Sudianto, Agus (2024) Modeling and optimization of the end milling process for Aluminum Alloy (AA6041) using response surface method. Doctoral thesis, Universiti Teknikal Malaysia Melaka.
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Abstract
End milling process is among the most widely used method in machining of components for industrial needs and purposes. Manufacturers are faced with greater demands for precision, quality, and efficiency of production process. This has raised the needs for establishing optimal machining process measured by the quality of the dependent responses such as the surface finishing quality, cutting temperature, and the generated cutting force. This thesis presented work on end milling process parameters modelling and optimization that considered the cutting speed, feed rate, depth of cut, width of cut and number of flute of high precision machining type tool on a 3-axes Computer Numerical Control (CNC) machining centre. The specific material of concerned was aluminium alloy AA6041 which made up the connecting rod of an automotive engine component. The screening phase applied the Minitab statistical tool using the Taguchi method with regression analysis for the surface roughness response that identified cutting speed, feed rate and depth of cut for final consideration of optimal end milling process parameters based on a coefficient p-value of less than 0.05. In the second phase, optimization and modelling were performed using Design Expert software with Response Surface Method (RSM). Results of the three optimal response values were analysed in ANOVA using the analysis of variance based on quadratic model with randomized Box-Behnken method that generated three regression equations whereby one optimization test and three validation tests were performed. Results were compared with Function Block and Python Program. The ANOVA analyses have identified optimal cutting speed, feed rate, and depth of cut at 155 m/min, 708.256 mm/min, 0.306 mm respectively. The predicted responses in the forms of surface roughness value (Ra), cutting temperature (Tc), and cutting force (Fc) were measured using Mitutoyo surface roughness tester, infrared thermometer sensor MLX90614 and a Kistler dynamometer respectively. The optimized cutting parameters produced predictive errors of 1.16%, 0.11%, and 8.12% while the validation machining process produced predicted error values of 4.168%, 0.819% and 11.171% for surface roughness, cutting temperature, and cutting force respectively. These findings will contribute toward improvements in machining efficiency of metal-based manufacturing process industries. In future, further analysis on impacts of other cutting parameter such as cutting tool geometry can be included and analysed using finite element method embedded with artificial intelligent elements.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Milling- machines, Surface roughness |
| Divisions: | Library > Tesis > FTKIP |
| Depositing User: | Muhamad Hafeez Zainudin |
| Date Deposited: | 21 Jan 2026 08:06 |
| Last Modified: | 21 Jan 2026 08:06 |
| URI: | http://eprints.utem.edu.my/id/eprint/29180 |
| Statistic Details: | View Download Statistic |
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