Edge-focused wire arc additive manufacturing: Method development with ANN-based stress-strain and mass-efficiency.

Area of Science:

• Materials Science and Engineering
• Additive Manufacturing
• Mechanical Engineering

Background:

• Conventional Wire Arc Additive Manufacturing (WAAM) often faces challenges with residual stress and limited heat input control.
• Optimizing WAAM processes is crucial for improving material properties and structural integrity in additively manufactured components.

Purpose of the Study:

• To evaluate the performance of Edge-Focused Wire Arc Additive Manufacturing (EF-WAAM) for CT38 steel.
• To assess the impact of EF-WAAM's localized heat input on mechanical properties and residual stress.
• To develop a transferable workflow and predictive models for EF-WAAM applications.

Main Methods:

• Utilized EF-WAAM with ER70S-6 filler on CT38 steel, employing an edge-guided toolpath and controlled travel angle.
• Conducted standardized 3-point bending tests to determine maximum flexural stress and strength-to-density ratios.
• Developed an artificial neural network (ANN) to map process variables to stress-strain behavior and reconstruct material response curves.

Main Results:

• EF-WAAM builds achieved a maximum flexural stress up to 3,338.11 MPa, a 171% improvement over the CT38 substrate.
• Mass efficiency (strength-to-density) reached 420.4 MPa·cm³·g⁻¹, outperforming the substrate by 40.4% and conventional WAAM by 83.3%.
• ANN models accurately reconstructed stress-strain curves, with layer thickness identified as the dominant process variable.

Conclusions:

• EF-WAAM offers superior flexural strength and mass-specific performance compared to conventional WAAM and the substrate.
• The study provides a standardized evaluation pipeline, actionable parameter insights, and a predictive ANN tool for EF-WAAM.
• The developed methodology and assets facilitate the transfer of EF-WAAM technology to related additive manufacturing variants.