Multi-response optimization in cold metal transfer wire arc additive manufacturing of Inconel 718 thin walls
Email:
vtle@lqdtu.edu.vn
Từ khóa:
CMT-WAAM, Inconel 718, thin wall, optimization, COPRAS, WASPAS
Tóm tắt
Wire arc additive manufacturing (WAAM) of Inconel 718 thin walls requires careful parameter selection to balance geometric accuracy and surface quality. In this study, the effects of wire feed speed (wfs) and travel speed (v) on the effective width (EW), total height (TH), and surface roughness (Sa) of 20-layer walls fabricated by cold metal transfer (CMT)-WAAM process were first analyzed through a full factorial experiment design. To resolve the trade-off between productivity and surface integrity, a multi-criteria decision-making framework integrating Entropy weight method (EWM) with COPRAS and WASPAS methods was applied. The analysis of variance (ANOVA) results reveal that wfs primarily governs EW, while v dominates TH and Sa. Both COPRAS and WASPAS optimization techniques consistently identify wfs = 6 m/min and v = 40 cm/min as the optimal condition, achieving high deposition height with acceptable surface roughness. The proposed framework offers a robust strategy for optimization in CMT-WAAM of Inconel 718 superalloys.Tài liệu tham khảo
[1]. F. He, L. Yuan, J. Yang, W. Zhou, Y. Zhang, Z. Pan, H. Li, Process innovations in wire arc additive manufacturing systems: progress, challenges and future opportunities, Progress in Materials Science. 163 (2027) 101776. https://doi.org/10.1016/j.pmatsci.2026.101776
[2]. B. Das, N. Sahoo, P. Patel, A. Ghosh, M.A. Khan, B. Bhushan, R. Madan, P. Shrivastava, V. Msomi, Wire Arc Additive Manufacturing (WAAM): A comprehensive review of process, materials, modelling, artificial intelligence, and industrial applications, Journal of Alloys and Compounds. 1058 (2026) 186347. https://doi.org/10.1016/j.jallcom.2026.186347
[3]. F. Lambiase, P.B. Yanala, F. Pace, E. Andreucci, A. Paoletti, A state of the art review of wire arc additive manufacturing (WAAM)-part 1: process fundamentals, parameters and materials, The International Journal of Advanced Manufacturing Technology. 138 (2025) 4965-4993. https://doi.org/10.1007/s00170-025-15781-8
[4]. V.T. Le, D.M. Dinh, V.-C. Tran, Q.H. Mai, Q.H. Pham, Machine learning-based prediction models of single weld bead attributes in wire arc additive manufacturing of Inconel 625 superalloy and process map generation, International Journal of Pressure Vessels and Piping. 219 (2026) 105659. https://doi.org/10.1016/j.ijpvp.2025.105659
[5]. Q. Shen, J. Xue, Z. Zheng, X. Yu, N. Ou, Machine learning-based prediction of CoCrFeNiMo0.2 high-entropy alloy weld bead dimensions in wire arc additive manufacturing, Materials Today Communications. 41 (2024) 110359. https://doi.org/10.1016/j.mtcomm.2024.110359
[6]. R. Mamedipaka, M. Hemachandra, A. Mishra, S. Sinhmar, S. Thapliyal, Machine learning-assisted wire arc additive manufacturing and heat input effect on mechanical and corrosion behaviour of 316 L stainless steels, Structures. 68 (2024) 107126. https://doi.org/10.1016/j.istruc.2024.107126
[7]. P. Saravanamuthukumar, A.B. Abdullah, Z.K. Wani, Machine learning-based prediction and optimization of WAAM process parameters for enhanced deposition efficiency, Progress in Additive Manufacturing. 10 (2025) 11093-11107. https://doi.org/10.1007/s40964-025-01276-1
