<div class="section abstract"><div class="htmlview paragraph">Cargo box is one of the indispensable structures of a pickup truck which makes it capable of transporting heavy cargo weights. This heavy cargo weight plays an important role in durability performance of the box structure when subjected to road load inputs. Finite element representation for huge cargo weight is always challenging, especially in a linear model under dynamic proving ground road load durability analysis using a superposition approach. Any gap in virtual modeling technique can lead to absurd cargo box modes and hence durability results. With the existing computer aided engineering (CAE) approach, durability results could not correlate much with physical testing results. It was crucial to have the right and robust CAE modeling technique to represent the heavy cargo weight to provide the right torsional and cargo modes of the box structure and in turn good durability results.</div><div class="htmlview paragraph">As there are multiple variables owing to heavy cargo weight, Design for Six Sigma (DFSS) methodology was used to develop a robust CAE modeling technique. This project was defined with multiple key control factors considering geometrical and material properties of cargo along with the multiple ways to connect cargo ballast with the box floor bed. The manufacturing variability of box panels was considered as a noise factor. L18 Orthogonal Array matrix was chosen to optimize the variables based on factorial effects of signal to noise (S/N) ratio. Finally, the validation was done for the achieved optimum solutions using numerical tests.</div><div class="htmlview paragraph">This paper describes the DFSS project in detail, and emphasizes on how the DFSS approach could be very effective to predict the influential control factors and how the combination of these factors was utilized to find out the optimum solution. This paper also highlights how the optimized/proposed CAE modeling technique improves the accuracy of the cargo box modal and durability results.</div></div>
