Scholarlink

<p>Accurate prediction of mechanical properties in gas tungsten arc welding (GTAW) remains challenging due to the complex, nonlinear relationships between process parameters and weld quality. This study introduces a novel framework that systematically evaluates seven state-of-the-art metaheuristic algorithms: spider wasp optimizer (SWO), weighted mean of vectors (INFO), gradient-based optimizer (GBO), artificial rabbits optimization (ARO), blood-sucking leech optimizer (BSLO), RUN beyond the metaphor (RUN), and successive history adaptive differential evolution (SHADE), for training artificial neural networks (ANNs) to predict ultimate tensile strength in GTAW of Inconel 825 alloy. The primary novelty lies in identifying the gradient-based optimizer as the most effective algorithm for this application, presenting superior generalization capability and establishing a new benchmark for welding parameter prediction. The optimized ANN-GBO model achieved significant performance improvements over conventional ANN approaches, with the coefficient of determination (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation>) increasing from 0.6844 to 0.8669 (26.7% improvement) and root mean square error (RMSE) decreasing from 51.89&#xa0;MPa to 33.71&#xa0;MPa (35.0% reduction). These substantial enhancements in prediction accuracy provide critical insights for optimizing high-performance nickel-based alloy welding processes.</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

An improved artificial neural network using gradient-based optimizer for precise ultimate tensile strength prediction in GTAW of Inconel 825

  • 期刊论文

  • 发表日期：

    2026年1月4日

查看全文
  • Brahim Boucetta,
  • Faiza Boumediene,
  • Mohamed Abdessamed Ait Chikh,
  • Adel Afia

摘要

Accurate prediction of mechanical properties in gas tungsten arc welding (GTAW) remains challenging due to the complex, nonlinear relationships between process parameters and weld quality. This study introduces a novel framework that systematically evaluates seven state-of-the-art metaheuristic algorithms: spider wasp optimizer (SWO), weighted mean of vectors (INFO), gradient-based optimizer (GBO), artificial rabbits optimization (ARO), blood-sucking leech optimizer (BSLO), RUN beyond the metaphor (RUN), and successive history adaptive differential evolution (SHADE), for training artificial neural networks (ANNs) to predict ultimate tensile strength in GTAW of Inconel 825 alloy. The primary novelty lies in identifying the gradient-based optimizer as the most effective algorithm for this application, presenting superior generalization capability and establishing a new benchmark for welding parameter prediction. The optimized ANN-GBO model achieved significant performance improvements over conventional ANN approaches, with the coefficient of determination ( \(R^2\) ) increasing from 0.6844 to 0.8669 (26.7% improvement) and root mean square error (RMSE) decreasing from 51.89 MPa to 33.71 MPa (35.0% reduction). These substantial enhancements in prediction accuracy provide critical insights for optimizing high-performance nickel-based alloy welding processes.