eXplainable artificial intelligence for automatic defect detection in additively manufactured parts using CT scan analysis - Harsh Bordekar; Nicola Cersullo; Marco Brysch; Jens Philipp; Christian Hühne (Journal of Intelligent Manufacturing, 2023)
Additive Manufacturing (AM) and in particular has gained significant attention due to its capability to produce complex geometries using various materials, resulting in cost and mass reduction per part. However, metal AM parts often contain internal defects inherent to the manufacturing process. Non-Destructive Testing (NDT), particularly Computed Tomography (CT), is commonly employed for defect analysis. Today adopted standard inspection techniques are costly and time-consuming, therefore an automatic approach is needed. This paper presents a novel eXplainable Artificial Intelligence (XAI) methodology for defect detection and characterization. To classify pixel data from CT images as pores or inclusions, the proposed method utilizes Support Vector Machine (SVM), a supervised machine learning algorithm, trained with an Area Under the Curve (AUC) of 0.94. Density-Based Spatial Clustering with the Application of Noise (DBSCAN) is subsequently applied to cluster the identified pixels into separate defects, and finally, a convex hull is employed to characterize the identified clusters based on their size and shape. The effectiveness of the methodology is evaluated on Ti6Al4V specimens, comparing the results obtained from manual inspection and the ML-based approach with the guidance of a domain expert. This work establishes a foundation for automated defect detection, highlighting the crucial role of XAI in ensuring trust in NDT, thereby offering new possibilities for the evaluation of AM components.
Keywords: 620; Additive Manufacturing (AM); Laser-Powder Bed Fusion (L-PBF); Non Destructive Testing (NDT); Computed Tomography (CT); Machine Learning (ML); EXplainable Artificial Intelligence (XAI); Internal defects