... a classification model. Specifically, we employ transfer learning by pre-training a classification network with sufficient amounts of simulated WBMs and then fine-tuning it with a few real-WBMs. We further employ ensemble learning to overcome the overfitting problem in transfer learning by fine-tuning multiple...

... of interest. We propose a novel approach to transfer learning, specifically designed for the fusion of diverse, unstructured 3D shape data and process inputs from multiple sources. The proposed approach has demonstrated remarkable results in predicting the functional properties of 3D-printed lattice...

... sometimes follow different distributions in real-world manufacturing environments as the diversity of industrialization processes leads to heterogeneous data collection under different production conditions. This will reduce the ability of decision-making with conventional approaches. Transfer learning (TL...

...@msstate.edu Email: sabbaghi@purdue.edu Email: ravishm@pitt.edu Email: ghasrikh@ualberta.ca Email: bian@ise.msstate.edu 19 07 2019 27 01 2020 14 02 2020 19 02 2020 additive manufacturing laser processes transfer learning distortion The objective...

.... This paper proposes a transfer learning (TL) framework to deal with these challenges by which the common information from a spatial model of an HDM-measured surface is transferred to a new surface where only low-resolution data are available. Under this framework, the paper develops and compares three...