[8]. V.T. Le, D.M. Dinh, V.-C. Tran, M. Muzamil, Modelling, analysis, and multi-objective optimization of single weld bead characteristics in wire arc additive manufacturing of Inconel 625 based on machine learning and NSGA-II, Materials Today Communications. 49 (2025) 113831. https://doi.org/10.1016/j.mtcomm.2025.113831
[9]. H. Mirabi, F. Mirakhorli, J.F. Béland, F. Nadeau, V. Demers, P. Bocher, On selecting proper process parameters for cold metal transfer (CMT)-based wire arc additive manufacturing (WAAM) process, International Journal of Advanced Manufacturing Technology. 133 (2024) 6083-6093. https://doi.org/10.1007/s00170-024-14110-9
[10]. D.M. Dinh, V.T. Le, Wire and arc additive manufacturing of Inconel alloys: A review on microstructures, mechanical properties, and common defects, Journal of Science and Technology - HaUI. 60 (2024) 20-23. https://doi.org/10.57001/huih5804.2024.176
[11]. M. Chandra, K.E.K. Vimal, S. Rajak, A comparative study of machine learning algorithms in the prediction of bead geometry in wire-arc additive manufacturing, International Journal on Interactive Design and Manufacturing (IJIDeM). 18 (2024) 6625-6638. https://doi.org/10.1007/s12008-023-01326-4
[12]. D. Ding, F. He, L. Yuan, Z. Pan, L. Wang, M. Ros, The first step towards intelligent wire arc additive manufacturing: An automatic bead modelling system using machine learning through industrial information integration, Journal of Industrial Information Integration. 23 (2021) 100218. https://doi.org/10.1016/j.jii.2021.100218
[13]. Z. Zhang, J. Yan, X. Lu, T. Zhang, H. Wang, Optimization of porosity and surface roughness of CMT-P wire arc additive manufacturing of AA2024 using response surface methodology and NSGA-Ⅱ, Journal of Materials Research and Technology. 24 (2023) 6923-6941. https://doi.org/10.1016/j.jmrt.2023.04.259
[14]. R.M. Kindermann, M.J. Roy, R. Morana, P.B. Prangnell, Process response of Inconel 718 to wire + arc additive manufacturing with cold metal transfer, Materials and Design. 195 (2020) 109031. https://doi.org/10.1016/j.matdes.2020.109031
[15]. R.M. Kindermann, M.J. Roy, R. Morana, J.A. Francis, Effects of microstructural heterogeneity and structural defects on the mechanical behaviour of wire + arc additively manufactured Inconel 718 components, Materials Science and Engineering A. 839 (2022) 142826. https://doi.org/10.1016/j.msea.2022.142826
[16]. F.R. Teixeira, F.M. Scotti, L.O. Vilarinho, C.A.M. da Mota, A. Scotti, Transferability of the working envelope approach for parameter selection and optimization in thin wall WAAM, International Journal of Advanced Manufacturing Technology. 119 (2022) 969-989. https://doi.org/10.1007/s00170-021-08326-2
[17]. D.M. Dinh, H.S. Lam, V.T. Le, Q.H. Pham, Evaluation of the forming quality of Inconel 625 thin-walled parts manufactured via cold metal transfer additive manufacturing, Science & Technology Development Journal. 27 (2024) 3570-3582. https://doi.org/10.32508/stdj.v27i3.4308
[18]. V.T. Le, L. Hoang, M.F. Ghazali, V.T. Le, M.T. Do, T.T. Nguyen, T.S. Vu, Optimization and comparison of machining characteristics of SKD61 steel in powder-mixed EDM process by TOPSIS and desirability approach, The International Journal of Advanced Manufacturing Technology. 130 (2024) 403-424. https://doi.org/10.1007/s00170-023-12680-8
[19]. S.H. Mousavi-Nasab, A. Sotoudeh-Anvari, A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems, Materials & Design. 121 (2017) 237-253. https://doi.org/10.1016/j.matdes.2017.02.041
