Scholarlink

<p>This research develops a surrogate-assisted framework for exploring mechanical property relationships in friction stir additive manufacturing (FSAM) of dissimilar aluminum alloys (AA6061/AA7075) under data-scarcity conditions. Using a limited experimental dataset of 9 data points, Radial Basis Function (RBF) interpolation generates 882 synthetic samples covering tool rotation speed (1000–1200&#xa0;rpm), traverse speed (30–40&#xa0;mm/min), and tilt angle (1.0–2.2°). Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Bayesian Ridge Regression models are trained to predict ultimate tensile strength (UTS) and Vickers hardness (HV) for both 6061-over-7075 and 7075-over-6061 material stacking configurations. GPR indicates performance with mean absolute errors of 2.34–8.67&#xa0;MPa for UTS and comparable accuracy for hardness, achieving R² &gt; 0.85 across synthetic samples. SHAP-based analysis of the surrogate-trained models indicates that tool rotation speed exerts the strongest influence within the learned surrogate response surfaces, followed by traverse speed and tilt angle, with relative contributions varying across outputs. Critically, this near zero error is obtained on synthetic samples derived from RBF interpolation of the original 9 measurements, not on independent experimental data. The models effectively learn and reproduce the RBF surrogate response surface within the explored design space. The framework successfully indicates a methodology for parameter space exploration under data scarcity; however, it illustrates conceptual parameter selection workflows based on surrogate-assisted trend exploration. Independent experimental validation is required before any practical optimization. The work provides a template applicable to other data-scarce manufacturing domains, with the essential caveat that external experimental validation is a prerequisite to practical application.</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

RBF-assisted surrogate modeling and machine learning for mechanical property prediction in friction stir additive manufacturing: Application to dissimilar AA6061/AA7075 aluminum alloys.

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月4日

查看全文
  • Krishnan Venkatachalam,
  • Senthil Kumaran Selvaraj,
  • Giriraj Mannayee,
  • Kalyanaraman Pattabiraman,
  • Sundaramali G

摘要

This research develops a surrogate-assisted framework for exploring mechanical property relationships in friction stir additive manufacturing (FSAM) of dissimilar aluminum alloys (AA6061/AA7075) under data-scarcity conditions. Using a limited experimental dataset of 9 data points, Radial Basis Function (RBF) interpolation generates 882 synthetic samples covering tool rotation speed (1000–1200 rpm), traverse speed (30–40 mm/min), and tilt angle (1.0–2.2°). Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Bayesian Ridge Regression models are trained to predict ultimate tensile strength (UTS) and Vickers hardness (HV) for both 6061-over-7075 and 7075-over-6061 material stacking configurations. GPR indicates performance with mean absolute errors of 2.34–8.67 MPa for UTS and comparable accuracy for hardness, achieving R² > 0.85 across synthetic samples. SHAP-based analysis of the surrogate-trained models indicates that tool rotation speed exerts the strongest influence within the learned surrogate response surfaces, followed by traverse speed and tilt angle, with relative contributions varying across outputs. Critically, this near zero error is obtained on synthetic samples derived from RBF interpolation of the original 9 measurements, not on independent experimental data. The models effectively learn and reproduce the RBF surrogate response surface within the explored design space. The framework successfully indicates a methodology for parameter space exploration under data scarcity; however, it illustrates conceptual parameter selection workflows based on surrogate-assisted trend exploration. Independent experimental validation is required before any practical optimization. The work provides a template applicable to other data-scarce manufacturing domains, with the essential caveat that external experimental validation is a prerequisite to practical application.