彭正,鲁翠媛,王克鲁,鲁世强

• 1:南昌航空大学材料科学与工程学院
• 2:南昌大学先进制造学院

摘要(Abstract)：

高精度且鲁棒的预测模型建立高度依赖于样本数据的大小、多样性和分布；日益积累的文献数据为获得大量的多样性样本数据提供了可能。以SLM-ed IN 718合金的相对密度(RD)为研究对象，针对从文献中挖掘的激光功率P、扫描速度V、扫描间距HS和铺粉厚度LT与RD样本数据存在缺失参数和分布不均问题，采用最大期望化(EM)算法对缺失参数进行补齐；采用带有梯度惩罚的WGAN算法(WGAN-GP)对数据稀疏的低RD区间生成虚拟样本数据。然后，分别基于补齐文献数据和补充虚拟数据，采用常青藤算法优化的随机森林(IVYA-RF)构建了RD预测模型，并对模型预测精度进行了定量评估和实验验证。结果表明：基于补充虚拟数据集构建的IVYA-RF模型II比基于补齐文献数据集构建的IVYA-RF模型I具有更好的预测精度，其原因主要来源于在低RD区间生成虚拟数据后，使建模样本数据的分布均匀性得到改善，这也是数据增强与机器学习相结合的意义所在。对新实验数据的验证取得了满意的预测精度，其中，IVYA-RF模型I验证结果的统计学参数R~2(决定系数)、RMSE(均方根误差)、MAE(平均绝对误差)和MRE(平均相对误差)分别达到了0.891、1.352%、0.915%和0.98%;IVYA-RF模型II验证结果的R~2增大至0.956,RMSE、MAE和MRE分别减小至0.833%、0.687%和0.71%,同样显示出后者比前者具有更好的预测精度。实验验证结果表明，所构建的RD预测模型具有较好的鲁棒性，从而具备了较好的工程应用价值。

关键词(KeyWords)： SLM-ed IN 718合金;缺失参数补齐;虚拟样本生成;预测建模

Abstract:

Keywords:

基金项目(Foundation): 江西省自然科学基金(20232BAB214001);; 2023年度“赣鄱俊才支持计划-高层次和急需紧缺海外人才项目(20232BCJ25074)

作者(Author): 彭正,鲁翠媛,王克鲁,鲁世强

DOI: 10.13289/j.issn.1009-6264.2025-0163

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