无监督的深度学习用于板金属中高级成型极限分析:基于拉伸测试的方法
Aleksandra Thamm1, Florian Thamm1, Annette Sawodny2
1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058 Erlangen, Germany.
Materials (Basel, Switzerland)
|November 14, 2023
概括
本研究引入了一种机器学习方法,使用单轴拉伸试验和立体相机准确确定轻质材料和高强度钢的成型极限曲线. 该方法在预测材料可塑性方面表现有前途,在许多情况下与ISO标准保持一致.
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
- 计算力学 计算力学 计算力学
背景情况:
- 准确的金属板可塑性评估对于优化制造工艺至关重要.
- 形成极限曲线 (FLC) 的传统纳吉马测试由于摩擦和复杂的应变路径而存在局限性.
- 现有的方法可能低估了轻质材料和高强度钢的可塑性.
研究的目的:
- 开发和评估一种机器学习方法,使用单轴拉伸试验来确定成形极限曲线 (FLC).
- 适应卷积神经网络 (CNN) 在单轴拉伸测试中用于FLC预测.
- 评估基于立体摄像头的FLC确定方法的准确性和可转移性.
主要方法:
- 使用一个弱监督的卷积神经网络 (CNN),最初是为纳卡吉马测试设计的,适用于单轴拉伸测试.
- 开发了一个基于立体相机的系统,用于实时测量和分析应变.
- 使用AA6016,DX54D和DP800等材料训练和验证模型,采用交叉验证和代数据组成.
主要成果:
- 基于CNN立体摄像机的方法成功预测了各种材料和厚度的主要应变,显示了与ISO标准的密切一致.
- 对于DX54D (0.8mm和2.0mm) 和AA6016 (1.0mm) 的具体预测与ISO值密切匹配.
- 对于DP800 (1.0mm) 发现了差异,但总的来说,该方法与横截面方法的定量对齐.
结论:
- 机器学习,特别是CNN与立体相机测量相结合,为单轴拉伸试验中精确的FLC确定提供了可行的替代方案.
- 这种方法克服了传统方法的局限性,并显示了低估某些材料可塑性的潜力.
- 开发的方法为评估轻质和高强度钢的可塑性提供了定量和可靠的手段.
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