journal article·2024-11

Optimisation of interlayer temperature in wire-arc additive manufacturing process using NURBS-based metamodel

Mathilde Zani, Enrico Panettieri, Marco Montemurro

Publisher page

Abstract

For wire arc additive manufacturing (WAAM) process the interlayer temperature highly influences the quality of manufactured parts. This paper proposes an optimisation of deposition parameters for a better control of interlayer temperature while reducing the printing time employing a Finite Element (FE) model and a metamodel based on Non Uniform Rational Basis Splines (NURBS) entities for a thin-walled part in aluminium alloy. Firstly, the thermal FE model is created to extract the interlayer temperature as a function of different deposition parameters that will be optimised. These parameters are the wire feed speed and the cooling time between deposition of two consecutive layers. Then, a NURBS-based metamodel is generated to approximate the (unknown) transfer function between input variables and output responses of the problem at hand. One of the advantages of this metamodeling strategy is the possibility of obtaining the gradient of the output responses without the requirement of further computational resources, as the resulting metamodel is available in analytical form with the requisite continuity and differentiability. The NURBS-based metamodel is generated as a solution of a three-step optimisation strategy aiming at determining all the parameters defining the shape of the NURBS entity. Finally, the NURBS-based metamodel is included in the optimisation process related to the considered application. The optimisation problem is defined as a weighted sum of different criteria, i.e., total printing time and the average interlayer temperature difference for each layer. The solution obtained is subsequently validated a posteriori using the high-fidelity FE model, demonstrating an excellent agreement between the prediction of the NURBS-based metamodel and those of the FE model.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (50)

