基于DCNN的缺陷的语义细分及其对SLM Ti-6Al-4V合金疲劳寿命预测的应用
Jinchao Pan1, Dianyin Hu2,3,4, Liucheng Zhou5
1School of Energy and Power Engineering, Beihang University, Beijing 100191, People's Republic of China.
概括
一种新的语义细分方法可以准确地识别3D打印合金 (SLM Ti-6Al-4V) 的缺陷. 这种缺陷表征可以改善材料疲劳寿命的预测,这对结构完整性至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算机视觉 计算机视觉
背景情况:
- 增材制造中的缺陷显著影响材料疲劳寿命.
- 准确的缺陷表征对于预测结构完整性至关重要.
- 语义细分为自动缺陷分析提供了一个有希望的方法.
研究的目的:
- 开发和验证用于SLM Ti-6Al-4V合金中缺陷特征的语义细分方法.
- 提高缺陷识别的准确性,特别是对于小目标.
- 为了利用缺陷细分来预测疲劳寿命.
主要方法:
- 使用同步辐射计算机断层扫描获取缺陷数据集.
- 开发一个基于DeepLabV3+的语义细分网络.
- 应用数据集预处理 (切割,低样本) 和后处理 (形态操作) 的应用.
- 对缺陷矩阵像素比率实施加权分类方案.
主要成果:
- 在测试数据集上实现了98.2%的像素识别准确度.
- 在两批SLMTi-6Al-4V材料中成功细分了缺陷.
- 预测疲劳寿命,误差在 ±2.2 分散带内.
结论:
- 开发的语义细分方法有效地识别和描述3D打印合金中的缺陷.
- 精确的缺陷细分提高了疲劳寿命预测的可靠性.
- 这种方法有助于确保增材制造组件的结构完整性.
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