3D free-form surfaces with high aesthetical and functional requirements are widely used in automotive and aerospace industry. Geometric and dimensional variations of these free-form surfaces caused by inevitable uncertainties in the manufacturing process often leads to product quality issues. Failing to model the effect of non-ideal parts, i.e., parts with geometric and dimensional errors, during design inhibits the ability to predict such quality issues. A major challenge for accurate modelling of non-ideal parts during early design phase is the limited availability of data and the ability to effectively utilise the historical data from similar parts. Overcoming this challenge a novel morphing Gaussian Random Field (mGRF) methodology for shape error modelling of 3D free-form surfaces during the early design stage is presented in this paper. The mGRF methodology works under the constraint of limited data availability and can utilise historical measurement data of similar parts to generate non-ideal parts that exhibit spatial deviation patterns as similar as possible to the true manufactured part and conform to specified form tolerance requirements for the profile of a surface. Additionally, the mGRF methodology provides designers with an intuitive way to explore several `What if?' scenarios relating to part geometric variations during early design phase. This is achieved by first, modelling the spatial correlation in the deviations of the part from its design nominal using Gaussian processes and then, utilising the modelled spatial correlations to generate non-ideal parts by conditional simulations.The developed mGRF methodology is demonstrated, compared with state-of-art methodologies, and validated using a sport-utility-vehicle door part.

中文翻译：

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在早期设计阶段通过变形高斯随机场对 3D 自由曲面进行形状误差建模和仿真

具有高美学和功能要求的 3D 自由曲面被广泛应用于汽车和航空航天工业。由制造过程中不可避免的不确定性引起的这些自由曲面的几何和尺寸变化通常会导致产品质量问题。在设计期间未能对非理想零件（即具有几何和尺寸错误的零件）的影响进行建模会抑制预测此类质量问题的能力。在早期设计阶段对非理想零件进行准确建模的一个主要挑战是数据的有限可用性以及有效利用来自类似零件的历史数据的能力。为了克服这一挑战，本文提出了一种新颖的变形高斯随机场 (mGRF) 方法，用于在早期设计阶段对 3D 自由曲面进行形状误差建模。mGRF 方法在有限数据可用性的约束下工作，可以利用相似零件的历史测量数据生成非理想零件，这些零件的空间偏差模式尽可能与真实制造的零件相似，并符合轮廓的指定形状公差要求的一个表面。此外，mGRF 方法为设计师提供了一种直观的方式来探索多个“假设？” 在早期设计阶段与零件几何变化相关的场景。这是通过首先使用高斯过程对零件与其设计标称偏差的空间相关性进行建模，然后利用建模的空间相关性通过条件模拟生成非理想零件来实现的。 演示了开发的 mGRF 方法，