Deep-Learning Quantitative Structural Characterization in Additive Manufacturing
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Abstract (arXiv)
With a goal of accelerating fabrication of additively manufactured components with precise microstructures, we developed a method for structural characterization of key features in additively manufactured materials and parts. The method utilizes deep learning based on an image-to-image translation conditional Generative Adversarial Neural Network architecture and enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing induced defects visible in etched optical images. These structural details are heterogeneous in nature. Our method specifies the microstructure state of an additive built via statistical distribution of structural details, based on an ensemble of collected images. Extensions of the method are proposed to address Artificial Intelligence implementation of developed machine learning model for in real time control of additive manufacturing.
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References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2022Generative Adversarial Networkscited by 5,603
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
- 2022MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learningcited by 19
- 2019Accurate prediction of melt pool shapes in laser powder bed fusion by the non-linear temperature equation including phase changes - isotropic versus anisotropic conductivitycited by 2
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