基于CALPHAD的ICME合金增材制造不确定性量化与成分优化
Uncertainty Quantification and Composition Optimization for Alloy Additive Manufacturing Through a CALPHAD-based ICME Framework
摘要：
在粉末生产过程中，预合金粉末组成通常偏离目标组成，导致增材制造（AM）部件的不期望的性质。因此，我们开发了一种基于CALPHAD的ICME框架（CALPHAD：calculations of phase diagrams，ICME：integrated computational materials engineering）进行高通量计算和不确定性量化的方法，以高强度低合金钢（HSLA）为例进行成分优化。我们分析了围绕HSLA-115的标称组成的450，000种组成的工艺-结构-性能关系。性能的关键性能，如屈服强度，冲击转变温度和可焊性，进行了评估，以优化组合物。在与初始组成相同的不确定性下，确定了优化的平均组成，这将实现成功的AM构建的概率提高了44.7%。本策略是通用的，并且可以应用于其他合金成分优化，以扩大用于增材制造的合金的选择。这种方法还需要高质量的CALPHAD数据库和ICME预测模型。
英文摘要：
During powder production, the pre-alloyed powder composition often deviates from the target composition leading to undesirable properties of additive manufacturing (AM) components. Therefore, we developed a method to perform high-throughput calculation and uncertainty quantification by using a CALPHAD-based ICME framework (CALPHAD: calculations of phase diagrams, ICME: integrated computational materials engineering) to optimize the composition, and took the high-strength low-alloy steel (HSLA) as a case study. We analyzed the process-structure-property relationships for 450,000 compositions around the nominal composition of HSLA-115. Properties that are critical for the performance, such as yield strength, impact transition temperature, and weldability, were evaluated to optimize the composition. With the same uncertainty as the initial composition, an optimized average composition has been determined, which increased the probability of achieving successful AM builds by 44.7%. The present strategy is general and can be applied to other alloy composition optimization to expand the choices of alloy for additive manufacturing. Such a method also calls for high-quality CALPHAD databases and predictive ICME models.
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