[20]. A. Radomska-Zalas, Application of the WASPAS method in a selected technological process, Procedia Computer Science. 225 (2023) 177-187. https://doi.org/10.1016/j.procs.2023.10.002
[21]. J. Shi, N. Qian, S. Sun, Y. Kaynak, R. Das, Y. Fu, H. Su, Effect of process parameters on microstructure and properties of Inconel-718 superalloy fabricated by wire-arc direct energy deposition technique, Journal of Materials Research and Technology. 37 (2025) 173-185. https://doi.org/10.1016/j.jmrt.2025.06.026
[2]. B. Das, N. Sahoo, P. Patel, A. Ghosh, M.A. Khan, B. Bhushan, R. Madan, P. Shrivastava, V. Msomi, Wire Arc Additive Manufacturing (WAAM): A comprehensive review of process, materials, modelling, artificial intelligence, and industrial applications, Journal of Alloys and Compounds. 1058 (2026) 186347. https://doi.org/10.1016/j.jallcom.2026.186347
[3]. F. Lambiase, P.B. Yanala, F. Pace, E. Andreucci, A. Paoletti, A state of the art review of wire arc additive manufacturing (WAAM)-part 1: process fundamentals, parameters and materials, The International Journal of Advanced Manufacturing Technology. 138 (2025) 4965-4993. https://doi.org/10.1007/s00170-025-15781-8
[4]. V.T. Le, D.M. Dinh, V.-C. Tran, Q.H. Mai, Q.H. Pham, Machine learning-based prediction models of single weld bead attributes in wire arc additive manufacturing of Inconel 625 superalloy and process map generation, International Journal of Pressure Vessels and Piping. 219 (2026) 105659. https://doi.org/10.1016/j.ijpvp.2025.105659
[5]. Q. Shen, J. Xue, Z. Zheng, X. Yu, N. Ou, Machine learning-based prediction of CoCrFeNiMo0.2 high-entropy alloy weld bead dimensions in wire arc additive manufacturing, Materials Today Communications. 41 (2024) 110359. https://doi.org/10.1016/j.mtcomm.2024.110359
[6]. R. Mamedipaka, M. Hemachandra, A. Mishra, S. Sinhmar, S. Thapliyal, Machine learning-assisted wire arc additive manufacturing and heat input effect on mechanical and corrosion behaviour of 316 L stainless steels, Structures. 68 (2024) 107126. https://doi.org/10.1016/j.istruc.2024.107126
[7]. P. Saravanamuthukumar, A.B. Abdullah, Z.K. Wani, Machine learning-based prediction and optimization of WAAM process parameters for enhanced deposition efficiency, Progress in Additive Manufacturing. 10 (2025) 11093-11107. https://doi.org/10.1007/s40964-025-01276-1
[8]. V.T. Le, D.M. Dinh, V.-C. Tran, M. Muzamil, Modelling, analysis, and multi-objective optimization of single weld bead characteristics in wire arc additive manufacturing of Inconel 625 based on machine learning and NSGA-II, Materials Today Communications. 49 (2025) 113831. https://doi.org/10.1016/j.mtcomm.2025.113831
[9]. H. Mirabi, F. Mirakhorli, J.F. Béland, F. Nadeau, V. Demers, P. Bocher, On selecting proper process parameters for cold metal transfer (CMT)-based wire arc additive manufacturing (WAAM) process, International Journal of Advanced Manufacturing Technology. 133 (2024) 6083-6093. https://doi.org/10.1007/s00170-024-14110-9
[10]. D.M. Dinh, V.T. Le, Wire and arc additive manufacturing of Inconel alloys: A review on microstructures, mechanical properties, and common defects, Journal of Science and Technology - HaUI. 60 (2024) 20-23. https://doi.org/10.57001/huih5804.2024.176
[11]. M. Chandra, K.E.K. Vimal, S. Rajak, A comparative study of machine learning algorithms in the prediction of bead geometry in wire-arc additive manufacturing, International Journal on Interactive Design and Manufacturing (IJIDeM). 18 (2024) 6625-6638. https://doi.org/10.1007/s12008-023-01326-4