Show source-provided references
1. Ding D, Pan Z, Cuiuri D, Li H (2014) A tool-path generation strategy for wire and arc additive manufacturing. Int J Adv Manuf Technol 73(1–4):173–18310.1007/s00170-014-5808-5 ↗
2. Moreira CA, Caicedo MA, Cervera M, Chiumenti M, Baiges J (2022) A multi-criteria h-adaptive finite-element framework for industrial part-scale thermal analysis in additive manufacturing processes. Eng Comput 38(6):4791–481310.1007/s00366-022-01655-0 ↗
3. Pant H, Arora A, Gopakumar GS, Chadha U, Saeidi A, Patterson AE (2023) Applications of wire arc additive manufacturing (waam) for aerospace component manufacturing. Int J Adv Manuf Technol, pp 1–1710.1007/s00170-023-11623-7 ↗
4. Lockett H, Ding J, Williams S, Martina F (2017) Design for wire + arc additive manufacture: design rules and build orientation selection. J Eng Des 28(7–9):568–59810.1080/09544828.2017.1365826 ↗
5. Rodrigues TA, Duarte V, Miranda RM, Santos TG, Oliveira JP (2019) Current status and perspectives on wire and arc additive manufacturing (WAAM). Mater 12(7)10.3390/ma12071121 ↗
6. Cambon C (2021) Étude thermomécanique du procédé de fabrication métallique arc-fill: approche numérique et expérimentale. Université Montpellier, Theses
7. Ding D, Pan Z, Cuiuri D, Li H (2015) Wire-feed additive manufacturing of metal components: technologies, developments and future interests. Int J Adv Manuf Technol 81(1–4):465–48110.1007/s00170-015-7077-3 ↗
8. Wu B, Ding D, Pan Z, Cuiuri D, Li H, Han J, Fei Z (2017) Effects of heat accumulation on the arc characteristics and metal transfer behavior in Wire Arc Additive Manufacturing of Ti6Al4V. J Mater Process Technol 250:304–31210.1016/j.jmatprotec.2017.07.037 ↗
9. Limousin M, Manokruang S, Vignat F, Museau M, Grandvallet C, Béraud N (2023) Effect of temperature and substrate geometry on single aluminium weld bead geometry deposited by wire arc additive manufacturing: Proposition of an experimental procedure. CIRP J Manuf Sci Technol 45:61–6810.1016/j.cirpj.2023.06.010 ↗
10. Teixeira FR, Scotti FM, Jorge VL, Scotti A (2023) Combined effect of the interlayer temperature with travel speed on features of thin wall waam under two cooling approaches. Int. J Adv Manuf Technol 126(1):273–28910.1007/s00170-023-11105-w ↗
11. Derekar K, Lawrence J, Melton GB, Addison A, Zhang X, Xu L (2019) Influence of interpass temperature on wire arc additive manufacturing (waam) of aluminium alloy components. In MATEC Web of Conferences, pp 5001. EDP Sci10.1051/matecconf/201926905001 ↗
12. Chergui MA (2021) Simulation Based deposition Strategies Evaluation and Optimization in Wire Arc Additive Manufacturing. Université Grenoble Alpes, Theses
13. Béraud N, Chergui A, Limousin M, Villeneuve F, Vignat F (2022) An indicator of porosity through simulation of melt pool volume in aluminum wire arc additive manufacturing. Mech Ind 2310.1051/meca/2021052 ↗
14. Kovšca D, Starman B, Klobčar D, Halilovič M, Mole N (2023) Towards an automated framework for the finite element computational modelling of directed energy deposition. Finite Elem Anal Des 221:10394910.1016/j.finel.2023.103949 ↗
15. Tapia G, Khairallah S, Matthews M, King WE, Elwany A (2018) Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316l stainless steel. Int J Adv Manuf Technol 94:3591–360310.1007/s00170-017-1045-z ↗
16. Montevecchi F, Venturini G, Grossi N, Scippa A, Campatelli G (2018) Idle time selection for wire-arc additive manufacturing: a finite element-based technique. Addit Manuf 2110.1016/j.addma.2018.01.007 ↗
17. Leiy Y, XIONGJ L (2018) Effectofinterlayeridletimeon thermalbehaviorformulti-layersingle-passthin-walledparts in gmaw-based additive manufacturing. Int J Adv Manuf Technol 96(1):210.1007/s00170-018-1699-1 ↗
18. Denlinger ER, Heigel JC, Michaleris P, Palmer TA (2015) Effect of inter-layer dwell time on distortion and residual stress in additive manufacturing of titanium and nickel alloys. J Mater Process Technol 215:123–13110.1016/j.jmatprotec.2014.07.030 ↗
19. Zhao H, Zhang G, Yin Z, Wu L (2013) Effects of interpass idle time on thermal stresses in multipass multilayer weld-based rapid prototyping. J Manuf Sci Eng 135(1):01101610.1115/1.4023363 ↗
20. Zani M, Panettieri E, Montemurro M (2024) A metamodel of the wire arc additive manufacturing process based on basis spline entities. Eng Comput, 40: 2037–206110.1007/s00366-023-01926-4 ↗
21. Vuillod B, Zani M, Hallo L, Montemurro M (2024) Handling noise and overfitting in surrogate models based on non-uniform rational basis spline entities. Comput Methods Appl Mech Eng 425:11691310.1016/j.cma.2024.116913 ↗
22. Chiappetta M, Piazzola C, Carraturo M, Tamellini L, Reali A, Auricchio F (2023) Sparse-grids uncertainty quantification of part-scale additive manufacturing processes. Int J Mech Sci 256:10847610.1016/j.ijmecsci.2023.108476 ↗
23. Jiang P, Wang C, Zhou Q, Shao X, Shu L, Li X (2016) Optimization of laser welding process parameters of stainless steel 316l using fem, kriging and nsga-ii. Adv Eng Softw 99:147–16010.1016/j.advengsoft.2016.06.006 ↗
24. García-Merino JC, Calvo-Jurado C, Martínez-Pañeda E, García-Macías E (2023) Multielement polynomial chaos kriging-based metamodelling for bayesian inference of non-smooth systems. Appl Math Model 116:510–53110.1016/j.apm.2022.11.039 ↗
