The Selection of Machining Parameters to Minimize Deformation caused by Heat

Transcription

1 The Selection of Machining Parameters to Minimize Deformation caused by Heat Hengbo Cui Jong-Yun Jung Dug-Hee Moon Abstract Machining processes generate severe heat, which deforms thin metallic parts while they are being processed. The amount of deformation of a thin part is mainly dependent on heat and the shape of a component. The selection of machining parameters can decrease the temperature arisen during a machining process. This paper investigates selecting machining parameters to minimize the heat-deformation that is produced during machining. Taguchi method is applied to setup experiments. Three factors and three levels for each factor in the experimental design are selected. This research analyzes the effects of machining parameters to minimize the heat-deformation through examining the analysis of variance (ANOVA) and the signal to noise ratio (S/N). Key words: Machining parameter selection; Heat deformation; Taguchi method; Analysis of variance; Signal-to-noise ratio 1. Introduction Light and tough is necessary condition for the material of aircraft components. Hardened and heat treated aluminum or titanium alloy is good for the component since they are light. Aircraft industries manufacture the parts by machining processes. The end milling is one of the frequently used processes in metal working industries. While the material is being cut, work-pieces especially for thin parts are easily deformed because heat generated during the machining process is high and the melting point of the material is relatively low. Under these conditions, some problems are arisen in cutting thin aluminum alloys. They are that the modulus of elasticity is turned into smaller and elastic recovery is larger, so the deformation of the part is greater after cutting. [1] It is important to select optimal parameters in machining since they can reduce the temperature. However, the machining parameters are determined based on the past experience in machining shop. This does not mean that the selected machining parameters are optimal or near optimal for the machining performance. [2] This research presents the selection of optimal machining parameters using Taguchi method that reduces the number of experiments. 2. Machining Conditions The specimen designed for the experiment is shown in Fig. 1. The material is AL 7050/T7451 that is widely used for aircraft components. The cutting tool used is a flat end-mill and TiN coated carbide with four flutes. Its diameter is 16mm. The machining trials are carried out on a 323

2 5 axis high-speed vertical machining center DMU 70 evolution manufactured by a German company. The following Table 1 shows the specification of the machine tool. The specimens are measured on a Global Image frame by Global Brown & Sharpe company, with the specification: X stroke 1.5m, Y stroke 2.2m, Z stroke 1.0m; error: 2.8+L/333 µ m. Table 1: The specification of DMU 70 evolution Work Area (mm) X: 750 Y: 600 Z: 520 Work Spindle SK 40 Speed Range (rpm) 30,000 Tool Changer, number of tools 32(60/92) Feed Range (mm/min) 20,000 Rapid traverse, linear axes(m/min) 50 The measuring points are selected in four different positions. Four groups of data are collected from the measurement. They are top-front points, bottom-front points, top-back points, and bottom-back points. Each group of data includes 5 points in a row. The top-front and bottom-front points can be seen in Fig.1, the top-back and bottom-back points are opposite to them, respectively. Analysis is taken on each data. the machining experiments is analyzed using the S/N ratio and ANOVA. Based on the results of analysis, the optimal selection of the machining parameters for heat deformation is carried and verified. 3.1 Taguchi Method The major parameters for machining are cutting speed, feed, and depth of cut. They are factors for the experimental design in this research. Three levels of each factor are shown in the following Table 2. The number of experiments of full factorial design for this research should be 27. But, it is simplified an L9 orthogonal array by the application of Taguchi method. This array has eight degrees of freedom and it can handle three-level design parameters. The experimental layout for machining parameters using the L9 orthogonal array is shown in Table 3. The machining process is shown in Fig.2. Fig.2 The machining process Fig.1 The design of specimen 3. Taguchi Method: Design and Analysis Taguchi method reduces the number of cutting experiments in this research. The data of 3.2 Analysis of the S/N ratio The Taguchi method uses the signal-to-noise (S/N) ratio to convert the trial result data into a value for the evaluation characteristic in the optimum setting analysis. This is because the S/N ratio reflects both the average and the variation of parameter s feature. [3] The data is analyzed by using the Minitab software. Table 4 to 7 show the mean S/N ratio for each level of the machining parameter Table 4 to 7 show actual data for heat deformation with 324

