Object shape error modelling and simulation during early design stage by morphing Gaussian Random Fields

TL;DR

This paper introduces a novel Morphing Gaussian Random Field (MGRF) methodology for early-stage object shape error modeling, enabling high-fidelity, versatile, and designer-centric simulation of part defects to improve product quality prediction.

Contribution

The paper presents the MGRF methodology, which enhances defect fidelity, data versatility, and designer usability in shape error modeling during early design stages.

Findings

MGRF accurately models local and global shape defects.

MGRF effectively utilizes limited historical defect data.

MGRF supports 'what if' analysis for design decision-making.

Abstract

Geometric and dimensional variations in objects are caused by inevitable uncertainties in manufacturing processes and often lead to product quality issues. Failing to model the effect object shape errors, i.e., geometric and dimensional errors of parts, early during design phase inhibits the ability to predict such quality issues; consequently leading to expensive design changes after freezing of design. State-of-Art methodologies for modelling and simulating object shape error have limited defect fidelity, data versatility, and designer centricity that prevent their effective application during early design phase. Overcoming these limitations a novel Morphing Gaussian Random Field (MGRF) methodology for object shape error modelling and simulation is presented in this paper. The MGRF methodology has (i) high defect fidelity and is capable of simulating various part defects including local and global deformations, and technological patterns; (ii) high data versatility and can effectively simulate non-ideal parts under the constraint of limited data availability and can utilise historical non-ideal part data; (iii) designer centric capabilities such as performing `what if?' analysis of practically relevant defects; and (iv) capability to generate non-ideal parts conforming to statistical form tolerance specification. The aforementioned capabilities enable MGRF methodology to accurately model and simulate the effect of object shape variations on product quality during the early design phase. This is achieved by first, modelling the spatial correlation in the deviations of the part from its design nominal using Gaussian Random Field and then, utilising the modelled spatial correlations to generate non-ideal parts by conditional simulations. Practical applications of developed MGRF methodology and its advantages are demonstrated using sport-utility-vehicle door parts.