TY - EJOU AU - Zhang, Jianwei AU - Shen, Ran AU - Zhang, Qianqi AU - Li, Yuanxin AU - Chen, Shengchao AU - Zhao, Minghao AU - Shi, Lubing AU - Wang, Bing TI - Characterization of Non-Equibiaxial Residual Stresses via Machine Learning Enhanced Instrumented Indentation Testing T2 - Computers, Materials \& Continua PY - VL - IS - SN - 1546-2226 AB - Non-equibiaxial residual stresses are prevalent in engineering components such as welding, additive manufacturing, and surface strengthening, making their accurate detection critical for ensuring structural integrity. This paper proposes a novel method capable of simultaneously identifying two principal stress components (

σ_{x}^{R}

σ_{z}^{R}

) using only an individual instrumented indentation. First, the normalized total indentation work variation W_norm and the residual indentation ellipticity λ are extracted as sensitive features from the indentation responses through dimensional analysis. Subsequently, a finite element (FE) simulation database comprising 2400 datasets was established to train three types of neural networks: the single-target approach (ST-MLP), the classical multi-output multi-layer perceptron (MLP), and the parameter sharing-based deep network (DMTR). The outcomes of the training phase indicate that the overall error of the DMTR model is less than 5%, while those of the MLP and ST-MLP models are less than 7%. Furthermore, supplementary FE simulations were conducted on Al 7075, Al 2024, and Ti Grade 5 alloys for validation, showing that the MLP and DMTR prediction errors across all models are controlled within 11%. Finally, experimental validation was conducted on Al 7075 alloy and 18CrNiMo7-6 alloy cruciform specimens. The result is the DMTR model maintains a prediction error within 30 MPa. This study provides a feasible path for the rapid on-site detection of non-equibiaxial residual stress. KW - Non-equibiaxial residual stress; spherical indentation; machine learning DO - 10.32604/cmc.2026.084790