Wire and arc-based direct energy deposition (wire and arc DED) is a promising metallic additively manufacturing (MAM) technology to fabricate large-size metallic components. Among high-strength-low-alloy steels used in wire and arc DED processes, ER110S-G is a commonly used material. However, previous studies have often relied on process parameters recommended by suppliers for conventional welding methods. This study aims to predict the optimum processing parameters for the manufacture of ER110S-G steel by a wire and arc DED process based on experiment and multi-attribute optimization algorithms. The experiment was designed using the full factorial method with the input variables (U– voltage, I– current, and v– traveling speed) while considering three attributes of single tracks (STW– single track width, STH– single track height, and LMP– length of melting pool). To find the optimal processing variables, MONS-MFO (Multi-Objective Non-Sorted Moth Flame) was utilized. The outcomes reveal that the developed predictive models of attributes feature a high accurate level (R2 = 98.11%, 98.38%, and 98.07% for STW, STH, and LMP, respectively). The optimal parameters obtained by MONS-MFO & TOPSIS were I = 159.4 A, v = 0.3 m/min, and U = 21.4 V allow fabricating single tracks with smooth and regular shape and a s-shaped thin-wall component with regular shape and height. Moreover, no spatters generated with the optimal parameters, demonstrating their suitability and efficiency for the printing process.
AhnD-G. Directed energy deposition (DED) process: state of the art. Int J Precis Eng Manuf Green Technol2021; 8: 703–742.
2.
JafariDVanekerTHJGibsonI. Wire and arc additive manufacturing: opportunities and challenges to control the quality and accuracy of manufactured parts. Mater Des2021; 202: 109471.
3.
KazanasPDeherkarPAlmeidaP, et al. Fabrication of geometrical features using wire and arc additive manufacture. Proc IMechE, Part B: J Engineering Manufacture2012; 226: 1042–1051.
4.
WilliamsSWMartinaFAddisonAC, et al. Wire + arc additive manufacturing. Mater Sci Technol2016; 32(7): 641–647.
5.
CunninghamCRFlynnJMShokraniA, et al. Invited review article: strategies and processes for high quality wire arc additive manufacturing. Addit Manuf2018; 22: 758–774.
6.
SrivastavaMRatheeSTiwariA, et al. Wire arc additive manufacturing of metals: a review on processes, materials and their behaviour. Mater Chem Phys2023; 294: 126988.
7.
AnTBWeiJSZhaoL, et al. Influence of carbon content on microstructure and mechanical properties of 1000 MPa deposited metal by gas metal arc welding. J Iron Steel Res Int2019; 26: 512–518.
8.
ElmerJWGibbsG. The effect of atmosphere on the composition of wire arc additive manufactured metal components. Sci Technol Weld Joining2019; 24: 367–374.
9.
da SilvaLJSouzaDMde AraújoDB, et al. Concept and validation of an active cooling technique to mitigate heat accumulation in WAAM. Int J Adv Manuf Technol2020; 107: 2513–2523.
10.
CostelloSCACunninghamCRXuF, et al. The state-of-the-art of wire arc directed energy deposition (WA-DED) as an additive manufacturing process for large metallic component manufacture. Int J Comput Integr Manuf2023; 36: 469–510.
11.
HuangGWanXLWuKM, et al. Effect of Cu addition on microstructure and impact toughness in the simulated coarse-grained heat-affected zone of high-strength low-alloy steels. Mater Sci Technol2017; 33: 602–614.
12.
PlanggerJSchabhüttlPVuhererT, et al. CMT additive manufacturing of a high strength steel alloy for application in crane construction. Metals2019; 9: 650.
13.
MoynihanMCAllwoodJM. Utilization of structural steel in buildings. Proc R Soc A Math Phys Eng Sci2014; 470: 20140170.
14.
RodriguesTADuarteVAvilaJA, et al. Wire and arc additive manufacturing of HSLA steel: Effect of thermal cycles on microstructure and mechanical properties. Addit Manuf2019; 27: 440–450.
15.
DinovitzerMChenXLaliberteJ, et al. Effect of wire and arc additive manufacturing (WAAM) process parameters on bead geometry and microstructure. Addit Manuf2019; 26: 138–146.
16.
DingDPanZCuiuriD, et al. A practical path planning methodology for wire and arc additive manufacturing of thin-walled structures. Robot Comput Integr Manuf2015; 34: 8–19.
17.
