刀具剩余使用寿命预测方法研究进展OA

Significance:Cutting tools are indispensable key instruments in the manufacturing industry,whose performance status directly affects machining quality,production efficiency,and equipment safety.Accurate prediction of the remaining useful life(RUL)of tools not only enables the intelligent transition from"scheduled replacement"to"condition-based replacement"but also significantly reduces resource waste caused by premature tool changes,workpiece scrapping and even equipment damage risks due to delayed replacement.With the deep integration of industrial automation,digitalization,and smart manufacturing,tool RUL prediction has become one of the core technologies in intelligent manufacturing and predictive maintenance,holding substantial engineering application value and theoretical research significance for enhancing the overall competitiveness of the manufacturing industry.Progress:This paper systematically reviews the research progress in methods for predicting the remaining useful life of cutting tools.Based on their prediction principles,these methods are categorized into four main types,and their modeling ideas,applicable scenarios,advantages,and disadvantages are analyzed in depth.(1)Physics-based model prediction methods:These methods start from the physical mechanisms of tool wear,constructing mathematical models to describe the wear process,such as wear mechanism models,cutting force coefficient models,and finite element models.Their advantage lies in having clear physical significance and strong interpretability,making them particularly suitable for stable machining processes with well-understood mechanisms.However,these methods rely on accurate modeling of multiple physical fields in complex machining environments,face difficulties in parameter identification,and exhibit weak adaptability to dynamically changing working conditions.(2)Data-driven statistical model prediction methods:These methods do not rely on physical mechanisms but instead analyze historical monitoring data to build RUL prediction models using statistical laws.They mainly include empirical wear models(e.g.,Taylor's formula and its extended forms)and stochastic process models(e.g.,Wiener process,Gamma process,inverse Gaussian process).Such methods demonstrate good fitting capability when data is sufficient and can quantify prediction uncertainty,but their performance is limited by data quality and quantity,and their generalization ability is usually weak.(3)Artificial intelligence-based prediction methods:With the advancement of big data and computing power,artificial intelligence methods represented by machine learning and deep learning show great potential in tool RUL prediction.Machine learning models(e.g.,SVM,RVM,AR,HMM)are adept at handling small-sample and nonlinear problems;deep learning models(e.g.,RNN,LSTM,CNN,DBN)can automatically extract deep features from raw sensor data and possess stronger capabilities for temporal modeling and pattern recognition.Although AI methods offer high prediction accuracy and strong adaptability,their"black-box"nature leads to poor interpretability,and they require large volumes of high-quality labeled data.(4)Hybrid model prediction methods:To compensate for the limitations of single-method approaches,researchers in recent years tend to construct hybrid models that integrate the advantages of physics-based knowledge,data statistics,and artificial intelligence.For example,combining physical models with data-driven methods,or introducing stochastic modeling of the degradation process into the AI framework,to balance prediction accuracy and model reliability.Through multi-source information fusion and complementarity,hybrid models significantly enhance RUL prediction capability under complex working conditions,representing a current hot research direction.Conclusions and Prospects:Through a systematic review of existing research,it can be concluded that tool RUL prediction methods evolve from single models to multi-method fusion,and from offline analysis to online intelligent diagnosis.However,this field still faces the following major challenges.(1)Reliability of machining signal acquisition and processing:Industrial field data is often plagued by noise interference and incomplete sampling.There is an urgent need to develop more robust feature extraction and signal denoising methods,and to explore real-time data acquisition technologies based on new sensing methods such as intelligent tool holders.(2)Effective fusion of multi-sensor data:Effectively integrating multi-source heterogeneous information(e.g.,force,vibration,acoustic emission)and extracting common features strongly correlated with tool degradation from them are key to enhancing model robustness.(3)Balancing model accuracy and generalization ability:Most current models perform well under specific conditions but are prone to performance degradation in application scenarios with varying tool materials and machining parameters.Future research needs to explore cross-condition,adaptive,and lightweight model architectures.(4)Improving the interpretability of hybrid models:Although hybrid models have advantages in accuracy,their decision-making processes often lack transparency.Enhancing model interpretability so that their predictions can be understood and trusted by engineers is a crucial link in promoting technology implementation.