用机器学习提升材料制造实验效率，比传统方法更准更快。
Enhancing Experimental Efficiency in Materials Design: A Comparative Study of Taguchi and Machine Learning Methods
- 结合高斯过程与主动学习，动态优化实验参数采样。
- 在15组测试中，机器学习模型预测误差更低，耗时更少。
- 适合需要高效探索复杂工艺参数的材料研发人员。
材料设计常需优化多个变量，全因子实验难以实施。传统实验设计（DOE）如田口法虽能高效采样，但无法捕捉变量间的非线性关系。本文对比田口法与基于主动学习的高斯过程回归（GPR）模型在丝弧增材制造（WAAM）中的表现，用于精准预测焊缝深度、宽度和高度。田口法采用三因素五水平的L25正交数组确定焊接参数，而GPR模型结合不确定性驱动的探索策略与拉丁超立方采样生成初始数据。在15组测试案例中，GPR在准确性和效率上均优于田口法。该方法可推广至其他需高效探索复杂参数的材料加工领域。
原文摘要 · Abstract (English)
Materials design problems often require optimizing multiple variables, rendering full factorial exploration impractical. Design of experiment (DOE) methods, such as Taguchi technique, are commonly used to efficiently sample the design space but they inherently lack the ability to capture non-linear dependency of process variables. In this work, we demonstrate how machine learning (ML) methods can be used to overcome these limitations. We compare the performance of Taguchi method against an active learning based Gaussian process regression (GPR) model in a wire arc additive manufacturing (WAAM) process to accurately predict aspects of bead geometry, including penetration depth, bead width, and height. While Taguchi method utilized a three-factor, five-level L25 orthogonal array to suggest weld parameters, the GPR model used an uncertainty-based exploration acquisition function coupled with latin hypercube sampling for initial training data. Accuracy and efficiency of both models was evaluated on 15 test cases, with GPR outperforming Taguchi in both metrics. This work applies to broader materials processing domain requiring efficient exploration of complex parameters.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。