[12]. D. Ding, F. He, L. Yuan, Z. Pan, L. Wang, M. Ros, The first step towards intelligent wire arc additive manufacturing: An automatic bead modelling system using machine learning through industrial information integration, Journal of Industrial Information Integration. 23 (2021) 100218. https://doi.org/10.1016/j.jii.2021.100218
[13]. Z. Zhang, J. Yan, X. Lu, T. Zhang, H. Wang, Optimization of porosity and surface roughness of CMT-P wire arc additive manufacturing of AA2024 using response surface methodology and NSGA-Ⅱ, Journal of Materials Research and Technology. 24 (2023) 6923-6941. https://doi.org/10.1016/j.jmrt.2023.04.259
[14]. R.M. Kindermann, M.J. Roy, R. Morana, P.B. Prangnell, Process response of Inconel 718 to wire + arc additive manufacturing with cold metal transfer, Materials and Design. 195 (2020) 109031. https://doi.org/10.1016/j.matdes.2020.109031
[15]. R.M. Kindermann, M.J. Roy, R. Morana, J.A. Francis, Effects of microstructural heterogeneity and structural defects on the mechanical behaviour of wire + arc additively manufactured Inconel 718 components, Materials Science and Engineering A. 839 (2022) 142826. https://doi.org/10.1016/j.msea.2022.142826
[16]. F.R. Teixeira, F.M. Scotti, L.O. Vilarinho, C.A.M. da Mota, A. Scotti, Transferability of the working envelope approach for parameter selection and optimization in thin wall WAAM, International Journal of Advanced Manufacturing Technology. 119 (2022) 969-989. https://doi.org/10.1007/s00170-021-08326-2
[17]. D.M. Dinh, H.S. Lam, V.T. Le, Q.H. Pham, Evaluation of the forming quality of Inconel 625 thin-walled parts manufactured via cold metal transfer additive manufacturing, Science & Technology Development Journal. 27 (2024) 3570-3582. https://doi.org/10.32508/stdj.v27i3.4308
[18]. V.T. Le, L. Hoang, M.F. Ghazali, V.T. Le, M.T. Do, T.T. Nguyen, T.S. Vu, Optimization and comparison of machining characteristics of SKD61 steel in powder-mixed EDM process by TOPSIS and desirability approach, The International Journal of Advanced Manufacturing Technology. 130 (2024) 403-424. https://doi.org/10.1007/s00170-023-12680-8
[19]. S.H. Mousavi-Nasab, A. Sotoudeh-Anvari, A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems, Materials & Design. 121 (2017) 237-253. https://doi.org/10.1016/j.matdes.2017.02.041
[20]. A. Radomska-Zalas, Application of the WASPAS method in a selected technological process, Procedia Computer Science. 225 (2023) 177-187. https://doi.org/10.1016/j.procs.2023.10.002
[21]. J. Shi, N. Qian, S. Sun, Y. Kaynak, R. Das, Y. Fu, H. Su, Effect of process parameters on microstructure and properties of Inconel-718 superalloy fabricated by wire-arc direct energy deposition technique, Journal of Materials Research and Technology. 37 (2025) 173-185. https://doi.org/10.1016/j.jmrt.2025.06.026
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25/03/2026
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03/11/2026
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10/11/2026
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15/11/2026
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Kiểu trích dẫn
Van Thao, L., Chi Tinh, P., Manh Tung, D., Dinh Si, M., Van Son, N., Ba Tinh, D., Manh Cuong, B., & Tat Khoa, D. (1794675600). Multi-response optimization in cold metal transfer wire arc additive manufacturing of Inconel 718 thin walls. Tạp Chí Khoa Học Giao Thông Vận Tải, 77(9), 1211-1225. https://doi.org/10.47869/tcsj.77.9.7
