Investigation of CNC Lathe Machining Parameters for AMS 5643 using Taguchi-RSM with CAM Simulation Approach

• Autorzy: Ismartaya Kristian , Limanta Dimitri Manggala , Nugraha Aditya , Sukwadi Ronald , Yang Ching-Chow

Identyfikatory

DOI

10.24425/mper.2025.153931

• Języki publikacji: EN

Abstrakty

EN

Aerospace components use AMS 5643 stainless steel as a raw material. Material toughness and hardness are challenges in the roughing machining process on CNC lathes. We designed experiments combining Taguchi-Response Surface Method to optimize multi-response: cycle time, material removal rate, and cutting power. This study uses CAM Espirit TNG and Celos Tech software simulations as an experimental approach. Confirmation test results show that changing process parameters in simulation software is able to produce a response that is close to reality. This research succeeded in identifying the contribution of machining process factors and finding parameters with optimal multi-response.

Słowa kluczowe

EN

aerospace component; AMS 5643; cam simulation; optimization; taguchi-rsm

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2025
• Tom: Vol. 16, No. 1
• Strony: 1--14
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wykr., zdj.

Twórcy

autor

Ismartaya Kristian

• Duta Wacana Christian University, Product Design, Indonesia

autor

Limanta Dimitri Manggala

• Polytechnic of ATMI Surakarta, Manufacture Technology Engineering, Indonesia

autor

Nugraha Aditya

• Polytechnic of ATMI Surakarta, Manufacture Technology Engineering, Indonesia

autor

Sukwadi Ronald

• Faculty of Engineering, Atma Jaya Catholic University of Indonesia, Jl. Jenderal Sudirman 51, 12930, Jakarta, Indonesia

