This study presents surface roughness modeling for machined parts based on cutting parameters (spindle speed, cutting depth, and feed rate) and machining vibration in the end milling process. Prediction models were developed using multiple regression analysis and an artificial neural network (ANN) modeling approach. To reduce the effect of chatter, machining tests were conducted under varying cutting parameters as defined in the stable regions of the milling tool. The surface roughness and machining vibration level are modeled with nonlinear quadratic forms based on the cutting parameters and their interactions through multiple regression analysis methods, respectively. Analysis of variance was employed to determine the significance of cutting parameters on surface roughness. The results show that the combined effects of spindle speed and cutting depth significantly influence surface roughness. The comparison between the prediction performance of the multiple regression and neural network-based models reveal that the ANN models achieve higher prediction accuracy for all training data with R = 0.96 and root mean square error (RMSE) = 3.0% compared with regression models with R = 0.82 and RMSE = 7.57%. Independent machining tests were conducted to validate the predictive models; the results conclude that the ANN model based on cutting parameters with machining vibration has a higher average prediction accuracy (93.14%) than those of models with three cutting parameters. Finally, the feasibility of the predictive model as the base to develop an online surface roughness recognition system has been successfully demonstrated based on contour surface milling test. This study reveals that the predictive models derived on the cutting conditions with consideration of machining stability can ensure the prediction accuracy for application in milling process.
This study aims to investigate the dynamic characteristics of a milling machine with different head stocks by using finite element (FE) method and receptance coupling analysis (RCA). For this purpose, five full finite element machine models, including vertical column, reformed head stock and feeding mechanism were created. With these models, the tool point frequency response functions were directly predicted. Another approach was the application of the receptance coupling method, in which the frequency response of the assembly milling tool was calculated from the receptance components of the individual substructures through the coupling operation with the interfaces of the feeding mechanism. Results show that a whole machine model with reformed stock has superior dynamic behavior when compared with the original design, by an increment of 10% in the dynamic stiffness. The receptance coupling method was verified to show an accurate prediction of the frequency response functions of the spindle tool when compared with the results obtained from the full FE models. Overall, the proposed methodology can help the designer to efficiently and accurately develop the machine tool structure with excellent mechanical performance.
Regenerative chatter has a fatal influence on machine performance in high-speed milling process. Basically, machine condition without chattering can be selected from the stability lobes diagram, which is estimated from the tool point frequency response function (FRF). However, measurements of the tool point FRF would be a complicated and time-consuming task with less efficiency. Therefore prediction of the tool point FRF is of importance for further calculation of the machining stability. This study employed the receptance coupling analysis method to predict the FRF of a tool holder-tool module, which is normally composed of substructures, tool holder and cutter with different length. In this study, the angular components of FRFs of the substructures required for coupling operation were predicted by finite element analysis, apart from the translational components measured by vibration experiments. Using this method, the effects of the overhang length of the cutter on the dynamic characteristics have been proven and successfully verified by the experimental measurements. The proposed method can be an effective way to accurately predict the dynamic behavior of the spindle tool system with different tool holder-tool modules.
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