Abstract

Statistical model improvement consists of model calibration, validation, and refinement techniques. It aims to increase the accuracy of computational models. Although engineers in industrial fields are expanding the use of computational models in the process of product development, many field engineers still hesitate to perform statistical model improvement due to its practical aspects. Therefore, this paper describes research aimed at addressing three practical issues that hinder statistical model improvement in industrial fields: (1) lack of experimental data for quantifying uncertainties of true responses, (2) numerical input variables for propagating uncertainties of the computational model, and (3) model form uncertainties in the computational model. Issues 1 and 2 deal with difficulties in uncertainty quantification of experimental and computational responses. Issue 3 focuses on model form uncertainties, which are due to the excessive simplification of computational modeling; simplification is employed to reduce the calculation cost. Furthermore, the paper outlines solutions to address these three issues, specifically: (1) kernel density estimation with estimated bounded data, (2–1) variance-based variable screening, (2–2) surrogate modeling, and (3) a model refinement approach. By examining the computational model of an automobile steering column, these techniques are shown to demonstrate efficient statistical model improvement. This case study shows that the suggested approaches can actively reduce the burden in statistical model improvement and increase the accuracy of computational modeling, thereby encouraging its use in industry.