Due to the growing trend towards miniaturization, small-scale manufacturing processes have become widely used in various engineering fields to manufacture miniaturized products. These processes generally exhibit complex size effects, making the behavior of materials highly dependent on their geometric dimensions. As a result, accurate understanding and modeling of such effects are crucial for optimizing manufacturing outcomes and achieving high-performance final products. To this end, advanced gradient-enhanced plasticity theories have emerged as powerful tools for capturing these complex phenomena, offering a level of accuracy significantly greater than that provided by classical plasticity approaches. However, these advanced theories often require the identification of a large number of material parameters, which poses a significant challenge due to limited experimental data at small scales and high computation costs. The present paper aims at evaluating and comparing the effectiveness of various optimization techniques, including evolutionary algorithm, response surface methodology and Bayesian optimization, in identifying the material parameter of a recent flexible gradient-enhanced plasticity model developed by the authors. The paper findings represent an attempt to bridge the gap between advanced material behavior theories and their practical industrial applications, by offering insights into efficient and reliable material parameter identification procedures.

由于小型化趋势的日益发展，小规模制造工艺已广泛应用于各个工程领域来制造小型化产品。这些过程通常表现出复杂的尺寸效应，使得材料的行为高度依赖于其几何尺寸。因此，准确理解此类效应并对其进行建模对于优化制造结果和实现高性能最终产品至关重要。为此，先进的梯度增强可塑性理论已成为捕捉这些复杂现象的强大工具，其精度明显高于经典可塑性方法。然而，这些先进理论往往需要识别大量的材料参数，由于小尺度的实验数据有限且计算成本高昂，这带来了巨大的挑战。本文旨在评估和比较各种优化技术（包括进化算法、响应面方法和贝叶斯优化）在识别作者最近开发的柔性梯度增强塑性模型的材料参数方面的有效性。该论文的研究结果表明，通过提供对高效可靠的材料参数识别程序的见解，试图弥合先进材料行为理论与其实际工业应用之间的差距。