In order to satisfy the machining requirements of aero-engine casing in modern aviation industry, this paper investigates three main issues during the design and development process of a five-axis machine tool with hi...In order to satisfy the machining requirements of aero-engine casing in modern aviation industry, this paper investigates three main issues during the design and development process of a five-axis machine tool with high accuracy, stiffness and efficiency, including whole structure design,key components design, and supporting stiffness design. First, an appropriate structure of five-axis machine tool is determined considering the processing characteristics of aero-engine casing. Then, a dual drive swing head and a compact motorized spindle are designed with enough drive capability and stiffness, and related structure, assembly method, cooling technology, and performance simulation are given in detail. Next, a design method of supporting stiffness of guide is proposed through the deformation prediction of the spindle end. Based on above work, a prototype of machine tool is developed, and some experiments are carried out, including performance tests of swing head and motorized spindle, and machining of a simulated workpiece of aero-engine casing. All experimental results show that the machine tool has satisfactory accuracy, stiffness and efficiency, which meets the machining requirements of aero-engine casing. The main work can be used as references for engineers and technicians, which are meaningful in practice.展开更多
High-speed milling(HSM)is advantageous for machining high-quality complex-structure surface components with various materials.Identifying and estimating cutting force signals for characterizing HSM is of high signific...High-speed milling(HSM)is advantageous for machining high-quality complex-structure surface components with various materials.Identifying and estimating cutting force signals for characterizing HSM is of high significance.However,considering the tool runout and size effects,many proposed models focus on the material and mechanical characteristics.This study presents a novel approach for predicting micromilling cutting forces using a semianalytical multidimensional model that integrates experimental empirical data and a mechanical theoretical force model.A novel analytical optimization approach is provided to identify the cutting forces,classify the cutting states,and determine the tool runout using an adaptive algorithm that simplifies modeling and calculation.The instantaneous un-deformed chip thickness(IUCT)is determined from the trochoidal trajectories of each tool flute and optimized using the bisection method.Herein,the computational efficiency is improved,and the errors are clarified.The tool runout parameters are identified from the processed displacement signals and determined from the preprocessed vibration signals using an adaptive signal processing method.It is reliable and stable for determining tool runout and is an effective foundation for the force model.This approach is verified using HSM tests.Herein,the determination coefficients are stable above 0.9.It is convenient and efficient for achieving the key intermediate parameters(IUCT and tool runout),which can be generalized to various machining conditions and operations.展开更多
Milling force is key to the understanding of cutting mechanism and the control of machining process.Traditional milling force models have limited prediction accuracy due to their simplified conditions and incomplete k...Milling force is key to the understanding of cutting mechanism and the control of machining process.Traditional milling force models have limited prediction accuracy due to their simplified conditions and incomplete knowledge contained for model construction.On the other hand,due to the lack of guidance from physics,the data-driven models lack interpretability,making them challenging to generalize to practical applications.To meet these difficulties,a deep network model guided by milling dynamics is proposed in this study to predict the instantaneous milling force and spindle vibration under varying cutting conditions.The model uses a milling dynamics model to generate data sets to pre-train the deep network and then integrates the experimental data for fine-tuning to improve the model’s generalization and accuracy.Additionally,the vibration equation is incorporated into the loss function as the physical constraint,enhancing the model’s interpretability.A milling experiment is conducted to validate the effectiveness of the proposed model,and the results indicate that the physics incorporated could improve the network learning capability and interpretability.The predicted results are in good agreement with the measured values,with an average error as low as 2.6705%.The prediction accuracy is increased by 24.4367%compared to the pure data-driven model.展开更多
基金co-supported by the Natural Science Foundation of Beijing(No.3214043)the Project of State Key Lab of Tribology of Tsinghua University(No.SKLT2021D16)the National Natural Science Foundation of China(No.51975319)。
文摘In order to satisfy the machining requirements of aero-engine casing in modern aviation industry, this paper investigates three main issues during the design and development process of a five-axis machine tool with high accuracy, stiffness and efficiency, including whole structure design,key components design, and supporting stiffness design. First, an appropriate structure of five-axis machine tool is determined considering the processing characteristics of aero-engine casing. Then, a dual drive swing head and a compact motorized spindle are designed with enough drive capability and stiffness, and related structure, assembly method, cooling technology, and performance simulation are given in detail. Next, a design method of supporting stiffness of guide is proposed through the deformation prediction of the spindle end. Based on above work, a prototype of machine tool is developed, and some experiments are carried out, including performance tests of swing head and motorized spindle, and machining of a simulated workpiece of aero-engine casing. All experimental results show that the machine tool has satisfactory accuracy, stiffness and efficiency, which meets the machining requirements of aero-engine casing. The main work can be used as references for engineers and technicians, which are meaningful in practice.
基金Supported by National Natural Science Foundation of China(Grant No.52175528).
文摘High-speed milling(HSM)is advantageous for machining high-quality complex-structure surface components with various materials.Identifying and estimating cutting force signals for characterizing HSM is of high significance.However,considering the tool runout and size effects,many proposed models focus on the material and mechanical characteristics.This study presents a novel approach for predicting micromilling cutting forces using a semianalytical multidimensional model that integrates experimental empirical data and a mechanical theoretical force model.A novel analytical optimization approach is provided to identify the cutting forces,classify the cutting states,and determine the tool runout using an adaptive algorithm that simplifies modeling and calculation.The instantaneous un-deformed chip thickness(IUCT)is determined from the trochoidal trajectories of each tool flute and optimized using the bisection method.Herein,the computational efficiency is improved,and the errors are clarified.The tool runout parameters are identified from the processed displacement signals and determined from the preprocessed vibration signals using an adaptive signal processing method.It is reliable and stable for determining tool runout and is an effective foundation for the force model.This approach is verified using HSM tests.Herein,the determination coefficients are stable above 0.9.It is convenient and efficient for achieving the key intermediate parameters(IUCT and tool runout),which can be generalized to various machining conditions and operations.
基金supported in part by the National Natural Science Foundation of China(52175528)in part by the National Key Research and Development Program of China,the Chinese Ministry of Science and Technology(2018YFB1703200).
文摘Milling force is key to the understanding of cutting mechanism and the control of machining process.Traditional milling force models have limited prediction accuracy due to their simplified conditions and incomplete knowledge contained for model construction.On the other hand,due to the lack of guidance from physics,the data-driven models lack interpretability,making them challenging to generalize to practical applications.To meet these difficulties,a deep network model guided by milling dynamics is proposed in this study to predict the instantaneous milling force and spindle vibration under varying cutting conditions.The model uses a milling dynamics model to generate data sets to pre-train the deep network and then integrates the experimental data for fine-tuning to improve the model’s generalization and accuracy.Additionally,the vibration equation is incorporated into the loss function as the physical constraint,enhancing the model’s interpretability.A milling experiment is conducted to validate the effectiveness of the proposed model,and the results indicate that the physics incorporated could improve the network learning capability and interpretability.The predicted results are in good agreement with the measured values,with an average error as low as 2.6705%.The prediction accuracy is increased by 24.4367%compared to the pure data-driven model.
