A Critical Review on Tensile Behavior and Johnson-CookConstitutive Model Calibration of Wire Arc Additive Manufactured Materials

Abstract

Wire arc additive manufacturing (WAAM) has emerged as a key near-net-shape fabrication route for large-scale aerospace metallic components, offering deposition rates of 3-10 kg/h and material utilization exceeding 90%. However, the non-equilibrium microstructures formed under repeated WAAM thermal cycling — comprising coarse columnar grains, heterogeneous Laves phase, Widmanstatten lamellar alpha, and gradient in-situ aging precipitates — render the mechanical behavior of as-deposited materials fundamentally distinct from wrought counterparts. This disparity gives rise to a systematic 'parameter transplantation fallacy' in engineering simulations that rely on Johnson-Cook (JC) constitutive parameters calibrated from forged stock. The present review systematically examines 69 representative studies published between 2009 and 2026, covering three representative WAAM structural materials: Inconel 718, Ti-6Al-4V (TC4), and 18Ni300 maraging steel. A causal framework linking microstructure formation, tensile behavior, and JC calibration is established. Methodologically, this review proposes a new anisotropy classification taxonomy — texture-precipitate coupling type (IN718), grain boundary-texture type (TC4), and phase distribution-thermal history type (18Ni300) — and builds a cross-material three-tier error priority framework quantifying state-jump error, anisotropy error, and model calibration error. Key findings are: (i) classical JC models exhibit systematic prediction errors of 10-30% for WAAM materials at high strain rates (>1000 s-1) and elevated temperatures (>600 degrees C); (ii) for WAAM IN718, the state-jump error (~42%) far exceeds the anisotropy error (~14%) and model error (<2%), establishing as-deposited-specific parameter databases as the highest-priority research task; (iii) all five JC parameters remain entirely uncharacterized for WAAM as-deposited 18Ni300, representing the most critical knowledge gap in the field. A research roadmap with specific experimental matrices is proposed for near-, mid-, and long-term research directions.