数据驱动的差距桥梁性能优化和建模多针摩擦接EN AW 7020-T651关节的性能

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Generating Lap Joints Via Friction Stir Spot Welding on DP780 Steel

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Ramin Delir Nazarlou¹, Pouya Zarei¹, Samita Salim¹

¹Department for Cutting and Joining Manufacturing Processes, University of Kassel, 34125 Kassel, Germany.

Materials (Basel, Switzerland)

|February 13, 2026

概括

这项研究开发了一种数据驱动的框架,用于预测线间隙的合金中的摩擦冲动接 (FSW) 性能. 一个随机森林模型准确地确定了高质量,无缺陷关节的最佳参数.

科学领域:

材料科学与工程材料科学与工程
制造过程制造过程制造过程
计算材料科学科学计算材料科学

背景情况:

高强度合金的摩擦冲动接 (FSW) 具有线间隙的挑战,影响关节完整性.
优化FSW过程参数对于实现无缺陷接和保持机械性能至关重要.

研究的目的:

开发一个预测性,数据驱动的框架,用于评估和优化EN AW 7020-T651合金中FSW接头的差距弥合能力.
确定影响接质量的关键工艺参数,在不同间隙条件 (0-4毫米) 下.

主要方法:

一个结构化的实验矩阵系统地改变了旋转速度,接速度,轴向力和工具肩直径.
采用多针式工具来确保稳定的材料流和一致的接质量.
用无缺陷标准,最低终极抗拉强度 (230 MPa) 和从微硬度绘图中获得的新型弱面积百分比 (WAP) 度量来评估接质量.

主要成果:

随机森林机器学习模型在分类接可接受性方面实现了92.5%的准确性和0.90的F1得分.
特性重要性分析显示",接速度 × 间隙大小"和"旋转速度 × 间隙大小"的相互作用是接质量最重要的预测因素.
开发的框架成功地预测和优化了FSW在一系列差距条件中的性能.

结论:

使用机器学习的数据驱动方法有效地预测了线间隙的合金FSW中的接质量.
"接速度 × 间隙大小"和"旋转速度 × 间隙大小"的相互作用是控制接完整性的关键因素.
与传统方法相比,新的WAP指标提供了在受热影响区域中更强大的机械完整性评估.

关键词:

摩擦动接接弥合差距的能力弥合差距的能力模拟建模的模型.多针式多针式弱面积百分比弱面积百分比

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