Unsupervised clustering approach for recognizing residual stress and distortion patterns for different parts for directed energy deposition additive manufacturing

Data obtained from additive manufactured components can be analyzed to gain a better understanding of the manufacturing physics and to improve the quality of the parts. However, additive manufacturing is a complex process with many sensitivities. Machine learning has recently been used in additive manufacturing to model and evaluate processes. It is not possible to provide a single regression model to predict mechanical behavior as the properties will vary based on the component geometry and in regions within a component. To provide a separate regression model for each region, it is better to categorize several regions for multiple geometries by their post- fabrication properties (maximum and minimum principal stresses and distortion). Clustering is a method for analyzing the quality of parts with similar characteristics in diverse areas. Twenty-three distinct geometries with numerous geometric characteristics, each experiencing a different history of heat during fabrication (due to their thickness and material distribution), are analyzed in this study. Three different clustering methods are employed (self-organizing map, k -means clustering, and fuzzy c -means clustering) . The results are presented in two parts. For the first case, a localized approach is taken, where a comprehensive data set is utilized. The observed maximum coefficient of variance is 0.07969. For the second case, each shape is considered as an instance (sample) with general geometric characteristics. Similar trends between different clustering methods were extracted for the global approach, highlighting the potential of this method, but the clustering results are dependent on the clustering method. According to these results, the analysis of the local data provides a deeper understanding of post-fabrication properties clustering. Additional geometric-based characteristics will be developed to refine and improve the global approach.