无关材料的机器学习方法使得粉床聚变产品的相对密度高
Jaemin Wang1, Sang Guk Jeong1, Eun Seong Kim1
1Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
Nature communications
|October 17, 2023
概括
这项研究提出了一种通用方法,用于预测激光粉床融合条件,以实现各种粉末的比密度超过98%. XGBoost模型优化了高质量的增材制造过程参数.
科学领域:
- 材料科学 材料科学 材料科学
- 添加剂制造 添加剂制造 添加剂制造
- 计算建模 计算建模
背景情况:
- 激光粉床融合 (LPBF) 是一种关键的增材制造技术.
- 实现高相对密度 (>98%) 对于LPBF中的组件性能至关重要.
- 预测各种材料的最佳工艺条件仍然是一个挑战.
研究的目的:
- 开发一种普遍适用的方法来预测LPBF工艺条件.
- 为了确定各种粉末材料的相对密度大于98%的参数.
- 提高材料-工艺-密度关系的准确性和理解.
主要方法:
- 开发使用粉末材料特性和工艺条件的XGBoost预测模型.
- 在模型的相对密度输出中应用一个sigmoid函数转换以提高准确性.
- 使用沙普利增量解释 (SHAP) 进行特征重要性和关系的深入分析.
主要成果:
- XGBoost模型准确地预测了实现高相对密度 (>98%) 的工艺条件.
- 该方法证明了在不钢316L,AlSi10Mg和中等合金中的可重复性和可转移性.
- SHAP分析提供了对输入特征对最终零件密度的影响的见解.
结论:
- 开发的方法为优化LPBF工艺条件提供了一个广泛适用的策略.
- 这种方法有助于在增材制造中实现一致的高质量零件.
- 这些发现促进了LPBF应用的高效工艺开发和材料选择.
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