autor

Yang Ching-Chow

• Chung Yuan Christian University, Industrial & Systems Engineering, Taiwan

Bibliografia

• Abhang, L.B., & Hameedullah, M. (2021). Modeling and Analysis of Surface Roughness with Statistical and Soft Computing Approach. Advances in Science and Technology, 106, 109–115. DOI: 10.4028/www.scien tific.net/AST.106.109
• Altin, A. (2023). A Precision Analysis of Machining Incoloy 901 Aerospace Materials with Cemented Carpide Tools by the Taguchi Method. Materials Science Forum, 1085, 133–137. DOI: 10.4028/p-69a714
• An, S., Eo, D., Sohn, I., & Choi, K. (2023). Homogenization on solution treatment and its effects on the precipitation-hardening of selective laser melted 17-4PH stainless steel. Journal of Materials Science & Technology, 166, 47–57. DOI: 10.1016/j.jmst.2023.04.055
• Biradar, J., Rao, P.D., & Kiran, C.U. (2014). Reducing Machining Time by Using Modern Manufacturing Software. International Journal of Engineering Development and Research, 2(4), 3619–3626. ISSN: 2321-9939
• But, A. (2019). Design for manufacturing using TEBIS CAM software for milling processing. IOP Conf. Ser.: Mater. Sci. Eng, 564, 012056. DOI: 10.1088/1757-899X/564/1/012056
• Daniyan, I., Muvunzi, R., Mpofu, K., & Adeodu. A. (2023). Optimisation of Process Parameters during the Turning Operation of Titanium Alloy (Ti6Al4V) using the Taguchi Methodology. Procedia CIRP, 118, 408–413. DOI: 10.1016/j.procir.2023.06.070
• Eliaz, N., Foucks, N., Geva, D., Oren, S., Shriki, N., Vaknin, D., Fishman, D., & Levi, O. (2020). Comparative Quality Control of Titanium Alloy Ti–6Al–4V, 17–4 PH Stainless Steel, and Aluminum Alloy 4047 Either Manufactured or Repaired by Laser Engineered Net Shaping (LENS). Materials, 13, 4171. DOI: 10.3390/ma13184171
• Faisal M.H., Krishnan A.M., Prabagaran, S., Venkatesh, R., Kumar, D.S., Christysudha, J., Seikh, A.H., Iqbal, A., & Ramaraj, E. (2023). Optimization and prediction of CBN tool life sustainability during AA1100 CNC turning by response surface methodology. Heliyon, 9, e18807. DOI: 10.1016/j.heliyon.2023.e18807
• Gercekcioglu, E., & Albaskara, M. (2023). Multi-response optimization of electrical discharge machining of 17-4 PH SS using taguchi-based grey relational analysis. Arch Metall Mater, 68(3), 861–868. DOI: 10.24425/amm.2023.145448
• Giganto, S., Martinez-Pellitero, S., Barreiro, J., Leo, P., & Castro-Sastre, M.A. (2022). Impact of the laser scanning strategy on the quality of 17-4PH stainless steel parts manufactured by selective laser melting. Journal of Materials Research and Technology, 20, 2734–2747. DOI: 10.1016/j.jmrt.2022.08.040
• Gopal, M., Gutema, E.M., & Solomon, Y. (2022). Experimental Investigation of Machining Time and Optimization of Machining Parameters Using RSM and Genetic Algorithm on 2205 – Duplex Stainless Steel. International Journal of Engineering Research in Africa, 60, 1–13. DOI: 10.4028/p-9933yq
• Gupta, K. (2022). Hybrid Optimization for Machinability Enhancement during Green Machining of Stainless Steel. Key Engineering Materials, 910, 411–420. DOI: 10.4028/p-3w62bv
• Gupta, P., Singh, B., & Shrivastava, Y. (2022). Grey relational analysis for optimal process variables during turning on CNC lathe. Materials Today: Proceedings. 51(1), 228–233. DOI: 10.1016/j.matpr.2021.05.259
• Hoesen, Y.A., Furqon, M., Novrinaldi, Hanifah, U., Arifudin, N. (2024). A CNC Turning Process Simulation for a Polygonal Shaft Using CAM ESPRIT Software. Eng. Proc. 63, 20. DOI: 10.3390/engproc2024063020
• Sivam, S.P.S.S., Loganathan, G.B., Kumaran, D., Saravanan, K., & RajendraKumar, S. (2019). Analysis of Product Quality Through Mechanical Properties and Determining Optimal Process Parameters of Untreated and Heat-Treated AISI 1050 Alloy during Turning Operation. Materials Science Forum, 969, 876–881. DOI: 10.4028/www.scientific.net/MSF.969.876
• Vadivel, M.A.S., Selvakumar, P., Mathan, P., & Ramkumar, P. (2022). Impact of Single and Duplex Cryogenic Jets on Mechanical and Physical Characteristics in Turning of Titanium Superalloys. Key Engineering Materials, 935, 125–137. DOI: 10.4028/p-g5164d
• Vasudevan, H., Rajguru, R.R., MoeizShaikh, & Shaikh, A. (2019). Optimization of Process Parameters in the Turning Operation of Inconel 625. Materials Science Forum, 969, 756–761. DOI: 10.4028/www.scientific.net/MSF.969.756
• VeeraBhadraRao, M., Patil, B.T., Shaikh, V.A., & Sudhakar, D.S.S. (2021). Contribution of Factors such as Machining Parameters, MQL Nozzle Orientation (Angle & Distance) and MQL Nano-Fluid Type on Surface Finish of Turned Steel Work-Pieces Using DOE Approach. Materials Science Forum, 1019, 181-193. DOI: 10.4028/www.scientific.net/MSF.1019.181
• Viswanathan, R., Ramesh, S., Maniraj, S., & Subburam, V. (2020). Measurement and Multi-response Optimization of Turning Parameters for Magnesium Alloy Using Hybrid Combination of Taguchi-GRA-PCA Technique. Measurement, 159. ISSN 0263-2241. DOI: 10.1016/j.measurement.2020.107800
• Zahid, M.N.O., Case, K., & Watts, D. (2014). Optimization of roughing operations in CNC machining for rapid manufacturing processes. Production & Manufacturing Research, 2(1), 519–529. DOI: 10.1080/21693277.2014.938277
• Zhujani, F., Todorov, G., Kamberov, K., & Abdullahu, F. (2023). Mathematical Modeling and Optimization of Machining Parameters in CNC Turning Process of Inconel 718 Using the Taguchi Method. Journal of Engineering Research. DOI: 10.1016/j.jer.2023.10.029