Numerical control machine tool thermal error real-time compensation modeling method based on time series algorithm

Abstract

The invention relates to a numerical control machine tool thermal error real-time compensation modeling method based on a time series algorithm, which belongs to the technical field of precision machining. The method comprises the steps of (1) carrying out data zero mean pretreatment, namely employing an inverted sequence test method and a kurtosis and skewness test method to judge the stationarity and the normality of the data; (2) using an autocorrelation function, a partial correlation function, and the censored results as judgment criteria to carry out the pattern recognition of a thermal error mathematical model; (3) employing a least square estimation method or a long autoregressive residual calculating method to realize the parameter estimation of the thermal error mathematical model; (4) determining the order of the thermal error mathematical model, namely employing a judgment method that combines an AIC order determination criterion, an F test order determination criterion, and a whiteness test order determination criterion to realize the order determination of the thermal error mathematical model; (5) and carrying out integration processing of synthesizing judgment conditions, namely constructing a complete forecasting mathematical model formula. The modeling method provided by the invention has the advantages that less hardware is required, the applicability is wide, and the established model has high prediction precision and reliability.

Description

technical field [0001] The invention relates to a thermal error compensation modeling method in the technical field of precision machine tool processing, in particular to a real-time compensation modeling method for numerical control machine tool thermal error based on a time series algorithm. Background technique [0002] After years of development, the domestic CNC machine tool industry has made great progress in production scale, and has successfully entered the ranks of the machine tool production countries. At present, the CNC machine tools produced in my country account for about 35% of the domestic CNC machine tool market share, but they are mainly concentrated in the low-end and low-end markets, while the high-end CNC machine tool market is mostly occupied by imported machine tools, especially in the high-end market. In the field of CNC machine tools, the market share of domestic machine tool products only accounts for about 4%. As the current industrial products are...

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Problems solved by technology

[0003] At present, the "bottleneck" problem of domestic CNC machine tools entering the high-end field is mainly that the machining accuracy and reliability are difficult to reach the level of similar foreign products, and this involves many issues: 1. The processing of various assembly parts of high-end CNC machine tools The accuracy is difficult to guarantee due to the lack of more precise machine tools as the working machine; second, the design of the hardware structure of the machine tool in consideration of error compensation and the processing of error factors in the manufacturing process have not yet reached the same level as foreign machine tool manufacturers; The CNC system does not yet have software compensation technology, and the related research is still in the laboratory stage, and has not been implemented in actual production

However, the time series modeling method proposed in this paper lacks the key judgment conditions of time series modeling, and it is difficult to guarantee the accuracy of the model by using a single judgment criterion

First, only the stationarity of the thermal error data series is tested by the statistical variance method in this paper, and the zero-mean method of the sample is not processed, and the specific method of the stationarity test is not pointed out, and the zero-mean method is not tested at the same time. The normality of the processed samples was judged; secondly, the AR model was directly used as the fitting model of the thermal error system in this paper, and the pattern recognition of the model was not carried out according to the sample sequence, so the final result may be caused by the wrong model selection. The mathematical model cannot achieve the compensation effect; thirdly, the model order determination method only adopts a single FPE criterion, and when the single order determination criterion is judged, because there are many uncertain factors encountered, sometimes there will be ambiguity. Increased difficulty and instability of order identification

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