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<p>The mechanical integrity of welded joints is fundamentally determined by their strength and toughness, which directly affect the reliability, safety, and service life of welded structures. This research develops an advanced predictive framework for evaluating the mechanical properties of aluminum alloy joints produced using laser-cold metal transfer (CMT) hybrid welding technology. The methodology employs a third-order mathematical approach enhanced through ridge optimization techniques. Systematic evaluation of multiple computational approaches from first through tenth-order complexity identified the third-order formulation as optimal for capturing the nonlinear relationships between process parameters and joint performance. To address overfitting challenges, two regularization strategies were investigated: L1 penalty methods (Least Absolute Shrinkage and Selection Operator) and L2 penalty methods (ridge optimization). The ridge optimization approach with a regularization parameter α = 0.1 demonstrated superior performance. Experimental validation revealed that the optimized framework achieved exceptional predictive accuracy, with a correlation coefficient of 0.9503 and a root mean square error of 0.0549 on independent test data. These results significantly surpassed both conventional third-order approaches (correlation coefficient = 0.7890, root mean square error = 0.0686) and L1 penalty methods (correlation coefficient = 0.8890, root mean square error = 0.0573). The proposed methodology provides a robust, efficient tool for intelligent quality assessment in welding applications, offering substantial potential for industrial implementation.</p>

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Application of a ridge regression–optimized polynomial model for predicting the strength and toughness of laser–CMT hybrid welds in 7075 aluminum alloy

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Xiaocui Wu,
  • Changjun Liu,
  • Mingkui Dai,
  • Hao Zhang,
  • Jinxin Xue,
  • Yong Niu,
  • Xiyue Du,
  • Ting Zou

摘要

The mechanical integrity of welded joints is fundamentally determined by their strength and toughness, which directly affect the reliability, safety, and service life of welded structures. This research develops an advanced predictive framework for evaluating the mechanical properties of aluminum alloy joints produced using laser-cold metal transfer (CMT) hybrid welding technology. The methodology employs a third-order mathematical approach enhanced through ridge optimization techniques. Systematic evaluation of multiple computational approaches from first through tenth-order complexity identified the third-order formulation as optimal for capturing the nonlinear relationships between process parameters and joint performance. To address overfitting challenges, two regularization strategies were investigated: L1 penalty methods (Least Absolute Shrinkage and Selection Operator) and L2 penalty methods (ridge optimization). The ridge optimization approach with a regularization parameter α = 0.1 demonstrated superior performance. Experimental validation revealed that the optimized framework achieved exceptional predictive accuracy, with a correlation coefficient of 0.9503 and a root mean square error of 0.0549 on independent test data. These results significantly surpassed both conventional third-order approaches (correlation coefficient = 0.7890, root mean square error = 0.0686) and L1 penalty methods (correlation coefficient = 0.8890, root mean square error = 0.0573). The proposed methodology provides a robust, efficient tool for intelligent quality assessment in welding applications, offering substantial potential for industrial implementation.