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<p>This study investigates the effect of cold metal transfer (CMT) welding parameters weld current, welding speed, and shielding gas flow rate on the mechanical and microstructural properties of dissimilar stainless steels SS304L and SS439. Using response surface methodology, the welding parameters were optimized to maximize ultimate tensile strength (UTS), elongation (EL%), and microhardness (MH). The optimal parameters 121.66 A current, 7.06&#xa0;mm/sec welding speed, and 16&#xa0;l/min flow rate yielded a UTS of 231.088&#xa0;MPa, EL of 13.31%, and microhardness of 77.29 HV, with a desirability score of 0.870. This study also employed artificial neural networks (ANN) to predict welding outcomes based on the optimized parameters, achieving high accuracy for output parameters. The ANN models further validated the effectiveness of the parameter optimization, minimizing the need for extensive experimental trials and enhancing the reliability of the predictions. Microstructural analysis revealed significant grain coarsening at higher heat inputs in both the weld bead and heat-affected zone, while lower heat inputs resulted in finer grains. Scanning electron microscopy fracture analysis showed ductile behavior with uniform dimples at lower heat inputs, and mixed ductile-shear fractures with coarser dimples at higher heat inputs. Residual stress analysis indicated that higher current and slower welding speeds increased stress and distortion, while optimized parameters reduced these effects This research highlights the importance of optimizing CMT parameters for improved weld quality, offering valuable insights for industries such as automotive, chemical processing, and marine, where high-performance dissimilar metal welding is essential.</p>

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Machine Learning Prediction and Optimization of Cold Metal Transfer Welding Parameters for Enhancing the Mechanical and Microstructural Properties of Austenitic-Ferritic Stainless-Steel Joints

  • 期刊论文

  • 发表日期：

    2025年2月14日

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  • R. Ravikumar,
  • A. Mathivanan

摘要

This study investigates the effect of cold metal transfer (CMT) welding parameters weld current, welding speed, and shielding gas flow rate on the mechanical and microstructural properties of dissimilar stainless steels SS304L and SS439. Using response surface methodology, the welding parameters were optimized to maximize ultimate tensile strength (UTS), elongation (EL%), and microhardness (MH). The optimal parameters 121.66 A current, 7.06 mm/sec welding speed, and 16 l/min flow rate yielded a UTS of 231.088 MPa, EL of 13.31%, and microhardness of 77.29 HV, with a desirability score of 0.870. This study also employed artificial neural networks (ANN) to predict welding outcomes based on the optimized parameters, achieving high accuracy for output parameters. The ANN models further validated the effectiveness of the parameter optimization, minimizing the need for extensive experimental trials and enhancing the reliability of the predictions. Microstructural analysis revealed significant grain coarsening at higher heat inputs in both the weld bead and heat-affected zone, while lower heat inputs resulted in finer grains. Scanning electron microscopy fracture analysis showed ductile behavior with uniform dimples at lower heat inputs, and mixed ductile-shear fractures with coarser dimples at higher heat inputs. Residual stress analysis indicated that higher current and slower welding speeds increased stress and distortion, while optimized parameters reduced these effects This research highlights the importance of optimizing CMT parameters for improved weld quality, offering valuable insights for industries such as automotive, chemical processing, and marine, where high-performance dissimilar metal welding is essential.