An Analysis of Human Preference for Object Orientation in Mechanical Design Visualization

Abstract

Effective communication of new mechanical designs is critical during the conceptual design stage of product development and has a significant impact on how designers present their work. While 2D technical drawings are widely used to convey the 3D geometry of designs, critical factors of design visualization, such as view selection, including object orientation, rotation, and direction of view, are often overlooked. To enhance communication efficiency and reduce errors, it is essential to establish optimal criteria for presenting mechanical designs. This study explores human preferences for orienting mechanical components, focusing on two common approaches: functionality-based and stability-based orientations. To examine these preferences, two groups of participants with diverse academic backgrounds were tasked with selecting between these two orientations for a set of mechanical designs. Additionally, participants rated their familiarity with each object (on a scale from 0 to 3) to assess how familiarity influences orientation preferences. The findings aim to provide insights into the optimal presentation of mechanical designs, enhancing clarity and effectiveness in design communication. The experimental results reveal that participants with a higher level of familiarity with the design tend to prefer functionality-based orientations, while stability-based orientations are more frequently selected for less familiar designs. Additionally, academic background primarily influences participants’ familiarity with the designs but has minimal impact on their orientation preferences when familiarity is considered. These results provide valuable insights for optimizing the presentation of mechanical designs, enhancing clarity and communication effectiveness in design visualization. By understanding these preference patterns, this study offers practical implications for improving mechanical design communication. Tailoring visualization strategies based on familiarity levels can improve clarity and ensure that design intent is effectively communicated to different audiences. These findings provide a foundation for future research to optimize view selection in mechanical design visualization.