Research On Intelligent Monitoring Method Of Tool Wear And Remaining Life In Milling Process

With the emergence of industry 4.0 and "Internet + manufacturing",the digitization,automation,and intelligence of cutting processing are crucial for the development of the entire manufacturing industry.As an important part of the steam generator of the nuclear island,the water chamber head is made of high intensity508Ⅲ steel,which is mainly processed by milling.Difficult machining of part materials,large machining allowance and cutting parameters lead to serious tool wear issues in milling process.Severe tool wear may lead to abnormal cutting processes and even result in the failure of the workpiece being processed.The traditional tool replacement method based on subjective experience leads to increased auxiliary time of machine tool and better cost and resource consumption,which directly affects the processing efficiency and production cost.Thus,the research on intelligent monitoring method of tool wear and remaining life in milling process is of great significance to ensure the efficient and stable operation of upscale equipped machining system.In response to the problem of serious tool wear and low life of milling 508Ⅲsteel,which seriously affects the cutting efficiency and production cost.Tool wear monitoring test and and wear mechanism of milling 508 III steel is conducted.The force and vibration signals are used as monitored signals,the data collection platform for milling tool wear monitoring test is built,and the milling 508Ⅲ steel test is carried out.Tool wear forms and mechanisms are analyzed from a macro and micro perspective.The complete data of cutting force and vibration signals and corresponding tool flank wear in cutting are acquired,and the process of tool wear changes is discussed,providing the necessary data basis for the subsequent research.Aiming at the problem that tool wear has a significant effect on processing efficiency and machining stability,and if serious tool wear cannot be detected in time,it may lead to interruption of machining process and equipment failure.A tool wear prediction method using WOA-optimized support vector machine based on multidomain sensitive features of milling signals is proposed.The time,frequency and time-frequency domain features of the signal are analyzed theoretically,and the multi-domain features of the monitored signals are extracted and normalized.Multidomain sensitive features characterizing tool wear are selected by Pearson correlation coefficient method.WOA is used to optimize the internal parameters of SVM.The correlation mapping between sensitive features and tool wear is established,and the WOA-SVM regression prediction model is constructed to predict tool wear.The effectiveness is verified through comparison with other prediction methods and milling experiments,which provides a certain theoretical support for the realization of intelligent prediction of tool wear.In view of the unstable and large fluctuation of multi-source signals in milling,the time and frequency domain characteristics can sometimes be affected by cutting conditions and unstable signals,resulting in the failure to provide more accurate and comprehensive information about tool wear and the interference of redundant features.A tool wear state recognition method based on multi-signal singularity statistical feature fusion in milling process is proposed.Denoising and quantitative characterization of monitoring signal singularity are implemented by using wavelet transform modulus maxima based on signal singularity detection theory.Unsupervised clustering of tool wear states is carried out by using spatial clustering algorithm.Combined with the statistical theory and tool wear status information,the Lipschitz index probability density transformation is performed and the related statistical characteristics are extracted,and the correlation with tool wear are analyzed.Random forest algorithm and kernel principal component analysis algorithm are used for relatively important features screening and dimension reduction fusion of multi-signal singularity features,and the WOA-SVM classification model based on the multi-signal singularity fusion features is constructed to identify the tool wear status.Through comparison with other classification methods and milling experiments,the effectiveness is verified,which provides a certain theoretical foundation for the realization of intelligent recognition of tool wear.Tool remaining useful life prediction(RUL)prediction can provide reference for appropriate tool replacement timing.Reasonable selection of cutting parameters is an effective way to improve tool life,ensure cutting efficiency and machining quality.Based on the above research,the tool RUL prediction methods are proposed based on the WOA-XGBoost ensemble learning model and the multi-scale Dense Net-Res Net-GRU deep learning ensemble model respectively.The association model between fused features and remaining tool life is established to predict tool RUL,and the effectiveness is verified by comparison with other methods and milling tests.Based on milling experimental research,with tool life,cutting efficiency,and machined surface roughness as evaluation indicators,the multi-objective optimization method for cutting parameters based on improved genetic optimization algorithm combined with multi-attribute decision-making is proposed to achieve balance between different conflicting objectives,optimize the best decision plan,and improve the decision-making efficiency of actual machining operators.The above research results can provide theoretical basis and technical support for promoting intelligent warning of cutting tool status and intelligent optimization development of cutting process for high-end equipment.