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In order to provide an accurate estimation of fatigue life of automotive door hinges and check strap mounting location, it is crucial to understand the loading conditions associated with opening and closing the door. There are many random factors and uncertainties that affect the durability performance of hinge and check strap mount structures in either a direct or indirect way. Excessive loads are generated at the hinge and check arm mounting region during abuse conditions when opening the door. Repeating the abuse conditions will lead to fatigue failures in these components. Most influencing parameter affecting the fatigue performance for the door was the loads due to hinge-check arm sensitivity stoppage and the distance between hinge and check strap attachments. However, the probability of occurrences was low, but the impact is high.

Significance of CAE simulation thus is increasing because of its ability to predict the failure faster, also lot of design combinations can be evaluated with this before physical testing. Frame stiffness of side doors is one of the major criteria of a vehicle closure system. In most cases, designers around the globe will be designing same or very similar side door frame structures recurrently. In addition, in the current growing trend having an optimized side door frame design in quick time is very challenging. In this investigation, a new artificial intelligence (AI) approach was demonstrated to design and optimize frame reinforcement based on machine learning, which has been successful in many fields owing to its ability to process big data, can be used in structural design and optimization. This deep learning-based model is able to achieve accurate predictions of nonlinear structure-parameters relationships using deep neural networks.

In side-closures’ design, mass reduction provides numerous benefits in addition to reduced cost. This paper presents a Meta model based non-linear durability optimization to develop a lightweight structure for vehicle swing door. A surrogate model developed is using Kriging methodology and the thickness of the door components are given as input design variables. Adaptive Multi-Objective Genetic Algorithm (AMGA), a nonlinear optimization technique, is used in this study, to formulate the mass minimization under durability constraints. The optimized swing door design shows the overall mass saving of ~10% over initial design in terms of frame and sag deflection. The present investigation shows better effectiveness and practical applicability to develop the lightweight structure for the vehicle swing door.