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which show variations due to the optical hardware, types of specimen and imaging (sample preparation) protocols. Although there have been efforts utilizing unsupervised learning61,62,63,64,65,66,67and self-supervised learning16,68,69,70to alleviate the reliance on large-scale experimental training ...
Finally, considering that the specimen thickness Conclusions This paper presented a coupled data-physics computational framework for temperature, residual stress, and distortion prediction during autoclave curing processes. The novelties of the proposed framework include a dataset organization strategy and a...