Sevšek Luka, Pepelnjak Tomaž
Forming Laboratory, Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva 6, 1000 Ljubljana, Slovenia.
Materials (Basel). 2024 Nov 8;17(22):5459. doi: 10.3390/ma17225459.
Flexibility is crucial in forming processes as it allows the production of different product shapes without changing equipment or tooling. Single-point incremental forming (SPIF) provides this flexibility, but often results in excessive sheet metal thinning. To solve this problem, a pre-forming phase can be introduced to ensure a more uniform thickness distribution. This study represents advances in this field by developing a generalised approach that uses a multilayer perceptron artificial neural network (MLP ANN) to predict thinning results from the input parameters and employs a genetic algorithm (GA) to optimise these parameters. This study specifically addresses advanced high-strength steels (AHSSs) and provides insights into their formability and the optimisation of the forming process. The results demonstrate the effectiveness of the proposed method in minimising sheet metal thinning and represent a significant advance in flexible forming technologies applicable to a wide range of materials and industrial applications.
在成型过程中，灵活性至关重要，因为它允许在不更换设备或模具的情况下生产不同的产品形状。单点增量成型（SPIF）提供了这种灵活性，但常常会导致钣金过度变薄。为了解决这个问题，可以引入预成型阶段以确保更均匀的厚度分布。本研究通过开发一种通用方法取得了该领域的进展，该方法使用多层感知器人工神经网络（MLP ANN）根据输入参数预测变薄结果，并采用遗传算法（GA）来优化这些参数。本研究专门针对先进高强度钢（AHSS），并深入了解其可成型性以及成型工艺的优化。结果证明了所提出方法在最小化钣金变薄方面的有效性，并代表了适用于广泛材料和工业应用的柔性成型技术的重大进展。
