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基于机器视觉的钛合金焊接过程非平衡凝固组织性能智能控制
Graphic Learning Enabled Intelligent Optimizations of the Non-equilibrium Solidified Microstructure and Properties of Welded Titanium Alloy
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- DOI:
- 作者:
- 汪欣朝1 杜坤2 王毅1,2 柴再先1 张嘉3 杨超3 陈利阳4 刘希林4 张志远4 孙峰5 唐斌1
WANG Xinzhao 1 , DU Kun 2 , WANG William Yi 1,2 , CHAI Zaixian 1 , ZHANG Jia 3 , YANG Chao 3 , CHEN Liyang 4 , LIU Xilin 4 , ZHANG Zhiyuan 4 , SUN Feng 5 , TANG Bin 1,2 , KOU Hongchao 1,2 , LI Jinshan
- 作者单位:
- 1. 西北工业大学凝固技术国家重点实验室 2. 西北工业大学重庆科创中心 3. 西部超导材料科技股份有限公司 4. 中国船舶集团有限公司第七二五研究所 5. 重庆两航金属材料有限责任公司
1. StateKeyLaboratoryofSolidificationProcessing,NorthwesternPolytechnicalUniversity,Xi'an710072,China;2. Innovation Center NPU, Chongqing 401135, China; 3. Western Superconducting Technologies Co., Ltd., Xi'an 710018, China; 4. Luoyang Ship Material Research Institute, Luoyang 471000, China; 5. Ti-MAST High Performance Alloy Co., Ltd., Chongqing 401135, China
- 关键词:
- 非平衡凝固;钛合金;焊接;机器视觉;机器学习;
non-equilibrium solidification; titanium alloy; welding; machine vision; machine learning
- 摘要:
- 信息技术的发展使制造业逐步向“智造”转变。人工智能技术已成为精密铸造、焊接和增材制造等制造工艺由控形走向控性的共性关键技术。在这些工艺中非平衡凝固组织性能调控机理是限制其发展的基础科学问题。基于先进的人工智能技术发展控形控性一体化技术、构建完善的工艺质量体系对推动铸造、焊接、增材制造等制造工艺迈上新的台阶至关重要。与传统的物理冶金和铸造相比,增材制造、焊接、激光熔覆修复、单晶生长等过程的核心理念是“控制微区冶金过程”,即通过控制温度梯度、凝固速率、熔池尺度等关键工艺参量的作用机制,从微观上揭示非平衡条件下固液两相区组织结构和形貌演化规律,特别是合金元素的偏聚行为,界面的传热和传质特性、形核与长大、柱状晶-等轴晶转变、枝晶的竞争生长等。集成计算和实验方法的结合将为智能制造、增材智造和太空智造中先进金属材料的成分-工艺-组织-性能调控提供共性技术和理论支撑。本文以典型的焊接中非平衡凝固组织性能调控为研究方向,对近年来基于视觉传感的焊接调控技术取得的研究成果和进展进行了梳理。归纳了面向复杂工业环境的智能焊接关键技术、先进应用和技术挑战等,展示了基于数字孪生车间的钛合金焊接视觉学习结果。
Manufacturing is transitioning into the “ intelligent manufacturing ” paradigm accelerated by the development of
information technologies. Artificial intelligence has become one kind of key generic technologyfrom shape control to
performance control for a wide variety of manufacturing processes, including precision casting, welding, additive
manufacturing, and so on. Moreover, the controlling mechanisms based on the non-equilibrium solidified microstructure and
properties are the fundamental and scientific problems to be addressed. It is essential to develop the integrated technology
that controls the shape and the performance spontaneously and build a perfect process quality system via advanced artificial
intelligence technology to enhance casting, welding, additive manufacturing, and other manufacturing processes towards a more advanced level. Compared with traditional physical metallurgy and solidification, the key concept of controlling the
micro-area metallurgical process reveals that processes, such as additive manufacturing, welding, laser cladding repair, and
single crystal growth, can reveal the microstructure always dominated mechanisms related and morphology evolution laws
of solid-liquid two-phase regions under non-equilibrium conditions. In particular, the segregation behavior of alloy elements,
heat transfer and mass transfer characteristics of the interface, nucleation, growth, columnar to equiaxed transition,
competitive growth of dendrites, etc, are always dominated by mechanisms related to several processing parameters, such as
temperature gradients, solidification rates, and molten pool scales. The integration of calculation and experimental methods
will provide common technical and theoretical support for the composition-process-structure-performance regulation of
advanced metal materials in intelligent manufacturing, additive intelligent manufacturing, and space intelligent
manufacturing. The present paper is concerned with the control of the non-equilibrium solidification structure and the
properties of typical welding. Recent results revealing the advances in welding control technology based on visual detection
are reviewed and discussed. Correspondingly, the state-of-the-art applications and technical challenges of intelligent welding
in complex industrial environments are summarized, highlighting the graphic learning enabled intelligent optimizations of
the non-equilibrium solidified microstructure and properties of welded titanium alloy as a case study of digital twin
workshop.