中国机械工程 ›› 2026, Vol. 37 ›› Issue (6): 1272-1280.DOI: 10.3969/j.issn.1004-132X.2026.06.001

• 金属增材制造工艺及性能 • 上一篇    

直接能量沉积技术前沿:工艺创新和智能调控

伊浩1,2(), 张浩权1,2, 曹华军1,2   

  1. 1.重庆大学机械与运载工程学院, 重庆, 400030
    2.重庆大学高端装备机械传动全国重点实验室, 重庆, 400044
  • 收稿日期:2025-11-05 出版日期:2026-06-25 发布日期:2026-07-17
  • 通讯作者: 伊浩
  • 作者简介:伊浩*(通信作者),男,1988年生,副教授、博士研究生导师。研究方向为增材制造与绿色制造等。E-mail: haoyi@cqu.edu.cn
  • 基金资助:
    国家自然科学基金(52375306);重庆市自然科学基金(CSTB2025NSCQ-GPX0728);重庆市自然科学基金(CSTB2023NSCQ-LZX0137)

Frontiers of Direct Energy Deposition: Process Innovation and Intelligent Regulation

YI Hao1,2(), ZHANG Haoquan1,2, CAO Huajun1,2   

  1. 1.College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing,400030
    2.State Key Laboratory of Mechanical Transmission for Advanced Equipment,Chongqing;University,Chongqing,400044
  • Received:2025-11-05 Online:2026-06-25 Published:2026-07-17
  • Contact: YI Hao

摘要:

系统梳理了直接能量沉积技术的最新研究进展,重点围绕多热源复合、材料供给创新、能量场调控以及人工智能驱动四个关键发展方向展开深入探讨。在多热源复合方面,激光-电弧等复合工艺通过不同热源之间的协同作用与互补机制有效兼顾了加工效率与成形精度。在材料供给方面,绞丝、多丝及丝粉协同等新型送料模式显著拓展了功能梯度材料与多主元合金的制备能力,为提高构件综合性能提供了材料基础。在能量场调控方面,借助脉冲调制、路径规划以及多物理场耦合等手段,实现了对熔池动态行为与微观组织结构的精确调控。在人工智能驱动方面,机器学习与其他数据驱动技术的融合,推动了直接能量沉积技术在工艺参数优化、缺陷智能诊断以及数字孪生系统构建等方面的智能化转型。

关键词: 直接能量沉积, 多热源复合, 材料供给创新, 能量场调控, 人工智能驱动, 高性能金属构件

Abstract:

A systematic review of the latest research advances in DED technology was provided, focusing on four key developmental directions: multi-heat source hybridization, material supply innovation, energy field regulation, and AI-driven processes. Regarding multi-heat source hybridization, combined processes, such as laser-arc hybridization, the complementary mechanism between different heat sources were leveraged, effectively balancing processing efficiency and forming accuracy. Regarding material supply innovation, novel feeding approaches, including twisted wire, multi-wire, and wire-powder synergistic feeding, had significantly expanded the capability to fabricate functional gradient materials and multi-principal element alloys. It laid the material foundation for enhancing the performance of components overall. Regarding energy field regulation, several methods such as pulse modulation, path planning, and multi-physical field coupling achieved precise control over molten pool dynamics and microstructural evolution. Regarding AI driving, the integration of machine learning and other data-driven techniques expedited the intelligent evolution of DED technology in areas such as processing parameter optimization, intelligent defect diagnosis, and digital twin system development.

Key words: direct energy deposition(DED), multi-heat source hybridization, material supply innovation, energy field regulation, AI-driven, high-performance metal component

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