A constraint redundancy elimination strategy to improve design reuse in parametric modeling

•Constraint redundancies in parametric models significantly hinder alterability and design reusability.•The tendency to introduce redundant constraints is prevalent even among expert design engineers.•A software tool was developed to optimize parametric models by eliminating constraint redundancy conditions in profiles.•The algorithm is capable of providing efficient design alternatives that are more reusable. Strategies for design reuse play a fundamental role in the development of products that change and evolve over time. Design changes often involve modifications in the geometry of parts and assemblies, which are driven by changes in the digital representation of the product, i.e. the procedural and parametric CAD model. Consequently, constraint redundancies in the two-dimensional profiles that build the parametric model can significantly hinder alterability and reusability. This paper argues that constraint redundancy conditions are not solely a computational problem but a more complex issue that involves the interaction with the sketch throughout the modeling process and the modeling scheme used to convey a specific design intent. We analyze a representative group of 3D MCAD systems and the interaction with their corresponding geometric kernels and Geometric Constraint Solvers (GCS) to determine how constraint redundancy is managed at the profile level. Next, we report the results of a series of experiments to evaluate the influence of redundant constraints on model editing and reusability tasks and describe the development of a new software tool for identifying and parsing constraint redundancy conditions. We conclude that constraint redundancy in profiles significantly and unnecessarily compromises model conciseness, robustness, and the overall model quality, which negatively affects user productivity and downstream processes. The implications of constraint redundancy conditions for CAD training are emphasized. Our experiments also demonstrate the value of our parsing tool to assist users in maximizing model reuse (by removing redundancies) and the communication of design intent (by proposing an optimal set of non-redundant constraints), but further development is necessary to use it as a practical tool for engineering analysis.