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
Chatter is a self-excited vibration of parts in machining systems. It is widely present across a range of cutting processes, and has an impact upon both efficiency and quality in production processing. A great deal of...Chatter is a self-excited vibration of parts in machining systems. It is widely present across a range of cutting processes, and has an impact upon both efficiency and quality in production processing. A great deal of research has been dedicated to the development of technologies that are able to predict and detect chatter. The purpose of these technologies is to facilitate the avoidance of chatter during cutting processes, which leads to better surface precision, higher productivity,and longer tool life. This paper summarizes the current state of the art in research regarding the problems of how to arrive at stable chatter prediction, chatter identification, and chatter control/-suppression, with a focus on milling processes. Particular focus is placed on the theoretical relationship between cutting chatter and process damping, tool runout, and gyroscopic effect, as well as the importance of this for chatter prediction. The paper concludes with some reflections regarding possible directions for future research in this field.展开更多
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 by Projects of International Cooperation and Exchanges NSFC (51720105009)the National Natural Science Foundation of China (No. 51575147)the Youth Talent Support Program of Harbin University of Science and Technology (201507)
文摘Chatter is a self-excited vibration of parts in machining systems. It is widely present across a range of cutting processes, and has an impact upon both efficiency and quality in production processing. A great deal of research has been dedicated to the development of technologies that are able to predict and detect chatter. The purpose of these technologies is to facilitate the avoidance of chatter during cutting processes, which leads to better surface precision, higher productivity,and longer tool life. This paper summarizes the current state of the art in research regarding the problems of how to arrive at stable chatter prediction, chatter identification, and chatter control/-suppression, with a focus on milling processes. Particular focus is placed on the theoretical relationship between cutting chatter and process damping, tool runout, and gyroscopic effect, as well as the importance of this for chatter prediction. The paper concludes with some reflections regarding possible directions for future research in this field.
