A Data-Driven Approach for Process Optimization of Metallic Additive Manufacturing Under Uncertainty
A Data-Driven Approach for Process Optimization of Metallic Additive Manufacturing Under Uncertainty
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DOI：
10.1115/1.4043798
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发表时间：
2019-06
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影响因子：
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通讯作者：
Zhuo Wang;Pengwei Liu;Yaohong Xiao;X. Cui;Zhen Hu;Lei Chen
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作者：
Zhuo Wang;Pengwei Liu;Yaohong Xiao;X. Cui;Zhen Hu;Lei Chen
金属增材制造 (AM) 工艺中存在各种不确定性源，阻碍了生产始终如一的高质量增材制造产品。本文以 Ti-6Al-4V 的电子束熔炼 (EBM) 为例，提出了一种使用物理信息计算机模拟模型来优化工艺参数的数据驱动框架。目标是确定稳健的制造条件，使我们能够在不确定的情况下不断获得等轴材料微观结构。为了克服不确定性下稳健设计优化的计算挑战，基于经过验证的高保真多物理场 AM 仿真模型的仿真数据构建了两级数据驱动的替代模型。稳健的设计结果表明低预热温度、低光束功率和中等扫描速度的结合，使得能够重复生产等轴结构产品，正如基于物理的模拟所证明的那样。最佳设计点的全局敏感性分析表明，在所研究的六个噪声因素中，比热容和晶粒生长活化能对微观结构变化的影响最大。通过这一示例性工艺优化，当前的研究还证明了所提出的方法在促进其他复杂的增材制造工艺优化方面的巨大潜力，例如在孔隙率控制或直接机械性能控制方面的稳健设计。
The presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6Al-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multiphysics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power, and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have the largest impact on the microstructure variation. Through this exemplar process optimization, the current study also demonstrates the promising potential of the presented approach in facilitating other complicate AM process optimizations, such as robust designs in terms of porosity control or direct mechanical property control.