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<p>Coaxial wire feeding multi-beam laser metal deposition (CWM-LMD) addresses the bottlenecks of traditional lateral-feed processes, namely directional anisotropy and asymmetric energy coupling, through a beam-enveloping-wire architecture. This configuration provides deposition isotropy, high material utilization, and stable melt pool dynamics. This paper reviews CWM-LMD, categorizing optical system evolution from discrete beam-splitting to annular beam shaping. However, the multi-phase interaction between convergent beams and feedstock, which determines the thermodynamic stability of the melt pool, has received limited attention. This review then evaluates material-specific responses across titanium, superalloy, and reflective aluminum systems, linking thermal history to quasi-isotropic mechanical properties. The shift from open-loop parameter optimization to multi-sensor fusion and AI-driven closed-loop control is also analyzed, identifying perception-to-prediction latency as a primary hurdle for industrial zero-defect manufacturing. The review concludes by proposing a roadmap for functionally graded materials (FGMs) via multi-wire synergy and physics-informed neural networks (PINNs) to enable autonomous material-process design. This review provides a framework for transitioning CWM-LMD from a laboratory technique to an industrial standard.</p>

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A critical review of coaxial wire feeding multi-beam laser metal deposition: from optical evolution to AI-driven control

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Shichen Duan,
  • Peilei Zhang,
  • Liqiang Wang,
  • Chao Fang,
  • Qinghua Lu,
  • Hua Yan,
  • Haichuan Shi

摘要

Coaxial wire feeding multi-beam laser metal deposition (CWM-LMD) addresses the bottlenecks of traditional lateral-feed processes, namely directional anisotropy and asymmetric energy coupling, through a beam-enveloping-wire architecture. This configuration provides deposition isotropy, high material utilization, and stable melt pool dynamics. This paper reviews CWM-LMD, categorizing optical system evolution from discrete beam-splitting to annular beam shaping. However, the multi-phase interaction between convergent beams and feedstock, which determines the thermodynamic stability of the melt pool, has received limited attention. This review then evaluates material-specific responses across titanium, superalloy, and reflective aluminum systems, linking thermal history to quasi-isotropic mechanical properties. The shift from open-loop parameter optimization to multi-sensor fusion and AI-driven closed-loop control is also analyzed, identifying perception-to-prediction latency as a primary hurdle for industrial zero-defect manufacturing. The review concludes by proposing a roadmap for functionally graded materials (FGMs) via multi-wire synergy and physics-informed neural networks (PINNs) to enable autonomous material-process design. This review provides a framework for transitioning CWM-LMD from a laboratory technique to an industrial standard.