3 computed mean S/N ratio for each level of the machining parameter. [4] The results are calculated by Minitab. Delta means the absolute value between Max. and Min. S/N ratio of each machining parameter. The bigger the delta of parameter is, the bigger the deformation is influenced by this parameter. From the table, the cutting speed has the most important effect on heat deformation. Table 2: Factors and selected levels Factors Level1 Level2 Level3 Speed(rpm) Feed(mm) DOC(mm) Table 3: Experimental layout using an L 9 orthogonal array No Cutting parameter level A B C Speed Feed rate Depth of cut Analysis of variance (ANOVA) The purpose of the analysis of variance (ANOVA) is to investigate parameters which significantly affect the quality characteristic. [2] Table 8 to 11 show the results of ANOVA for heat deformation which are analyzed by the Minitab software. The Seq SS of cutting speed is the highest in the analysis tables except for the ANOVA of bottom-back points. Fig.3 to 6 show the mean effects plots for the heat deformation. The dots in the figures express the results of the deformation. It is also found that the dots in cutting speed columns have the tremendous fluctuations of the deformation. Therefore, cutting speed is the most significant machining parameters affecting the heat deformation. The change of the feed rate has an insignificant effect on heat deformation. Table 4: Response table for average S/N ratio (top-front points) Speed Feed rate DOC Table 5: Response table for average S/N ratio (bottom-front points) Speed Feed rate DOC Table 6: Response table for average S/N ratio (top-back points) Speed Feed rate DOC Table 7: Response table for average S/N ratio (bottom-back points) Speed Feed rate DOC

4 Table 8: Results of ANOVA for heat deformation (top-front points) Cutting Speed Feed DOC Residual Error Total Table 9: Results of ANOVA for heat deformation (bottom-front points) Cutting Speed Feed DOC Residual Error Total Table 10: Results of ANOVA for heat deformation (top-back points) Cutting Speed Feed DOC Residual Error Total Table 11: Results of ANOVA for heat deformation (bottom-back points) Cutting Speed Feed DOC Residual Error Total Conclusion This research presents the experiments on the heat deformation in the high-speed end milling of the material AL 7050/T7451. From the measurements, the specimens are deformed during a milling. Based on the S/N ratio and ANOVA created from analyzing collected data, the factors which are effective to the degree of deformation are the cutting speed, the depth of cut, and the feed rate in order. From the main effect plots it can be observed that the lower the cutting speed is the less deformation is attained. The least deformation is derived by the level of cutting speed in 5000rpm. Also, the higher the DOC is the less the deformation is. But it is displayed not as notable as the cutting speed. The feed rate does a worse influence on the deformation. Therefore, it is clear that the cutting speed must be maintained as lower as possible and the 326

5 DOC may be as high as necessary. The feed rate is less effective for the deformation so much. Mai n Ef f ect s Pl ot ( dat a means) for Resul t (bottom-back) Acknowledgement This work was supported by the Machine Tool Research Center and the MIRAE center of NURI at Mai n Ef f ect s Pl ot ( dat a means) for Resul t (top-front) Speed Feed DOC Fig.6 The mean effects plot for heat deformation (bottom-back points) Fig.3 The mean effects plot for heat deformation (top-front points) Mai n Ef f ect s Pl ot ( dat a means) for Resul t (bottom-front) Fig.4 The mean effects plot for heat deformation (bottom-front points) Mai n Ef f ect s Pl ot ( dat a means) for Resul t (top-back) References [1] Zhang Guimu, Yu Chao, Shi Rong Chen, and An Libao, Experimental study on the milling of then parts of titanium alloy (TC4), Journal of Materials Processing Technology 138 (2003) [2] W.H. Yang, and Y.S. Tarng, Design optimization of cutting parameters for turning operations based on the Taguchi method, Journal of Materials Processing Technology 84 (1984) [3] Tsann-Rong Lin, Experimental design and performance analysis of TiN-coated carbide tool in face milling stainless steel, Journal of Materials Processing Technology 127 (2002) 1-7. [4] J.A. Ghani, I.A. Choudhury, and H.H. Hassan, Application of Taguchi method in the optimization of end milling parameters, Journal of Materials Processing Technology 145 (2004) Fig.5 The mean effects plot for heat deformation (top-back points) 327