基于仿生机器学习的增材摩擦搅拌沉积铝合金壁结构力学性能预测方法
Biomimetic Machine Learning approach for prediction of mechanical properties of Additive Friction Stir Deposited Aluminum alloys based walled structures
AI总结：
本研究提出结合AFSD工艺数值建模与遗传算法优化的仿生机器学习方法，基于五种铝合金的200个模拟样本预测其壁结构力学性能，GA-RF模型预测精度最优，为AFSD工艺优化提供了有效工具。
摘要：
本研究提出一种利用仿生机器学习预测增材摩擦搅拌沉积（Additive Friction Stir Deposited, AFSD）铝合金壁结构力学性能的新方法。该研究将AFSD工艺的数值建模与遗传算法优化的机器学习模型相结合，用于预测冯·米塞斯应力和对数应变。研究采用有限元分析模拟了AA2024、AA5083、AA5086、AA7075和AA6061五种铝合金的AFSD工艺，捕捉了复杂的热相互作用和力学相互作用，并从这些模拟中生成了包含200个样本的数据集。随后，研究构建了经遗传算法优化的决策树（Decision Tree, DT）和随机森林（Random Forest, RF）回归模型，用于预测关键力学性能。其中GA-RF模型在预测冯·米塞斯应力（R平方=0.9676）和对数应变（R平方=0.7201）两方面均表现出更优性能。这种创新方法为理解和优化多铝合金的AFSD工艺提供了有力工具，也为不同工艺参数下的材料行为研究提供了思路。
英文摘要：
This study presents a novel approach to predicting mechanical properties of Additive Friction Stir Deposited (AFSD) aluminum alloy walled structures using biomimetic machine learning. The research combines numerical modeling of the AFSD process with genetic algorithm-optimized machine learning models to predict von Mises stress and logarithmic strain. Finite element analysis was employed to simulate the AFSD process for five aluminum alloys: AA2024, AA5083, AA5086, AA7075, and AA6061, capturing complex thermal and mechanical interactions. A dataset of 200 samples was generated from these simulations. Subsequently, Decision Tree (DT) and Random Forest (RF) regression models, optimized using genetic algorithms, were developed to predict key mechanical properties. The GA-RF model demonstrated superior performance in predicting both von Mises stress (R square = 0.9676) and logarithmic strain (R square = 0.7201). This innovative approach provides a powerful tool for understanding and optimizing the AFSD process across multiple aluminum alloys, offering insights into material behavior under various process parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
