Abstract
Effective communication of mechanical designs through technical drawings requires geometric accuracy, efficiency, and an understanding of human perception and cognition. Although advances in computer-aided design (CAD) software have improved drawing precision and automation, current tools often overlook the spatial reasoning processes that users employ to interpret these representations. This study seeks to improve the accuracy and efficiency of human-computer interaction in CAD environments by examining how individuals perceive and interpret three-dimensional mechanical components within the context of technical drawings. A key focus is the identification of canonical and optimal views that align with intuitive human understanding. Through a series of four experimental studies, this dissertation breaks down the optimal view selection into four core dimensions (steps): (1) orientation, (2) rotation, (3) visibility of features, and (4) display/viewing format. The findings demonstrate that users consistently prefer views with upright vertical alignment, a high number of visible effective edges, and minimal reliance on unimaginable or occluded features. These preferences are grounded in psychological theories of spatial cognition, human factors research, and established engineering design standards. In contrast to AI-driven methods that rely solely on pattern recognition to infer optimal views, this study introduces a human-centered framework that can be integrated into intelligent CAD systems. By bridging engineering design with perceptual science, this research supports the development of smart CAD tools that enhance the accuracy, efficiency, and usability of technical communication in mechanical design.
Publication Date
11-17-2025
Document Type
- Please Select One -
Student Type
Graduate
Degree Name
Mechanical and Industrial Engineering (Ph.D)
Department, Program, or Center
Mechanical Engineering
College
Kate Gleason College of Engineering
Advisor
Rui Liu
Advisor/Committee Member
Alfonso Fuentes Aznar
Advisor/Committee Member
Denis Cormier
Recommended Citation
Chen, Yan-Ting, "Exploring Human Perception and Cognition in Expressing and Understanding Mechanical Designs" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12350
Campus
RIT – Main Campus
