Numerical and Experimental Investigation of the Temperature Rise of Cutting Tools Caused by the Tool Deflection Energy

Tool temperature variation in flank milling usually causes excessive tool wear, shortens tool life, and reduces machining accuracy. The heat source is the primary factor of the machine thermal error in the process of cutting components. Moreover, the accuracy of the thermal error modeling is greatly influenced by the formation mechanism of the heat source. However, the tool heat caused by the potential energy of the tool bending and twisting has essentially not been taken into consideration in previous research. In this paper, a new heat source that causes the thermal error of the cutting tools is proposed. The potential energy of the tools’ bending and twisting is calculated using experimental data, and how tool potential energy is transformed into heat via friction is explored based on the energy conservation. The temperature rise of the cutting tool is simulated by a lattice-centered finite volume method. To verify the model, the temperature separation of a tool edge is measured experimentally under the given cutting load. The results of the numerical analysis show that the rise in tool temperature caused by the tool’s potential energy is related to the time and position of the cutting edge involved in milling. For the same conditions, the predicted results are consistent with the experimental results. The proportion of temperature rise due to tool potential energy is up to 6.57% of the total tool temperature rise. The results obtained lay the foundation for accurate thermal error modeling, and also provide a theoretical basis for the force–thermal coupling process.

A Review of Thermal Error Modeling Methods for Machine Tools

Thermal error caused by thermal deformation is one of the most significant factors influencing the accuracy of the machine tool. Compensation is a practical and efficient method to reduce the thermal error. Among all the thermal error compensation processes, thermal error modeling is the premise and basis because the effectiveness of the compensation is directly determined by the accuracy and robustness of modeling. In this paper, an overview of the thermal error modeling methods that have been researched and applied in the past ten years is presented. First, the modeling principle and compensation methods of machine tools are introduced. Then, the methods are classified and summarized in detail. Finally, the future research trend of thermal error modeling is forecasted.

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry. Among all errors, thermal error affects the machining accuracy considerably. Because of the significant impact of Industry 4.0 on machine tools, existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data. A thermal error modeling method is proposed based on bidirectional long short-term memory (BiLSTM) deep learning, which has good learning ability and a strong capability to handle a large group of dynamic data. A four-layer model framework that includes BiLSTM, a feedforward neural network, and the max pooling is constructed. An elaborately designed algorithm is proposed for better and faster model training. The window length of the input sequence is selected based on the phase space reconstruction of the time series. The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting. The average depth variation of the workpiece was reduced from approximately 50 µm to less than 2 µm after compensation. The reduction in maximum depth variation was more than 85%. The proposed model was proved to be feasible and effective for improving machining accuracy significantly.