Evaluation of a Design Support Tool Incorporating Sensory Performance Model of Ride Comfort for Conceptual Design of Controlled Suspensions

Abstract

<div class="section abstract"><div class="htmlview paragraph">The objective of this study is to introduce and assess a computational tool designed to facilitate product development via sensory scores, which serve as a quantifiable representation of human sensory experiences. In the context of designing ride comfort performance, the specialized terminology—either technical or sensory—often served as a barrier to comprehension among the diverse set of specialists constituting the multidisciplinary team. In a previous study by the authors introduced a tool that incorporated a model of sensory performance, utilizing sensory scores as universally comprehensible metrics. However, the tool had yet to be appraised by a genuine cross-functional team. In this study, the tool underwent evaluation through a user-testing process involving twenty-five cross-functional team members engaged in the conceptual design phase at an automotive manufacturing company. Five different suspension systems were examined, including a wheel rotational speed-driven damper system developed by the authors. To evaluate the best performance of each suspension systems, sensory scores served as the objective function. Design parameters were obtained by an improved particle swarm optimization 3</div><div class="htmlview paragraph">PSO) algorithm. The assessment procedure included participants experiencing simulated vibrations through a ride simulator. This was followed by the participants' subjective evaluations of the vibrations and structured interviews aimed at ascertaining the tool's advantages. The results revealed that 92% of participants responded the tool would be beneficial for their work. Moreover, a new sensory performance model was then constructed, incorporating principal components derived from combined vibration data and weights determined by the collected sensory scores for four distinct feature groups.</div></div>