摘要
钕铁硼(NdFeB)永磁材料凭借其极高的磁能积和矫顽力,在现代工业与电子技术领域得到了广泛应用。然而,在实际生产中,压型工艺作为钕铁硼生产的关键环节,其工艺参数的设置仍主要依赖于操作人员的经验。由于操作人员经验的差异以及生产过程的复杂性,这种依赖容易导致参数设置不稳定,从而影响产品质量并造成资源浪费。为了精确预测钕铁硼粉末压型阶段的工艺参数,本研究提出一种动态分层调整的CatBoost多输出预测模型DLA-CatBoost,并创新性地提出基于粒子群优化引导动态搜索空间调整的混合优化策略PSO-DSS-NSGA-Ⅲ,实现预测模型超参数的多目标协同优化。实验结果表明,经PSO-DSS-NSGA-Ⅲ策略优化的DLA-CatBoost模型在多输出预测任务中表现优异,其均方根误差(RMSE)在0.5~0.9之间,平均绝对误差(MAE)在0.2~0.5之间,决定系数(R2)在0.96~0.99之间,展现出卓越的预测效果,为钕铁硼压型工艺参数优化提供了一种有效的新方法。
NdFeB(neodymium-iron-boron)permanent magnetic materials have been widely applied in modern industry and electronics due to their exceptionally high magnetic energy product and coercivity.However,in practical production,the compaction process—a critical stage in NdFeB manufacturing—still relies primarily on operator experience for setting process parameters.Variations in operator expertise and the inherent complexity of the production process often lead to unstable parameter settings,which adversely affect product quality and result in resource wastage.To accurately predict the process parameters during the powder compaction stage,this study proposes a Dynamic Layered Adjustment CatBoost(DLA-CatBoost)multi-output prediction model.Furthermore,an innovative hybrid optimization strategy,PSO-DSS-NSGA-III,which integrates particle swarm optimization to guide dynamic search space adjustment,is introduced to achieve multi-objective cooperative optimization of the model′s hyperparameters.Experimental results demonstrate that the DLA-CatBoost model optimized with the PSO-DSS-NSGA-III strategy exhibits excellent performance in multi-output prediction tasks,with a root mean square error(RMSE)ranging from 0.5 to 0.9,a mean absolute error(MAE)between 0.2 and 0.5,and a coefficient of determination(R 2)between 0.96 and 0.99,thereby demonstrating its superior predictive capability and establishing it as an effective new approach for optimizing the process parameters in NdFeB compaction.
作者
章启平
谢小云
Zhang Qiping;Xie Xiaoyun(School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China;School of Electronic Information Engineering,Gannan Science and Technology College,Ganzhou 341000,China;Ganzhou Key Laboratory of Intelligent Interconnection,Ganzhou 341000,China)
出处
《电子测量技术》
北大核心
2025年第22期152-165,共14页
Electronic Measurement Technology
基金
国家重点研发计划专项(2020YFB1713700)
江西省03专项及5G项目(20204ABC03A18)资助。
