论文标题
通过基于Calphad的ICME框架，对合金添加剂制造的不确定性量化和组成优化
Uncertainty Quantification and Composition Optimization for Alloy Additive Manufacturing Through a CALPHAD-based ICME Framework
论文作者
论文摘要
在粉末生产过程中，预先合金的粉末成分通常会偏离目标组成，从而导致添加剂制造（AM）组件的不良特性。因此，我们开发了一种方法，通过使用基于calphad的ICME框架（Calphad：相图：ICME：INTICETACTIAN COMPATINATION ENGICERITION的计算）来执行高通量计算和不确定性定量，以优化组合物，并接受高强度的低功能低合金钢（HSLA）。我们分析了围绕HSLA-115的名义组成的450,000个组成的过程结构 - 特质关系。评估对性能至关重要的性能，例如屈服强度，撞击过渡温度和可焊性，以优化组合物。由于与初始组成相同的不确定性，已经确定了优化的平均成分，这将成功AM构建的可能性增加了44.7％。目前的策略是一般的，可以应用于其他合金组成优化，以扩大合金选择添加剂制造的选择。这种方法还要求高质量的calphad数据库和预测性ICME模型。
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.