25. Zhao L, Wang P (2019) An efficient kriging modeling method for high-dimensional design problems based on maximal information coefficient10.1007/s00158-019-02342-3 ↗
26. Kudela J, Matousek R (2022) Recent advances and applications of surrogate models for finite element method computations: a review. Soft Comput 2610.1007/s00500-022-07362-8 ↗
27. Shi Z, Mamun AA, Kan C, Tian W, Liu C (2022) An lstm-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing. J Intell Manuf, pp 1–1710.1007/s10845-021-01879-9 ↗
28. Wang X, Zhang C, Li P, Wang K, Hu Y, Zhang P, Liu H (2012) Modeling and optimization of joint quality for laser transmission joint of thermoplastic using an artificial neural network and a genetic algorithm. Opt Lasers Eng 50(11):1522–153210.1016/j.optlaseng.2012.06.008 ↗
29. Wang Z, Zimmer-Chevret S, Léonard F, Abba G (2021) Prediction of bead geometry with consideration of interlayer temperature effect for cmt-based wire-arc additive manufacturing. Weld World 65(12):2255–226610.1007/s40194-021-01192-2 ↗
30. Saves P, Lafage R, Bartoli N, Diouane Y, Bussemaker J, Lefebvre T, Hwang JT, Morlier J, Martins JRRA (2024) SMT 2.0: A surrogate modeling toolbox with a focus on hierarchical and mixed variables gaussian processes. Adv Eng Sofw 18810.1016/j.advengsoft.2023.103571 ↗
31. Chen G, Zhang K, Xue X, Zhang L, Yao C, Wang J, Yao J (2022) A radial basis function surrogate model assisted evolutionary algorithm for high-dimensional expensive optimization problems. Appl Soft Comput 11610.1016/j.asoc.2021.108353 ↗
32. Li F, Shang Z, Liu Y, Shen H, Jin Y (2024) Inverse distance weighting and radial basis function based surrogate model for high-dimensional expensive multi-objective optimization. Appl Soft Comput 15210.1016/j.asoc.2023.111194 ↗
33. Bai R, Liang G, Cheng H, Naceur H, Coutellier D, Zhao J, Luo J, Pu H, Yi J (2023) Optimizing additive manufacturing path pattern for ti-6al-4v thin rods using a combinatorial radial basis function surrogate-assisted genetic algorithm. Mater Des 236:11244710.1016/j.matdes.2023.112447 ↗
34. Audoux Y, Montemurro M, Pailhès J (2020) Non-Uniform Rational Basis Spline hyper-surfaces for metamodelling. Comput Methods Appl Mech Eng 364:11291810.1016/j.cma.2020.112918 ↗
35. Piegl L, Tiller W (1996) The NURBS book. Springer Science and Business Media10.1007/978-3-642-97385-7 ↗
36. Naidu DS, Ozcelik S, Moore KL (2003) Chapter 2 - Gas Metal Arc Welding: Modeling. In: Ozcelik Selahattin, Moore Kevin L (eds) Desineni Subbaram Naidu. Modeling, Sensing and Control of Gas Metal Arc Welding, Elsevier Science Ltd, Oxford, pp 9–93
37. Chergui A, Villeneuve F, Béraud N, Vignat F (2022) Thermal simulation of wire arc additive manufacturing: a new material deposition and heat input modelling. Int J Interact Des Manuf 16(1):227–23710.1007/s12008-021-00824-7 ↗
38. Goldak J, Chakravarti A, Bibby M (1984) A new finite element model for welding heat sources. Metall Trans B 15(2):299–30510.1007/BF02667333 ↗
39. Bonifaz EA, Mena A (2021) A Directed Energy Deposition Additive Manufacturing Process Simulated with ABAQUS AM Modeler. Int J Robotic Eng 6(1)10.35840/2631-5106/4134 ↗
40. Smith M (2020) ABAQUS/Standard User’s Manual, Version 6.20. Dassault Systèmes Simulia Corp, United States
41. Song X, Feih S, Zhai W, Sun CN, Li F, Maiti R, Wei J, Yang Y, Oancea V, Brandt LR, Korsunsky AM (2020) Advances in additive manufacturing process simulation: Residual stresses and distortion predictions in complex metallic components. Mater Des 19310.1016/j.matdes.2020.108779 ↗
42. Jiang P, Gao S, Geng S, Han C, Mi G (2020) Multi-physics multi-scale simulation of the solidification process in the molten pool during laser welding of aluminum alloys. Int J Heat Mass Trans 161:12031610.1016/j.ijheatmasstransfer.2020.120316 ↗
43. Zhao XF, Wimmer A, Zaeh MF (2023) Experimental and simulative investigation of welding sequences on thermally induced distortions in wire arc additive manufacturing. Rapid Prototyp J 29(11):53–6310.1108/RPJ-07-2022-0244 ↗
44. Wang F, Fathizadan S, Ju F, Rowe K, Hofmann N (2020) Print surface thermal modeling and layer time control for large-scale additive manufacturing. IEEE Trans Autom Sci Eng 18(1):244–25410.1109/TASE.2020.3001047 ↗
45. Zhai W, Wu N, Zhou W (2022) Effect of interpass temperature on wire arc additive manufacturing using high-strength metal-cored wire. Metals 12(2):21210.3390/met12020212 ↗
46. Lagouanelle P, Freschi F, Pichon L (2023) Adaptive sampling for fast and accurate metamodel-based sensitivity analysis of complex electromagnetic problems. IEEE Trans Electromagn Compat10.1109/TEMC.2023.3320285 ↗
47. Gálvez A, Iglesias A (2013) From nonlinear optimization to convex optimization through firefly algorithm and indirect approach with applications to CAD/CAM. Sci World J 201310.1155/2013/283919 ↗
48. Svanberg K (2002) A class of globally convergent optimization methods based on conservative convex separable approximations. SIAM J Optim 12:555–57310.1137/S1052623499362822 ↗
49. Montemurro M (2022) On the structural stiffness maximisation of anisotropic continua under inhomogeneous neumann-dirichlet boundary conditions. Compos Struct 287:11528910.1016/j.compstruct.2022.115289 ↗
50. Turner CJ, Crawford RH (2009) N-Dimensional Nonuniform Rational B-Splines for Metamodeling. J Comput Inf Sci Eng 9(3)10.1115/1.3184599 ↗