Multi-step forward intelligent prediction of tool wear condition
Accurate monitoring of tool condition is crucial for improving machining quality and efficiency.In the cur-rent widely used indirect methods for tool wear monitoring,the single-step or short-term predictions are predomi-nant,without achieving multi-step prediction and suffering from significant cumulative errors.Gaussian process is a machine learning method commonly applied in indirect methods.However,traditional Gaussian process regression has limited accuracy in tool wear prediction due to model structure and algorithm constraints.To address these shortcomings,an improved autoregressive recursive Gaussian process model was proposed for multi-step prediction of tool wear.To reduce cumulative prediction errors,the improved model updating methods and the composite ker-nel functions were applied to set forgetting factor for samples during model training.Additionally,a bias correction method was incorporated in the prediction process.The effects of each improvement factor on the model were stud-ied,and the accurate multi-step prediction of tool wear state was achieved by combining all favorable factors.The prediction errors reduced by 85.68％,20.67％and 63.32％on three test sets respectively.
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