Optimization of bead morphology for GMAW-based wire-arc additive manufacturing of 2.25 Cr-1.0 Mo steel using metal-cored wires

Jay Vora, Nipun Parikh, Rakesh Chaudhari, Vivek K. Patel, Heet Paramar, Daniel Yurievich Pimenov, Khaled Giasin

Research output: Contribution to journalArticlepeer-review

3 Downloads (Pure)

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.
Original languageEnglish
Article number5060
Number of pages18
JournalApplied Sciences (Switzerland)
Volume12
Issue number10
DOIs
Publication statusPublished - 17 May 2022

Keywords

  • wire-arc additive manufacturing (WAAM)
  • low alloy steel
  • 2.25 Cr-1.0 Mo steel
  • metal-cored wire
  • optimization
  • teaching–learning-based optimization (TLBO)

Fingerprint

Dive into the research topics of 'Optimization of bead morphology for GMAW-based wire-arc additive manufacturing of 2.25 Cr-1.0 Mo steel using metal-cored wires'. Together they form a unique fingerprint.

Cite this