3 research outputs found
Using Machine Learning Techniques to Model the Process-Structure-Property Relationship in Additive Manufacturing
Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is improving the quality of the fabricated parts. While there are several ways of approaching this problem, developing data-driven methods that use AM process signatures to identify these part anomalies can be rapidly applied to improve the overall part quality during the build. The objective of this dissertation is to model multiple processes within the AM to quantify the quality of the parts and reduced the uncertainty due to variation in input process parameters. The objective of first study is to build a new layer-wise process signature model to characterize the thermal-defect relationship. Based on melt pool images, we propose novel layer-wise key process signatures, which are calculated using multilinear principal component analysis (MPCA) and are directly correlated with layer-wise quality of the part. Second study broadens the spectrum of the dissertation to include mechanical properties, where a novel two-phase modeling methodology is proposed for fatigue life prediction based on in-situ monitoring of thermal history. In final study, our objective is to pave the way toward a better understanding of the uncertainty in the process-defect-structures relationship using an inverse robust design exploration method. The method involves two steps. In the first step, mathematical models are developed to characterize and model the forward flow of information in the intended additive manufacturing process. In the second step, inverse robust design exploration is carried out to investigate satisfying design solutions that meet multiple AM goals
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Layer-Wise Profile Monitoring of Laser-Based Additive Manufacturing
Additive manufacturing (AM) is a novel fabrication technique capable of producing highly
complex parts. Nevertheless, a major challenge is improving the quality of fabricated parts. While
there are a number of ways of approaching this problem, developing data-driven methods that use
AM process signatures to identify these part anomalies can be rapidly applied to improve overall
part quality during build. The objective of this study is to build a new layer-wise process signature
model to create the thermal-microstructure relationship. In this study, we derive novel key process
signatures for each layer (from melt pool thermal images), which are reduced using multilinear
principal component analysis (MPCA) and are directly correlated with layer-wise quality of the
part. Using these key process signatures, a Gaussian SVM classifier model is trained to detect the
existence of anomalies inside a layer. The proposed models are validated through a case study of
real-world direct laser deposition experiment where the layer-wise quality of the part is predicted
on the fly. The accuracy of the predictions is calculated using three measures (recall, precision,
and f-score), showing reasonable success of the proposed methodology in predicting layer-wise
quality. The ability to predict layer-wise quality enables process correction to eliminate anomalies
and to ultimately improve the quality of the fabricated part.Mechanical Engineerin