Optimization of Bead Morphology for GMAW-Based Wire-Arc Additive Manufacturing of 2.25 Cr-1.0 Mo Steel Using Metal-Cored Wires
Source abstract
Abstract
The fabrication of components involves the deposition of multiple beads in multiple layers for wire-arc additive manufacturing (WAAM). WAAM performed using gas metal arc welding (GMAW) allows for the manufacturing of parts through multiple-bead multi-layer deposition, which depends on the process variables. Thus, the selection of process parameters along with their required levels is mandatory to deposit multiple layers for WAAM. To obtain the desired levels of parameters, bead-on-plate trials were taken on the base plate of low alloy steel by following an experimental matrix produced through the Box–Behnken design (BBD) on GMAW-based WAAM. Wire feed speed, travel speed, and voltage were chosen as the input parameters and bead width and bead height were chosen as the output parameters. Furthermore, the robustness and adequacy of the obtained regression equations were analyzed by using analysis of variance (ANOVA). For both responses of BW and BH, values of R2 and adj. R2 were found to be near unity, which has shown the fitness of the model. Teaching–learning-based optimization (TLBO) technique was then employed for optimization. Within the selected range of process variables, the single-objective optimization result showed a maximum bead height (BH) of 7.81 mm, and a minimum bead width (BW) of 4.73 mm. To tackle the contradicting nature of responses, Pareto fronts were also generated, which provides a unique non-dominated solution. Validation trials were also conducted to reveal the ability and suitability of the TLBO algorithm. The discrepancy between the anticipated and measured values was observed to be negligible, with a deviation of less than 5% for all the validation trials. This demonstrates the success of the established model and TLBO algorithm. The optimum feasible settings for multi-layer metal deposition were determined after further tuning. A multi-layer structure free from any disbonding was successfully manufactured at the optimized variables. The authors suggest that the optimum parametric settings would be beneficial for the deposition of layer-by-layer weld beads for additive manufacturing of components.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Raisan, Gandhinagar 382007, India
Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, 454080 Chelyabinsk, Russia
School of Mechanical and Design Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK
References (37)
Show source-provided references
1. Review on effect of heat input for wire arc additive manufacturing process10.1016/j.jmrt.2021.02.002 ↗
2. Selection of process parameters for near-net shape deposition in wire arc additive manufacturing by genetic algorithm10.1007/s11665-020-04847-1 ↗
3. Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm10.1007/s00170-021-06846-5 ↗
4. Lee, S.H. (2020). Optimization of cold metal transfer-based wire arc additive manufacturing processes using gaussian process regression. Metals, 10.10.3390/met10040461 ↗
5. Review of wire arc additive manufacturing for 3D metal printing10.20965/ijat.2019.p0346 ↗
6. A comparative study of additive manufacturing techniques: Residual stress and microstructural analysis of CLAD and WAAM printed Ti–6Al–4V components10.1016/j.matdes.2015.09.115 ↗
7. Study on arc welding processes for high deposition rate additive manufacturing10.1016/j.procir.2017.12.095 ↗
8. Wire arc additive manufacturing of Al-6Mg alloy using variable polarity cold metal transfer arc as power source10.1016/j.msea.2017.11.084 ↗
9. Metal transfer modes in plasma Wire+ Arc additive manufacture10.1016/j.jmatprotec.2018.08.043 ↗
10. Designing a WAAM based manufacturing system for defence applications10.1016/j.procir.2015.08.085 ↗
11. Design for Wire+ Arc Additive Manufacture: Design rules and build orientation selection10.1080/09544828.2017.1365826 ↗
12. Application of multidirectional robotic wire arc additive manufacturing process for the fabrication of complex metallic parts10.1109/TII.2019.2935233 ↗
