Taking the minimum chip thickness effect,cutter deflection,and spindle run-out into account,a micro milling force model and a method to determine the optimal micro milling parameters were developed.The micro milling f...Taking the minimum chip thickness effect,cutter deflection,and spindle run-out into account,a micro milling force model and a method to determine the optimal micro milling parameters were developed.The micro milling force model was derived as a function of the cutting coefficients and the instantaneous projected cutting area that was determined based on the machining parameters and the rotation trajectory of the cutter edges.When an allowable micro cutter deflection is defined,the maximum allowable cutting force can be determined.The optimal machining parameters can then be computed based on the cutting force model for better machining efficiency and accuracy.To verify the proposed cutting force model and the method to determine the optimal cutting parameters,micro-milling experiments were conducted,and the results show the feasibility and effectiveness of the model and method.展开更多
Numerous factors affect the increased temperature of a machine tool, including prolonged and high-intensity usage,tool-workpiece interaction, mechanical friction, and elevated ambient temperatures, among others. Conse...Numerous factors affect the increased temperature of a machine tool, including prolonged and high-intensity usage,tool-workpiece interaction, mechanical friction, and elevated ambient temperatures, among others. Consequently,spindle thermal displacement occurs, and machining precision suffers. To prevent the errors caused by thetemperature rise of the Spindle fromaffecting the accuracy during themachining process, typically, the factory willwarm up themachine before themanufacturing process.However, if there is noway to understand the tool spindle’sthermal deformation, the machining quality will be greatly affected. In order to solve the above problem, thisstudy aims to predict the thermal displacement of the machine tool by using intelligent algorithms. In the practicalapplication, only a few temperature sensors are used to input the information into the prediction model for realtimethermal displacement prediction. This approach has greatly improved the quality of tool processing.However,each algorithm has different performances in different environments. In this study, an ensemble model is used tointegrate Long Short-TermMemory (LSTM) with Support VectorMachine (SVM). The experimental results showthat the prediction performance of LSTM-SVM is higher than that of other machine learning algorithms.展开更多
Research on dynamics and stability of machin-ing operations has attracted considerable attention. Cur-rently, most studies focus on the forward solution ofdynamics and stability in which material properties and thefre...Research on dynamics and stability of machin-ing operations has attracted considerable attention. Cur-rently, most studies focus on the forward solution ofdynamics and stability in which material properties and thefrequency response function at the tool tip are known topredict stable cutting conditions. However, the forwardsolution may fail to perform accurately in cases whereinthe aforementioned information is partially known or var-ies based on the process conditions, or could involve sev-eral uncertainties in the dynamics. Under thesecircumstances, inverse stability solutions are immenselyuseful to identify the amount of variation in the effectivedamping or stiffness acting on the machining system. Inthis paper, the inverse stability solutions and their use forsuch purposes are discussed through relevant examples andcase studies. Specific areas include identification of processdamping at low cutting speeds and variations in spindledynamics at high rotational speeds.展开更多
基金Project(NSC98-2221-E-033-047)supported by National Science Council
文摘Taking the minimum chip thickness effect,cutter deflection,and spindle run-out into account,a micro milling force model and a method to determine the optimal micro milling parameters were developed.The micro milling force model was derived as a function of the cutting coefficients and the instantaneous projected cutting area that was determined based on the machining parameters and the rotation trajectory of the cutter edges.When an allowable micro cutter deflection is defined,the maximum allowable cutting force can be determined.The optimal machining parameters can then be computed based on the cutting force model for better machining efficiency and accuracy.To verify the proposed cutting force model and the method to determine the optimal cutting parameters,micro-milling experiments were conducted,and the results show the feasibility and effectiveness of the model and method.
基金supported by the Ministry of Science and Technology,Taiwan,under Grant MOST 110-2218-E-194-010。
文摘Numerous factors affect the increased temperature of a machine tool, including prolonged and high-intensity usage,tool-workpiece interaction, mechanical friction, and elevated ambient temperatures, among others. Consequently,spindle thermal displacement occurs, and machining precision suffers. To prevent the errors caused by thetemperature rise of the Spindle fromaffecting the accuracy during themachining process, typically, the factory willwarm up themachine before themanufacturing process.However, if there is noway to understand the tool spindle’sthermal deformation, the machining quality will be greatly affected. In order to solve the above problem, thisstudy aims to predict the thermal displacement of the machine tool by using intelligent algorithms. In the practicalapplication, only a few temperature sensors are used to input the information into the prediction model for realtimethermal displacement prediction. This approach has greatly improved the quality of tool processing.However,each algorithm has different performances in different environments. In this study, an ensemble model is used tointegrate Long Short-TermMemory (LSTM) with Support VectorMachine (SVM). The experimental results showthat the prediction performance of LSTM-SVM is higher than that of other machine learning algorithms.
文摘Research on dynamics and stability of machin-ing operations has attracted considerable attention. Cur-rently, most studies focus on the forward solution ofdynamics and stability in which material properties and thefrequency response function at the tool tip are known topredict stable cutting conditions. However, the forwardsolution may fail to perform accurately in cases whereinthe aforementioned information is partially known or var-ies based on the process conditions, or could involve sev-eral uncertainties in the dynamics. Under thesecircumstances, inverse stability solutions are immenselyuseful to identify the amount of variation in the effectivedamping or stiffness acting on the machining system. Inthis paper, the inverse stability solutions and their use forsuch purposes are discussed through relevant examples andcase studies. Specific areas include identification of processdamping at low cutting speeds and variations in spindledynamics at high rotational speeds.
