Oil canning, characterized by localized buckling and surface waviness in thin metal panels, is a critical quality parameter in the automotive industry. Since outer panels serve both structural and aesthetic functions, even minor surface distortions can adversely affect perceived vehicle quality. Traditionally, evaluation of oil canning relies on extensive nonlinear Finite Element simulations. For example, assessing a single hood design variant may requires thousands of nonlinear Finite Element analyses. Considering multiple design iterations across major vehicle panels including hood, roof, doors, fenders, and trunk, the total number of simulations can exceed tens of thousands, leading to substantial computational cost and development time. Consequently, this conventional approach is both time intensive and resource demanding.
To address these limitations, advanced Machine Learning techniques are employed to significantly reduce simulation effort as well as accelerate design exploration. Initially, a deep learning based one-dimensional Reduced Order Model is developed to predict load–displacement response and oil canning propensity for a given panel. Indentation points on the hood panel are selected using a space filling Latin Hypercube sampling method. The Machine Learning based one-dimensional Reduced Order Model is trained by using 300-500 indentation points and validated using approximately 40-50 unseen indentation points. The verification is performed on multiple hood geometries. The predicted results demonstrate strong correlation with Finite Element generated load–displacement curves with that of ML based 1-dimensional reduced order model.
Furthermore, to enable rapid design exploration across different panel geometries, a dataset of 40 different hood designs is utilized. A geometric deep learning approach is applied to cluster panel geometries, and the resulting representations are used within a 3-dimensional deep neural network tool Ansys SimAI™ to predict oil canning propensity and quantify it using the canning ratio. Validation study is carried out on unseen panel geometries, and the Machine Learning predicted oil canning zones show good agreement with Finite Element identified critical regions. The proposed Machine Learning framework reduces prediction time from hours to minutes, offering a scalable and cost-effective solution for early-stage design evaluation and mitigation of oil canning in automotive panels.