Modelling Cutting Temperature and Tool Thermal Error in Dry Cutting under Different Cutting Parameters
Journal
Procedia CIRP
Journal Volume
130
Journal Issue
27
Start Page
1869
End Page
1874
ISSN
2212-8271
Date Issued
2024
Author(s)
DOI
10.1016/j.procir.2024.10.330
Abstract
This work introduces a non-destructive, wireless sensor system for real-time monitoring of cutting temperatures to predict thermal errors in tools during dry cutting, reducing environmental pollution compared to coolant-based methods. Specifically, the system employs a thermocouple sensor positioned between the disposable insert and the tool shank, secured by the clamping force of the screw, and transmits the temperature data through Bluetooth to achieve real-time measurement. The volume of the cutting slot per tooth is the main factor influencing cutting temperature, and the cutting temperature directly affects the magnitude of tool center point (TCP) thermal error. Therefore, this sensor system offers a solution to the thermal error issue by collecting temperature and thermal deformation data to train a model capable of predicting thermal errors in the cutting tool. This model comprises temperature and thermal error models, both utilizing four predictive models-Random Forest (RF), Gradient Boosting (GB), Extra Trees (ET), and eXtreme Gradient Boosting (XGB)-to identify the most accurate thermal error model. The results show that the ET model performs best for temperature prediction, with a root mean square error (RMSE) of only 1.05°C, while the RF model achieves the lowest error for thermal error prediction, with an RMSE of 1.07 µm. Consequently, this sensor system provides a method for manufacturers to predict thermal errors in real-time during the machining process.
Subjects
cutting tool thermal error
embedded thermocouple
machine learning
milling temperature
wireless temperature measurement
Publisher
Elsevier BV
Type
journal article