Methodology for Analyzing Engineering Drawings Utilizing Artificial Intelligence (AI) to Enhance Design Quality
DOI:
https://doi.org/10.63282/3050-9416.ICAIDSCT26-127Keywords:
Engineering Drawing Analysis, AI-Enabled Quality Assurance, Computer Vision In CAD, Optical Character Recognition (OCR), Geometric Parsing, GD&T Compliance, Tolerance Stack-Up Validation, Rule-Based Expert Systems, Machine Learning Anomaly Detection, NLP For Engineering Documentation, Revision Tracking, Assembly-Level Consistency Checks, Drawing DigitizationAbstract
Engineering drawings remain the foundational communication medium for mechanical design, manufacturing, and quality assurance [1]. However, as products become more complex and design cycles accelerate, traditional manual drawing review processes struggle to keep pace [2]. Errors in tolerances, GD&T, material specifications, datum structures, and assembly relationships often propagate downstream, leading to rework, manufacturing delays, and quality escapes [1], [3]. This paper proposes a structured methodology for applying Artificial Intelligence (AI) to analyze engineering drawings, detect inconsistencies, and improve overall design quality [4]. Leveraging experience in mechanical systems design, plasma chamber engineering, and AI‑enabled simulation workflows, the methodology integrates computer vision [5], natural language processing [6], rule‑based expert systems [7], and machine learning driven anomaly detection [8] to create a scalable, automated drawing‑quality framework.
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