In material science, models are derived to predict emergent material properties (e.g. elasticity, strength, conductivity) and their relations to processing conditions. A major drawback is the calibration of model parameters that depend on processing conditions. Currently, these parameters must be optimized to fit measured data since their relations to processing conditions (e.g. deformation temperature, strain rate) are not fully understood. We present a new approach that identifies the functional dependency of calibration parameters from processing conditions based on genetic programming. We propose two (explicit and implicit) methods to identify these dependencies and generate short interpretable expressions. The approach is used to extend a physics-based constitutive model for deformation processes. This constitutive model operates with internal material variables such as a dislocation density and contains a number of parameters, among them three calibration parameters. The derived expressions extend the constitutive model and replace the calibration parameters. Thus, interpolation between various processing parameters is enabled. Our results show that the implicit method is computationally more expensive than the explicit approach but also produces significantly better results.
翻译:在材料科学方面,为预测突发物质特性(例如弹性、强度、导电性)及其与处理条件的关系,将模型推导出各种模型,主要缺点是校准取决于加工条件的模型参数。目前,这些参数必须优化,以适应测量数据,因为它们与处理条件(例如变形温度、压力率)的关系不完全理解。我们提出了一个新办法,确定根据基因编程处理条件校准参数的功能依赖性。我们提出了两种(明确和隐含的)方法,以确定这些依赖性并产生可解释的简短表达方式。这个方法用来扩大基于物理的变形过程组成模型。这个构件模型使用内部材料变量,例如变形密度,并包含若干参数,其中包括三个校准参数。衍生的表达方式扩展了构件模型,取代了校准参数。因此,可以对各种处理参数进行相互调试算。我们的结果表明,隐含方法在计算上比明显的方法更昂贵,但也产生更好的结果。