ZhaoYJiaYChenS, et al. Process planning strategy for wire-arc additive manufacturing: thermal behavior considerations. Addit Manuf2020; 32: 100935.
18.
FuchsCSemmTZaehMF. Decision-based process planning for wire and arc additively manufactured and machined parts. J Manuf Syst2021; 59: 180–189.
19.
DingDPanZCuiuriD, et al. Adaptive path planning for wire-feed additive manufacturing using medial axis transformation. J Clean Prod2016; 133: 942–952.
20.
DingDPanZCuiuriD, et al. Bead modelling and implementation of adaptive MAT path in wire and arc additive manufacturing. Robot Comput Integr Manuf2016; 39: 32–42.
21.
HuZQinXLiY, et al. Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing. J Intell Manuf2020; 31: 1133–1147.
22.
SuryakumarSKarunakaranKPBernardA, et al. Weld bead modeling and process optimization in hybrid layered manufacturing. Comput Aided Des2011; 43: 331–344.
23.
DingDPanZCuiuriD, et al. A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM). Robot Comput Integr Manuf2015; 31: 101–110.
24.
ZhaoTLiuHLiL, et al. An automatic compensation method for improving forming precision of multi-layer multi-bead component. Proc IMechE, Part B: J Engineering Manufacture2021; 235: 1284–1297.
25.
XiongJZhangGHuJ, et al. Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J Intell Manuf2014; 25: 157–163.
26.
WangZZimmer-ChevretSLéonardF, et al. Prediction of bead geometry with consideration of interlayer temperature effect for CMT-based wire-arc additive manufacturing. Weld World2021; 65: 2255–2266.
27.
GengHXiongJHuangD, et al. A prediction model of layer geometrical size in wire and arc additive manufacture using response surface methodology. Int J Adv Manuf Technol2017; 93: 175–186.
28.
YouhengFGuilanWHaiouZ, et al. Optimization of surface appearance for wire and arc additive manufacturing of bainite steel. Int J Adv Manuf Technol2017; 91: 301–313.
29.
SarathchandraDTDavidsonMJVisvanathanG. Parameters effect on SS304 beads deposited by wire arc additive manufacturing. Mater Manuf Process2020; 35: 852–858.
30.
Prasanna NagasaiBMalarvizhiSBalasubramanianV. A study on wire arc additive manufacturing of 308L austenitic stainless steel cylindrical components: optimisation, microstructure and mechanical properties. Proc IMechE, Part B: J Engineering Manufacture2023; 237: 1391–1404.
31.
VeigaFSuarezAAldalurE, et al. Wire arc additive manufacturing of invar parts: bead geometry and melt pool monitoring. Measurement2022; 189: 110452.
Venkata RaoKParimiSSuvarna RajuL, et al. Modelling and optimization of weld bead geometry in robotic gas metal arc-based additive manufacturing using machine learning, finite-element modelling and graph theory and matrix approach. Soft Comput2022; 26: 3385–3399.
34.
SavsaniVTawhidMA. Non-dominated sorting moth flame optimization (NS-MFO) for multi-objective problems. Eng Appl Artif Intell2017; 63: 20–32.
35.
MirjaliliS. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst2015; 89: 228–249.
36.
DebKPratapAAgarwalS, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput2002; 6: 182–197.
37.
JangirP. Non-dominated sorting moth flame optimizer: a novel multi-objective optimization algorithm for solving engineering design problems. Eng Technol Open Access J2018; 2: 555579. DOI: 10.19080/etoaj.2018.02.555579
38.
LeVTBuiMCNguyenTD, et al. On the connection of the heat input to the forming quality in wire-and-arc additive manufacturing of stainless steels. Vacuum2023; 209: 111807.
39.
LeVTMaiDSDoanTK, et al. Wire and arc additive manufacturing of 308L stainless steel components: optimization of processing parameters and material properties. Eng Sci Technol Int J2021; 24: 1015–1026.
40.
NageshDSDattaGL. Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks. J Mater Process Technol2002; 123: 303–312.
41.
JindalSChhibberRMehtaNP. Effect of welding parameters on bead profile, microhardness and H2 content in submerged arc welding of high-strength low-alloy steel. Proc IMechE, Part B: J Engineering Manufacture2014; 228: 82–94.
42.
LeVTMaiDSHoangQH. A study on wire and arc additive manufacturing of low-carbon steel components: process stability, microstructural and mechanical properties. J Braz Soc Mech Sci Eng2020; 42: 480.