13. Process parameter optimization of gas metal arc welding on IS: 2062 mild steel using response surface methodology10.1016/j.jmapro.2016.12.016 ↗
14. Wire and arc additive manufactured steel: Tensile and wear properties
15. Experimental Investigation on welding of 2.25 Cr-1.0 Mo steel with Regulated Metal Deposition and GMAW technique incorporating metal-cored wires10.1016/j.jmrt.2021.08.081 ↗
16. Das, S., Vora, J., Patel, V., Anderrson, J., Pimenov, D.Y., and Giasin, K. (2021). Elucidating the Effect of Step Cooling Heat Treatment on the Properties of 2.25 Cr–1.0 Mo Steel Welded with a Combination of GMAW Techniques Incorporating Metal-Cored Wires. Materials, 14.10.3390/ma14206033 ↗
17. Temper embrittlement in 2-1/4 Cr-1 Mo steels after 75,000-hour isothermal aging10.1115/1.2903414 ↗
18. Prediction of angular distortion due GMAW process of thin-sheets Hardox 450® steel by numerical model and artificial neural network10.1016/j.jmapro.2021.06.045 ↗
19. Yang, K., Wang, F., Duan, D., Zhang, T., Luo, C., Cressault, Y., Yu, Z., Yang, L., and Li, H. (2021). Experimental Investigation of Integrated Circular Triple-Wire Pulse GMAW of Q960E High-Strength Steel for Construction Machinery. Materials, 14.10.3390/ma14020375 ↗
20. Zhang, X.-Y., Zha, X.-Q., Gao, L.-Q., Hei, P.-H., and Ren, Y.-F. (2021). Influence of Shielding Gas on Microstructure and Properties of GMAW DSS2205 Welded Joints. Materials, 14.10.3390/ma14102671 ↗
21. Mičian, M., Winczek, J., Gucwa, M., Koňár, R., Málek, M., and Postawa, P. (2020). Investigation of Welds and Heat Affected Zones in Weld Surfacing Steel Plates Taking into Account the Bead Sequence. Materials, 13.10.3390/ma13245666 ↗
22. Hybridization of filler wire in multi-pass gas metal arc welding of SA516 Gr70 carbon steel10.1080/10426914.2016.1244847 ↗
23. Czupryński, A. (2020). Comparison of properties of hardfaced layers made by a metal-core-covered tubular electrode with a special chemical composition. Materials, 13.10.3390/ma13235445 ↗
24. Welding parameters prediction for arbitrary layer height in robotic wire and arc additive manufacturing10.1007/s12206-020-0331-0 ↗
25. Correlation between arc mode, microstructure, and mechanical properties during wire arc additive manufacturing of 316L stainless steel10.1016/j.msea.2019.02.078 ↗
26. Wahsh, L., ElShater, A., Mansour, A., Hamdy, F., Turky, M., Azzam, M., and Salem, H. (2018). Parameter selection for wire arc additive manufacturing (WAAM) process. Mater. Sci. Technol., 78–85.10.7449/2018mst/2018/mst_2018_78_85 ↗
27. Parametric study and characterization of wire arc additive manufactured steel structures10.1007/s00170-021-07261-6 ↗
28. A multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO)10.1016/j.ins.2014.05.049 ↗
29. Vora, J., Chaudhari, R., Patel, C., Pimenov, D.Y., Patel, V.K., Giasin, K., and Sharma, S. (2021). Experimental Investigations and Pareto Optimization of Fiber Laser Cutting Process of Ti6Al4V. Metals, 11.10.3390/met11091461 ↗
30. Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm10.1007/s40436-019-00267-0 ↗
31. Vora, J., Patel, V.K., Srinivasan, S., Chaudhari, R., Pimenov, D.Y., Giasin, K., and Sharma, S. (2021). Optimization of activated tungsten inert gas welding process parameters using heat transfer search algorithm: With experimental validation using case studies. Metals, 11.10.3390/met11060981 ↗
32. Pan, Z., Ding, D., Wu, B., Cuiuri, D., Li, H., and Norrish, J. (2018). Arc welding processes for additive manufacturing: A review. Transactions on Intelligent Welding Manufacturing, Springer.10.1007/978-981-10-5355-9_1 ↗
33. A review of the wire arc additive manufacturing of metals: Properties, defects and quality improvement10.1016/j.jmapro.2018.08.001 ↗
34. A review on effects of GTAW process parameters on weld
35. Effect of process parameters on bead geometry and shape relationship of gas metal arc weldments
36. Prediction and optimization of weld bead geometry in oscillating arc narrow gap all-position GMA welding10.1007/s00170-015-6818-7 ↗
37. Fuse, K., Chaudhari, R., Vora, J., Patel, V.K., and de Lacalle, L.N.L. (2021). Multi-Response Optimization of Abrasive Waterjet Machining of Ti6Al4V Using Integrated Approach of Utilized Heat Transfer Search Algorithm and RSM. Materials, 14.10.3390/ma14247746 ↗