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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.