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<p>Cold metal transfer (CMT) welding has emerged as a transformative technology in metal joining, offering minimal heat input, reduced distortion, and superior deposition control. This review synthesizes experimental studies to evaluate CMT's capabilities in aerospace, automotive, and additive manufacturing applications. Key findings demonstrate that optimized parameters achieve dimensional accuracies within ± 0.2&#xa0;mm, heat-affected zones 40–60% narrower than conventional GMAW, and grain refinement (12–18&#xa0;μm) enhancing mechanical properties. Hybrid CMT-laser methods improve deposition rates by 15–30%, while numerical models predict thermal behavior with &gt; 90% accuracy. However, challenges persist in residual stress management for thick sections and standardized protocols for dissimilar materials. Future research must prioritize real-time monitoring, machine-learning-assisted optimization, and comprehensive testing standards to expand CMT's industrial adoption.</p>

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Comprehensive analysis of experimental studies in cold metal transfer welding

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月2日

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  • Mir Qurrat Ul Ain,
  • Deepak Kumar Naik,
  • Dinesh Kumar Rajendran,
  • Shiva Sekar

摘要

Cold metal transfer (CMT) welding has emerged as a transformative technology in metal joining, offering minimal heat input, reduced distortion, and superior deposition control. This review synthesizes experimental studies to evaluate CMT's capabilities in aerospace, automotive, and additive manufacturing applications. Key findings demonstrate that optimized parameters achieve dimensional accuracies within ± 0.2 mm, heat-affected zones 40–60% narrower than conventional GMAW, and grain refinement (12–18 μm) enhancing mechanical properties. Hybrid CMT-laser methods improve deposition rates by 15–30%, while numerical models predict thermal behavior with > 90% accuracy. However, challenges persist in residual stress management for thick sections and standardized protocols for dissimilar materials. Future research must prioritize real-time monitoring, machine-learning-assisted optimization, and comprehensive testing standards to expand CMT's industrial adoption.