On the effects of the fix geometric constraint in 2D profiles on the reusability of parametric 3D CAD models

[EN] In order to be reusable, history-based feature-based parametric CAD models must reliably allow for modifications while maintaining their original design intent. In this paper, we demonstrate that relations that fix the location of geometric entities relative to the reference system produce inflexible profiles that reduce model reusability. We present the results of an experiment where novice students and expert CAD users performed a series of modifications in different versions of the same 2D profile, each defined with an increasingly higher number of fix geometric constraints. Results show that the amount of fix constraints in a 2D profile correlates with the time required to complete reusability tasks, i.e., the higher the number of fix constraints in a 2D profile, the less flexible and adaptable the profile becomes to changes. In addition, a pilot software tool to automatically track this type of constraints was developed and tested. Results suggest that the detection of fix constraint overuse may result in a new metric to assess poor quality models with low reusability. The tool provides immediate feedback for preventing high semantic level quality errors, and assistance to CAD users. Finally, suggestions are introduced on how to convert fix constraints in 2D profiles into a negative metric of 3D model quality.The authors would like to thank Raquel Plumed for her support in the statistical analysis. This work has been partially funded by Grant UJI-A02017-15 (Universitat Jaume I) and DPI201784526-R (MINECO/AEI/FEDER, UE), project CAL-MBE. The authors also wish to thank the editor and reviewers for their valuable comments and suggestions that helped us improve the quality of the paper.González-Lluch, C.; Company, P.; Contero, M.; Pérez Lopez, DC.; Camba, JD. (2019). On the effects of the fix geometric constraint in 2D profiles on the reusability of parametric 3D CAD models. 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Are we training our novices towards quality 2D profiles for 3D models?

In the history-based, feature-based, parametric CAD approach, 2D profile sketches are the basis for 3D models. Fully-constraining profiles is mandatory to create robust profiles. At present, neither CAD applications nor Model Quality Testing Tools usually check whether 2D profiles contain redundant constraints. Besides, our experience shows that novices tend to introduce redundant constraints. We hypothesize that 2D profiles over-constrained with redundant relations are more difficult to edit than those that avoid redundancies. In the present work―and as a first step to demonstrate this hypothesis―an experiment was conducted. Students of the subject “Graphics engineering” were taught on the creation of constrained 2D profiles. Then, they were asked two questions. On the one hand, novices had to identify and reason whether a simple given profile was fully-constrained, over-constrained or under-constrained. On the other hand, they had to identify and point out the types of the constraints. The results showed that in spite that novices received a specific training, roughly half of them failed to say if the 2D profile sketch was fully-constrained and which type of constraints it contained. Furthermore, the results of the second question revealed that more than the half of students did not recognize perpendicularity as a geometric constraint. As future work, we will try to demonstrate whether a reinforced training through simple exercises and a quick and effective feedback, will allow novices to improve the identification and removal of redundant 2D constraints when drawing 2D profile sketches (thus helping to produce robust profiles)

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Advances in technology and an increase in the amount and complexity of data that are generated in healthcare have led to an indispensable revolution in this sector related to big data. Analytics of information based on multimodal clinical data sources requires big data projects. When starting big data projects in the healthcare sector, it is often necessary to assess the maturity of an organization with respect to big data, i.e., its capacity in managing big data. The assessment of the maturity of an organization requires multicriteria decision making as there is no single criterion or dimension that defines the maturity level regarding big data but an entire set of them. Based on the ISO 15504, this article proposes a fuzzy ELECTRE structure methodology to assess the maturity level of small- and medium-sized enterprises in the healthcare sector. The obtained experimental results provide evidence that this methodology helps to determine and compare maturity levels in big data management of organizations or the evolution of maturity over time. This is also useful in terms of diagnosing the readiness of an organization before starting to implement big data initiatives or technologies. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature