Affiliation:
1. TATA Motors Ltd, Digital Product Development Systems
2. TATA Motors Ltd
Abstract
<div class="section abstract"><div class="htmlview paragraph">Thin plates buckle after applying load and return to normal position after the
load is released, this process is called oil canning. Waviness in thin panels
can be seen on various plates of metals. Oil canning is a major issue if panels
are too thin and these panels create vibration and noise in the vehicle body
panel. If the panels are wider, then there are more chances of oil canning
issues. Different digital simulations and physical techniques are currently
available to check the canning performance, but they required geometrical data
and physical setup. In this paper machine learning (ML) approach to predict the
oil canning performance is presented. This approach adds a new process to the
existing process of vehicle door design, but it helps avoid the number of
simulations and unwanted structural modifications at the early design stage,
making it a handy and powerful tool for the designer.</div></div>