Scholarlink

<p>The focus of this research is the integration of friction stir welding (FSW) and friction stir processing (FSP) to enhance the ultimate tensile strength (UTS) of aluminium 2024 (AA2024) reinforced with multiwalled carbon nanotubes (CNTs) and to predict UTS using machine learning (ML) techniques. Tensile strength increased from 476 MPa to 780 MPa after processing and optimal parameters of tool rotation speed (TRS), feed rate (FR), groove depth (GD), and tilt angle (TA) were determined using the Taguchi method. Traditional statistical methods, such as linear regression, were less effective in capturing nonlinear relationships, whereas ML models, including random forest regression, gradient boosting, TPOT regression, and neural networks, provided more accurate predictions.</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

Machine learning approach for the prediction of tensile strength of carbon nanotubes reinforced AA2024 by friction stir welding and friction stir processing

  • 期刊论文

  • 发表日期：

    2026年2月11日

查看全文
  • Tarran Sidhaarth,
  • Pranay Anandbabu Obla,
  • Lokeshkumar Ramasamy,
  • Senthil Kumaran Selvaraj

摘要

The focus of this research is the integration of friction stir welding (FSW) and friction stir processing (FSP) to enhance the ultimate tensile strength (UTS) of aluminium 2024 (AA2024) reinforced with multiwalled carbon nanotubes (CNTs) and to predict UTS using machine learning (ML) techniques. Tensile strength increased from 476 MPa to 780 MPa after processing and optimal parameters of tool rotation speed (TRS), feed rate (FR), groove depth (GD), and tilt angle (TA) were determined using the Taguchi method. Traditional statistical methods, such as linear regression, were less effective in capturing nonlinear relationships, whereas ML models, including random forest regression, gradient boosting, TPOT regression, and neural networks, provided more accurate predictions.