Energy consumption prediction for CNC machine tool machining based on improved stochastic configuration networks
ZHANG Weifeng
SUN Xingwei
LIU Yin
ZHAO Hongxun
MU Shibo
Abstract：[Objective]CNC machine tools in the operating state feature high instantaneous power and low energy efficiency,and their machining energy consumption power changes with complex and variable processing tasks in real time,thus making it difficult to predict energy consumption of machine tool machining.The prediction mechanism model of machining energy consumption of CNC machine tools based on information flow and energy flow requires operators to be aware of the operating status of the machine tool and the characteristics of energy consumption changes of the machine tool,which results in difficult prediction of machining energy consumption of machine tools and long cycles.As the testing techniques and computational power of computers significantly improve,data-driven prediction methods have been introduced to the research on predicting the machining energy consumption of machine tools.Therefore,an adaptive incremental machine learning approach that combines stochastic configuration networks(SCNs)with a multi-mechanism-improved sand cat swarm optimization(SCSO)was proposed to achieve efficient and high-precision prediction of the machining energy consumption of machine tools.[Methods]By taking the helical groove CNC milling machine milling screw rotor as an example,based on the process parameters,the machining energy consumption milling experiments and collected machining energy consumption data were designed.Meanwhile,SCNs were optimized by adopting the multi-mechanism-improved SCSO algorithm to build a prediction model for machining energy consumption.The SCN algorithm was employed as the prediction model for machining energy consumption.The SCSO algorithm improved by combining the Tent population initialization strategy,variable helix search strategy and adaptive t-distribution strategy solved the scale factor and regularization parameter during SCN modeling to improve the prediction accuracy and prediction efficiency of SCNs.[Results]To verify the accuracy of the model,the root mean square error(RMSE)and mean absolute percentage error(MAPE)were employed as the evaluation indexes to compare the BP neural networks(SSA-BP)optimized by the SCSO-SCNs,SCNs,and squirrel search algorithm.Comparison results show that compared to SSA-BP and SCNs,SCSO-SCNs shows a decrease of 38.62％and 46.03％in RMSE respectively,while it presents a reduction of 40.47％and 47.33％in MAPE compared to SSA-BP and SCNs respectively,which proves the performance superiority of the SCSO-SCNs model in the prediction of machining energy consumption.[Conclusions]The proposed machine learning method,which integrates SCNs and multi-mechanism-improved SCSO algorithm,shows more obvious performance advantages in terms of machining energy consumption prediction for CNC machine tools.The improved SCSO algorithm enhances the search efficiency and the ability to jump out of local optimal solutions by optimizing the initial population of the algorithm and improving the population position updating strategy in the improvement and exploitation phases.The SCSO algorithm,based on multi-mechanism improvement,greatly improves the prediction accuracy of the model by seeking the optimal scale factor and generalization factor of SCNs.Comparison with existing machine learning algorithms shows that the proposed method has higher prediction accuracy and greatly improves the prediction efficiency of machining energy consumption.
Keywords：CNC machine toolsand cat swarm optimizationmachine learningstochastic configuration networkshelical surfaceprocess parameterorthogonal testmachining energy consumption
Publication Date：2025-11-25
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：8( 775-782 )