Scholarlink

<p>A Random Forest model is developed to predict single-pass GMAW bead width and height from current, voltage, travel speed, derived heat input/polynomial terms, and filler-wire chemistry using a database compiled from published cross-sections. Trained on four steel wires, the model achieves <i>R</i><sup>2</sup> = 0.97 for both targets. With only four ER70S-6 samples added, the transferred model reaches <i>R</i><sup>2</sup> = 0.98 (width) and 0.96 (height) without degrading accuracy on the original wires; the maximum absolute error is 1.21&#xa0;mm. Against a Box–Behnken response-surface model built from 17 experiments, the proposed approach delivers comparable or lower errors with far fewer new tests. Interpretability analyses (feature importance, decision paths, PCA/t-SNE, and cosine similarity) highlight heat input as the dominant driver and explain the cross-wire generalization.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Beyond data scarcity: achieving cross-wire generalization in single-pass gas metal arc welding through inter-material feature transfer learning

  • 期刊论文

  • 发表日期：

    2026年2月21日

查看全文
  • Wenguang Luo,
  • Yaonan He,
  • Zixuan Wen,
  • Zidong Lin,
  • Xinghua Yu

摘要

A Random Forest model is developed to predict single-pass GMAW bead width and height from current, voltage, travel speed, derived heat input/polynomial terms, and filler-wire chemistry using a database compiled from published cross-sections. Trained on four steel wires, the model achieves R2 = 0.97 for both targets. With only four ER70S-6 samples added, the transferred model reaches R2 = 0.98 (width) and 0.96 (height) without degrading accuracy on the original wires; the maximum absolute error is 1.21 mm. Against a Box–Behnken response-surface model built from 17 experiments, the proposed approach delivers comparable or lower errors with far fewer new tests. Interpretability analyses (feature importance, decision paths, PCA/t-SNE, and cosine similarity) highlight heat input as the dominant driver and explain the cross-wire generalization.