A strategy to classify and resolve ambiguities when reverse engineering drawings to CAD models

Oftentimes, designers need to reverse engineer detailed design drawings to create 3D models. These drawings are commonly accepted as complete information sources in design datasets but may contain errors or ambiguities and lead to misinterpretation, resulting in digital CAD models that inherit such ambiguities, or modelling situations with no solution. We propose a classification of errors based on the type of ambiguity they produce (redundancy, omission, contradiction, and polysemy) and discuss the results of an experiment designed to evaluate the effectiveness of a strategy based on this classification. The classification represents an essential step for developing a checklist to ensure drawing ambiguities are fully resolved before the corresponding 3D models are built. In the long term, the proposed classification can guide a divide-and-conquer strategy for training artificial intelligence systems in the detection and repair of these ambiguities.