用聚合物数据迁移金属3D打印件的力学性能预测，省钱省时。
A Dynamic Time Warping-Transfer Learning Approach to Transferring Knowledge in Stress-strain Behaviors from Polymers to Metals: An Affordable and Generalizable Additive Manufacturing Part Qualification Framework
- 通过动态时间规整选最像金属的聚合物数据，再用LSTM模型迁移学习。
- 对三种金属预测误差平均12.41%，决定系数达0.96，效果优于传统方法。
- 适合想低成本验证金属3D打印件性能的研究者和工程师。
增材制造（AM）零件合格性评估确保零件可稳定生产并可靠用于关键场景。其中关键环节是确定零件复杂的应力-应变行为。然而，传统方法如破坏性测试和无损检测成本高、耗时长，尤其适用于金属增材制造。为此，本文提出一种基于动态时间规整（DTW）与迁移学习（TL）相结合的框架，将低成本聚合物的应力-应变行为知识迁移到高性能、昂贵金属中。具体地，在多个聚合物数据集（Nylon、PLA、CF-ABS、Resin）中，利用DTW选出与目标金属数据集（AlSi10Mg、Ti6Al4V、碳钢）最相似的一个最优源数据集。随后，使用该最优聚合物数据集训练长短期记忆（LSTM）模型，并在三个金属数据集上测试。实验结果表明：对于AlSi10Mg和Ti6Al4V，Resin为最优源；对于碳钢，Nylon为最优源。基于单一最优聚合物数据训练的DTW-TL模型，在三类金属上的平均绝对百分比误差为12.41%，均方根误差为63.75，决定系数平均达0.96，显著优于无迁移学习的原始LSTM模型及使用全部四个聚合物数据训练的迁移学习模型。
原文摘要 · Abstract (English)
Part qualification in additive manufacturing (AM) ensures that additively manufactured parts can be consistently produced and reliably used in critical applications. One crucial aspect of part qualification is to determine the complex stress-strain behavior of additively manufactured parts. However, conventional part qualification techniques such as the destructive testing and non-destructive testing are costly and time consuming, especially for metal AM. To address this challenge, we develop a dynamic time warping (DTW)-transfer learning (TL) framework for AM part qualification by transferring knowledge gained from the stress-strain behaviors of additively manufactured low-cost polymers to high-performance, expensive metals. Specifically, the framework selects one single optimal polymer dataset that is the most similar to the metal dataset in the target domain using DTW among multiple polymer datasets, including Nylon, PLA, CF-ABS, and Resin. A long short-term memory (LSTM) model is then trained on one single optimal polymer dataset and tested on one of three target metal datasets, including AlSi10Mg, Ti6Al4V, and carbon steel datasets. Experimental results show that the Resin dataset is selected as the optimal polymer dataset in the source domain for the AlSi10Mg and Ti6Al4V datasets, while the Nylon dataset is selected as the optimal polymer dataset in the source domain for the carbon steel dataset. The DTWTL model trained on one single optimal polymer dataset as the source domain achieves the best predictive performance, including an average mean absolute percentage error of 12.41%, an average root mean squared error of 63.75, and an average coefficient of determination of 0.96 when three metals are used as the target domain, outperforming the vanilla LSTM model without TL as well as the TL model trained on all four polymer datasets as the source domain.
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