2026-10-08T10:29:35Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33779014 2026-09-15T08:44:12Z category_26254 category_26260 item_type_3 month_year_09_2026

Research data and code for "Reliability-based robust design of a nonlinear automotive assembly under manufacturing tolerances: a chance-constrained surrogate framework" Dongwoon Han (24918517) Dongwoon Han (20145893) Automotive safety engineering Automotive engineering not elsewhere classified Reliability-based design Chance-constrained optimization Manufacturing tolerance Gaussian process surrogate Uncertainty quantification Nonlinear assembly <p dir="ltr">This repository contains the dataset and Python code supporting the above study on reliability-based robust design of an automotive inner tie rod (ITR) caulking assembly.</p><p dir="ltr">The dataset (251202_rawdata_doe_fea_results_1.csv) comprises 81 high-fidelity finite-element analysis (FEA) cases from a three-level full-factorial design of experiments over four design factors (caulking stroke, housing lip length, machining volume, seating angle), together with the six resulting multi-physics responses (mass, caulking load, contact pressure, tilting torque, rotational torque, pull-out load).</p><p dir="ltr">The Jupyter notebooks reproduce the full analysis pipeline:</p><p dir="ltr">Surrogate modeling and validation: surrogate_comparison.ipynb (GPR vs. deep ensemble / RF / XGBoost / SVR), A_gpr_train_and_uq.ipynb (final GPR training and uncertainty calibration), gpr_parity_plot.ipynb, convergence_per_target.ipynb.</p><p dir="ltr">Interpretability: shap_beeswarm.ipynb, shap_interaction.ipynb, shap_interaction_heatmap.ipynb.</p><p dir="ltr">Optimization and diagnosis: optimizer_stats_v2.ipynb (BO/DE/PSO/SLSQP comparison with statistical tests), A_robustness_gpr.ipynb (local stability and weight sensitivity), diagnose_extrapolation.ipynb, SLSQP_only.ipynb.</p><p dir="ltr">Chance-constrained reformulation: chance_constrained_DE.ipynb, chance_constrained_slsqp.ipynb, chance_constrained_slsqp_CRN.ipynb.</p><p dir="ltr">Supplementary reliability analyses: reexp1_mc_violation.ipynb (Monte Carlo violation-probability estimation with confidence bounds), reexp2_largebudget.ipynb (budget-independence of the boundary collapse).</p><p dir="ltr">Requirements: Python 3.10, with numpy, pandas, scikit-learn, scipy, xgboost, shap, pyswarms, and bayesian-optimization. The trained GPR models and scalers are generated by running A_gpr_train_and_uq.ipynb first; the remaining notebooks load these artifacts.</p> 2026-09-15T08:44:12Z Dataset Dataset 10.6084/m9.figshare.33779014.v1 https://figshare.com/articles/dataset/Research_data_and_code_for_Reliability-based_robust_design_of_a_nonlinear_automotive_assembly_under_manufacturing_tolerances_a_chance-constrained_surrogate_framework_/33779014 CC BY 4.0