电子科技大学学报2024，Vol.53Issue(6)：844-851,8.DOI:10.12178/1001-0548.2023242
数据驱动的KDP晶体加工表面质量分类研究
Research on Data-Driven Surface Quality Classification for KDP Crystal Processing
摘要
Abstract
To assist in monitoring occasional processing errors in the precision machining of Potassium Dihydrogen Phosphate(KDP)on ultra-precision fly-cutting machines,this paper combines key feature extraction of vibration data and temperature data during the machining process to establish a predictive model for crystal processing surfaces.Based on ResNet-18,the relationship between vibration data and KDP crystal surface qualification is analyzed for binary classification predictions.The established model achieves an accuracy of 88.5%on the test set.Meanwhile,based on the XGBoost model,the relationship between temperature data and the low-frequency index P-V(Peak-to-Valley)of KDP crystal surface quality is analyzed and predicted.The experimental results show that the prediction model can predict the surface quality of the processed element quickly,and the overall error is within an acceptable range.By analyzing the processing errors,a complete machine tool model is constructed.The transient temperature field of the machine tool under long-time processing is calculated using finite element analysis.The simulation results show that the maximum temperature of the machine tool reaches 26.9℃after 8 580 s of the operation.Experimental verification confirms the accuracy of the simulation results and supports the conclusion that the"decline in KDP crystal processing quality in the later stage"is related to"the continuous warming of the machine tool spindle system during the processing".关键词
有限元分析/KDP晶体/ResNet/超精密飞切机床/XGBoostKey words
finite element analysis/KDP crystal/ResNet/ultra-precision flying cutting machine/XGBoost分类
能源科技引用本文复制引用
张川东,汪承毅,王伟..数据驱动的KDP晶体加工表面质量分类研究[J].电子科技大学学报,2024,53(6):844-851,8.基金项目
国家自然科学基金(52175456) （52175456）
