数据驱动的 SLM IN718 合金工艺-相对密度数据补齐及预测

Data-driven complementation and prediction of process-relative density data for SLM IN718 alloy

摘要

实现多工艺参数优化设计是增材制造领域面临的难题，数据驱动的性能预测模型是解决该难题的有效方法。日益积累的文献数据为数据驱动建模奠定了数据基础。本工作采用从选区激光熔化（selective laser melting，SLM）IN718 合金相关文献中挖掘的工艺参数（激光功率、扫描速率、扫描间距和铺粉厚度）与相对密度数据作为样本数据，运用期望最大化算法补齐文献中缺失的参数数据；基于麻雀搜索算法（sparrow search algorithm，SSA）的广义回归神经网络（generalized regression neural network，GRNN）、基于开普勒优化算法（Kepler optimization algorithm，KOA）的随机森林（random forest，RF）和极限梯度提升（extreme gradient boosting，XGBoost）构建相对密度预测模型。通过决定系数 R²、均方根误差（root mean square error，RMSE）、平均绝对误差（mean absolute error，MAE）、平均相对误差（mean relative error，MRE）等统计学参数评估表明，3 种模型均表现出较高预测精度。其中，KOA-XGBoost 模型精度最高，其 R²、RMSE、MAE 和 MRE 分别达到 0.967、0.806、0.437 和 0.47%；KOA-RF 模型次之，其 R²、RMSE、MAE 和 MRE 分别为 0.938、1.102、0.466 和 0.52%；SSA-GRNN 模型相对较差，其 R²、RMSE、MAE 和 MRE 分别为 0.899、1.399、0.588 和 0.63%。3 种模型对新实验数据的验证取得较满意的预测结果，且预测精度高低顺序为 KOA-XGBoost＞KOA-RF＞SSA-GRNN，这意味着建立的模型具有较好的鲁棒性和泛化能力。

Abstract

Optimization of multiple process parameters remains a challenge in additive manufacturing，and building data-driven property prediction models serve as an effective approach to address this challenge. Accumulating literature data has laid a fundamental data foundation for data-driven modeling. In this work， process parameters (laser power， scanning speed， hatch spacing，and layer thickness) and corresponding relative density data extracted from published studies on IN718 alloy fabricated by selective laser melting (SLM) are adopted as dataset samples. The expectation-maximization (EM) algorithm is employed to impute missing parameter values from collected literature. Three prediction models for relative density are established based on the sparrow search algorithm (SSA)-optimized generalized regression neural network (GRNN)， the Kepler optimization algorithm (KOA)-optimized random forest (RF)，and extreme gradient boosting (XGBoost). Statistical evaluation indicators including coefficient of determination (R²)，root mean square error (RMSE)，mean absolute error (MAE)，and mean relative error (MRE) verify that all three models achieve favorable prediction accuracy. Among them，the KOA-XGBoost model delivers the optimal predictive performance with R²， RMSE， MAE， and MRE of 0.967， 0.806， 0.437， and 0.47%， respectively； the KOA-RF model ranks second， with corresponding values of 0.938，1.102，0.466，and 0.52%；the SSA-GRNN model exhibits relatively inferior accuracy，with R²， RMSE，MAE，and MRE equal to 0.899，1.399，0.588，and 0.63%，respectively. Satisfactory prediction outcomes are obtained when three models are validated against independent experimental datasets， and the accuracy ranking follows the order： KOA-XGBoost>KOA-RF>SSA-GRNN，demonstrating robust stability and strong generalization capability of the developed models.

关键词

数据驱动 / 选区激光熔化 / IN718 合金 / 工艺与性能 / 缺失数据补齐 / 预测建模

Key words

data-driven / selective laser melting / IN718 alloy / process and property / missing data complemention / predictive modeling

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基金资助

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)