, and cracks in a fusion-based AM process, a CNN architecture is presented comparing the classification report and evaluation of different architectural settings and obtaining the optimized result from them. The performance of the network was also compared with the results from the previous study. The overall accuracy (98%) for both training and testing the CNN network presented in this work transcends the current state of the art (92%) for AM defect detection.
