Multi-objective optimization of the rotary turning of hardened mold steel for energy saving and surface roughness improvements

• Autorzy: Doan Tat Khoa , Nguyen Trung Thanh , Van An-Le

Identyfikatory

DOI

10.36897/jme/172877

• Języki publikacji: EN

Abstrakty

EN

In this investigation, the specific cutting energy (SCE) and average surface roughness (R_a) were decreased using the hard-rotary turning (HRT) factors, including the inclined angle (I), depth of cut (D), feed rate (f), and spindle speed (S). The Bayesian regularized feed-forward neural network was applied to develop the SCE and R_a models. The entropy method and vibration and communication particle swarm optimization (VCPSO) algorithm were employed to compute the weights and determine optimal factors. The optimizing outcomes presented that the optimal I, D, f, and S were 35 deg., 0.45 mm, 0.50 mm/rev., and 1200 rpm, respectively, while the SCE and R_a were decreased by 37.4% and 6.6%, respectively. The total turning cost was saved by 7.5% at the selected solution. The valuable outcomes could be applied to the practical HRT process to decrease performance measures, while the developed HRT operation could be utilized for machining difficult-to-cut materials.

Słowa kluczowe

EN

rotary turning; specific cutting energy; surface roughness; VCPSO

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2023
• Tom: Vol. 23, No. 4
• Strony: 101--121
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Doan Tat Khoa

• Mechanical Engineering, Le Quy Don Technical University, Vietnam

• https://orcid.org/0000-0002-3023-5826

autor

Nguyen Trung Thanh

• Mechanical Engineering, Le Quy Don Technical University, Vietnam

• https://orcid.org/0000-0002-0838-872X

autor

Van An-Le

• Faculty of Engineering and Technology, Nguyen Tat Thanh University, Vietnam

• https://orcid.org/0000-0002-3592-8541

Bibliografia

• [1] DESSOLY V., MELKOTE S.N., LESCALIER C. 2004, Modeling and Verification of Cutting Tool Temperatures in Rotary Tool Turning of Hardened Steel, Int. J. Mach. Tools Manuf., 44, 1463–1470.
• [2] KISHAWY H.A., WILCOX J., 2003, Tool Wear and Chip Formation During Hard Turning with Self-Propelled Rotary Tools, Int. J. Mach. Tools Manuf., 43, 433–439.
• [3] KISHAWY H.A., PANG L., BALAZINSKI M., 2011, Modeling of Tool Wear during Hard Turning with Self-Propelled Rotary Tools, Int. J. Mech. Sci., 2011, 53, 1015–1021.
• [4] KISHAWY H.A., BECZE C.E., MCINTOSH D.G., 2044, Tool Performance and Attainable Surface Quality During the Machining of Aerospace Alloys Using Self-Propelled Rotary Tools, J. Mater. Process. Technol., 152, 266–271.
• [5] WANG S.H., ZHU X., Li X., TURYAGYENDA G., 2006, Prediction of Cutting Force for Self-Propelled Rotary Tool Using Artificial Neural Networks, J. Mater. Process. Technol., 180, 23–29.
• [6] LI L., KISHAWY H.A. 2006, A Model for Cutting Forces Generated During Machining with Self-Propelled Rotary Tools, Int. J. Mach. Tools. Manuf., 46, 1388–1394.
• [7] EZUGWU E.O., 2207, Improvements in The Machining of Aero-Engine Alloys using Self-Propelled Rotary Tooling Technique, J. Mater. Process. Technol., 185, 60–71.
• [8] RAO T.B., KRISHNA A.G., KATTA R.K., KRISHNA K.R. 2015, Modeling and Multi-Response Optimization of Machining Performance While Turning Hardened Steel with Self-Propelled Rotary Tool, Adv. Manuf., 3, 84–95.
• [9] AMINI S., TEIMOURI R., 2017, Parametric Study and Multicharacteristic Optimization of Rotary Turning Process Assisted by Longitudinal Ultrasonic Vibration, Proc. Inst. Mech. Eng. E: J. Process Mech. Eng., 231/5, 978–991.
• [10] NGUYEN T.T., 2020, An Energy-Efficient Optimization of the Hard Turning Using Rotary Tool, Neural Comput. & Applic., 33, 2621–2644.
• [11] NGUYEN T.T., DUONG Q.D., MIA M., 2020, Sustainability-Based Optimization of the Rotary Turning of the Hardened Steel, Metals, 10, 939.
• [12] UMER U., KISHAWY H., ABIDI M.H., MIAN S.H., MOIDUDDIN K., 2020, Evaluation of Self-Propelled Rotary Tool in the Machining of Hardened Steel using Finite Element Models, Materials, 13, 5092.
• [13] AHMED W., HEGAB H., MOHANY A., KISHAWY H., 2021, Analysis and Optimization of Machining Hardened Steel AISI 4140 with Self-Propelled Rotary Tools. Materials, 14, 6106.
• [14] NIESLONY P., KROLCZYK G., CHUDY R., WOJCIECHOWSKI S., MARUDA R., BILOUS P., LIPOWCZYK M., STACHOWIAK L., 2020, Study on Physical and Technological Effects of Precise Turning with Self-Propelled Rotary Tool, Precis. Eng., 66, 62–75.
• [15] THELLAPUTTA G.R., BOSE P., RAO C., RAJU C., 2019, Effect of Machining Variables on Cutting Temperature While Rotary Milling of Inconel 625, Recent Advances in Material Sciences, 27–36.
• [16] AHMED W., HEGAB H., KISHAWY H., MOHANY A., 2021, Estimation of Temperature in Machining with Self-Propelled Rotary Tools Using Finite Element Method, J. Manuf. Process., 61, 100–110.
• [17] AHMED W., HEGAB H., MOHANY A., KISHAWY H., 2021, On Machining Hardened Steel AISI 4140 with Self-Propelled Rotary Tools: Experimental Investigation and Analysis, Int. J. Adv. Manuf. Technol., 113, 3163–3176.
• [18] UMER U., MIAN S.H., MOHAMMED M.K., ABIDI M.H. MOIDUDDIN K., KISHAWY H., 2022, Self-Propelled Rotary Tools in Hard Turning: Analysis and Optimization via Finite Element Models, Materials, 15/24, 8781.
• [19] TRUNG D.D., 2021, Application of TOPSIS an PIV Methods for Multi – Criteria Decision Making in Hard Turning Process, Journal of Machine Engineering, 21/4, 57–71, https://doi.org/10.36897/jme/142599.
• [20] NGUYEN A., NGUYEN V., LE T., NGUYEN N., 2023, A Hybridization of Machine Learning and NSGA-II for Multi-Objective Optimization of Surface Roughness and Cutting Force in ANSI 4340 Alloy Steel Turning, Journal of Machine Engineering, 23/1, 133–153, https://doi.org/10.36897/jme/160172.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).