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Abstract In additive manufacturing of metal parts, the ability to accurately predict the extremely variable temperature field in detail, and relate it quantitatively to structure and properties, is a key step in predicting part performance and optimizing process design. In this work, a finite element simulation of the directed energy deposition (DED) process is used to predict the space- and time-dependent temperature field during the multi-layer build process for Inconel 718 walls. The thermal model results show good agreement with dynamic infrared images captured in situ during the DED builds. The relationship between predicted cooling rate, microstructural features, and mechanical properties is examined, and cooling rate alone is found to be insufficient in giving quantitative property predictions. Because machine learning offers an efficient way to identify important features from series data, we apply a 1D convolutional neural network data-driven framework to automatically extract the dominant predictive features from simulated temperature history. Very good predictions of material properties, especially ultimate tensile strength, are obtained using simulated thermal history data. To further interpret the convolutional neural network predictions, we visualize the extracted features produced on each convolutional layer and compare the convolutional neural network detected features of thermal histories for high and low ultimate tensile strength cases. A key result is the determination that thermal histories in both high and moderate temperature regimes affect material properties.
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Convolutional signature for sequential data
Abstract Signature is an infinite graded sequence of statistics known to characterize geometric rough paths. While the use of the signature in machine learning is successful in low-dimensional cases, it suffers from the curse of dimensionality in high-dimensional cases, as the number of features in the truncated signature transform grows exponentially fast. With the idea of Convolutional Neural Network, we propose a novel neural network to address this problem. Our model reduces the number of features efficiently in a data-dependent way. Some empirical experiments including high-dimensional financial time series classification and natural language processing are provided to support our convolutional signature model.
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- Award ID(s):
- 2008427
- PAR ID:
- 10365784
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Digital Finance
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2524-6984
- Page Range / eLocation ID:
- p. 3-28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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