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

TL;DR

This paper introduces a DTW-transfer learning framework that leverages polymer stress-strain data to efficiently predict metal behaviors in additive manufacturing, reducing testing costs and time.

Contribution

It presents a novel transfer learning approach using DTW to select the most similar polymer dataset for predicting metal stress-strain behaviors, improving accuracy and efficiency.

Findings

The framework achieves an average MAPE of 12.41%.

It outperforms vanilla LSTM and multi-source TL models.

Resin and Nylon are identified as optimal polymers for different metals.

Abstract

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.