Abstract
Tool wear is a major contributor to machining errors of a workpiece. Tool wear prediction is an effective way to estimate the wear loss in precision machining. In this study, all kinds of machining conditions are treated as the input variables, the wear loss of the tool is treated as the output variable, and Projection Pursuit Regression (PPR) algorithm is proposed to mapping the tool wear loss. Finally, a real-time prediction device is presented based on the proposed PPR algorithm, and the prediction and measurement results are found to be in satisfied agreement with average error lower than 5%.
Highlights
Tool wear affects the quality of the product and the efficiency of the process, and many studies focus on the area in recent years. Girardin et al (2010) developed a TCM system, and analyzed instantaneous variations in rotational frequency so as to observe milling operation. Ratnam and Shahabi (2008) developed a vision system using high-resolution CCD camera and back-light for the on-line measurement of tool wear. Luo (2004) analyzed the formation mechanism of tool wear and presents a complete solution to calculate wear using a ball end cutter for high-speed cutting.In order to maintain product quality and process efficiency, machining processes attempt to prevent tool breakage by predicting the tool wear
The traditional method is moving the tool out of the machine to check the wear under a microscope, or checking the tool wear with other measuring devices, such as a charge-coupled device camera, machining has to be stopped for the out-ofprocess tool wear monitoring
Tool wears monitoring: Projection Pursuit Regression (PPR) method: The principle thought of PPR method is: with the machining parameters affecting tool wear as input variables and the tool wear as the output variable, the PPR method can be employed to establish the relationship between the input and output variables and predict tool wear of the milling machine according to experimental samples
Summary
Tool wear affects the quality of the product and the efficiency of the process, and many studies focus on the area in recent years. Girardin et al (2010) developed a TCM system, and analyzed instantaneous variations in rotational frequency so as to observe milling operation. Ratnam and Shahabi (2008) developed a vision system using high-resolution CCD camera and back-light for the on-line measurement of tool wear. Luo (2004) analyzed the formation mechanism of tool wear and presents a complete solution to calculate wear using a ball end cutter for high-speed cutting.In order to maintain product quality and process efficiency, machining processes attempt to prevent tool breakage by predicting the tool wear. Palanisamy et al (2008) presented An Artificial Neural Network (ANN) models for predicting tool wear. Tae and Dong (1996) introduced an adaptive signal processing scheme that uses a low-order autoregressive time series model to model the cutting force data for tool wear monitoring during face milling.
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