16 research outputs found

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

    Get PDF
    [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.GonzĂĄlez-Lluch, C.; Company, P.; Contero, M.; PĂ©rez Lopez, DC.; Camba, JD. (2019). On the effects of the fix geometric constraint in 2D profiles on the reusability of parametric 3D CAD models. International Journal of Technology and Design Education. 29(4):821-841. https://doi.org/10.1007/s10798-018-9458-zS821841294Ait-Aoudia, S., & Foufou, S. (2010). A 2D geometric constraint solver using a graph reduction method. Advances in Engineering Software, 41(10), 1187–1194. https://doi.org/10.1016/j.advengsoft.2010.07.008 .Ault, H. K. (1999). Using geometric constraints to capture design intent. Journal for Geometry and Graphics, 3(1), 39–45.Ault, H. K. (2004). Over-constrained, under-constrained or just right? Goldilocks evaluates DOF of sketched profiles. Paper presented at American Society for Engineering Education, 59th annual midyear meeting past, present and future? Williamsburg, November 21–23.Ault, H. K., Bu, L., & Liu, K. (2014). Solid modeling strategies-analyzing student choices. Paper presented at proceedings of the 121st ASEE annual conference and exposition, Indianapolis, June 15–18.Ault, H. K., & Fraser, A. (2013). A comparison of manual vs. online grading for solid models. Paper presented at 120th ASEE annual conference and exposition, Atlanta, GA, June 23–26, 2013, Paper ID #7233.Barbero, B. R., Pedrosa, C. M., & Samperio, R. Z. (2016). Learning CAD at university through summaries of the rules of design intent. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-016-9358-z .Bodein, Y., Bertrand, R., & Caillaud, E. (2014). Explicit reference modeling methodology in parametric CAD system. Computers in Industry, 65(1), 136–147. https://doi.org/10.1016/j.compind.2013.08.004 .Bouma, W., Fudos, I., Hoffmann, C., Cai, J., & Paige, R. (1995). Geometric constraint solver. Computer-Aided Design, 27(6), 487–501. https://doi.org/10.1016/0010-4485(94)00013-4 .Briggs, J. C., Hepworth, A. I., Stone, B. R., Cobum, J. Q., Jensen, C. G., & Red, E. (2015). Integrated, synchronous multi-user design and analysis. Journal of Computing and Information Science in Engineering, 15(3), 031002. https://doi.org/10.1115/1.4029801 .Buckley, J., Seery, N., & Canty, D. (2017). Heuristics and CAD modelling: An examination of student behaviour during problem solving episodes within CAD modelling activities. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-017-9423-2 .Camba, J. D., & Contero, M. (2015). Assessing the impact of geometric design intent annotations on parametric model alteration activities. Computers in Industry, 71, 35–45. https://doi.org/10.1016/j.compind.2015.03.006 .Camba, J. D., Contero, M., & Company, P. (2016). Parametric CAD modeling: An analysis of strategies for design reusability. Computer-Aided Design, 74, 18–31. https://doi.org/10.1016/j.cad.2016.01.003 .Camba, J. D., Contero, M., & Company, P. (2017). CAD reusability and the role of modeling information in the MBE context. Model-based enterprise summit 2017. National Institute of Standards and Technology (NIST), Gaithersburg, MD, April 3–7. MBE17-020. https://www.nist.gov/file/361581 .Cheng, Z., & Ma, Y. (2017). A functional feature modeling method. Advanced Engineering Informatics, 33, 1–15. https://doi.org/10.1016/j.aei.2017.04.003 .Cheng, Z., Xie, Y., & Ma, Y. (2018). Graph centrality analysis of feature dependencies to unveil modeling intents. Computer-Aided Design and Applications. https://doi.org/10.1080/16864360.2018.1441236 .Chester, I. (2007). Teaching for CAD expertise. International Journal of Technology and Design Education, 17, 23–35. https://doi.org/10.1007/s10798-006-9015-z .Company, P., Contero, M., Otey, J., & Plumed, R. (2015). Approach for developing coordinated rubrics to convey quality criteria in CAD training. Computer-Aided Design, 63, 101–117. https://doi.org/10.1016/j.cad.2014.10.00 .Company, P., & GonzĂĄlez-Lluch, C. (2013). CAD 3D con SolidWorks Âź Tomo I: Diseño bĂĄsico. Publicacions de la Universitat Jaume I. (ColecciĂłn Sapientia, NĂșm. 86). http://cad3dconsolidworks.uji.es .Contero, M., Company, P., Vila, C., & Aleixos, N. (2002). Product data quality and collaborative engineering. IEEE Computer Graphics Applications, 22(3), 32–42. https://doi.org/10.1109/MCG.2002.999786 .Dixon, B. M., & Dannenhoffer, J. F., III. (2014). Geometric sketch constraint solving with user feedback. Journal of Aerospace Information Systems, 11(5), 316–325. https://doi.org/10.2514/1.I010110 .Fudos, I., & Hoffmann, C. M. (1997). A graph-constructive approach to solving systems of geometric constraints. ACM Transactions on Graphics, 16(2), 179–216. https://doi.org/10.1145/248210.248223 .Ge, J. X., Chou, S. C., & Gao, X. S. (1999). Geometric constraint satisfaction using optimization methods. Computer-Aided Design, 31(14), 867–879. https://doi.org/10.1016/S0010-4485(99)00074-3 .GonzĂĄlez-Lluch, C., Company, P., Contero, M., Camba, J. D., & Colom, J. (2017a). A case study on the use of model quality testing tools for the assessment of MCAD models and drawings. International Journal of Engineering Education, 33(5), 1643–1653.GonzĂĄlez-Lluch, C., Company, P., Contero, M., Camba, J. D., & Plumed, R. (2017b). A survey on 3D CAD model quality assurance and testing tools. Computer-Aided Design, 83, 64–79. https://doi.org/10.1016/j.cad.2016.10.003 .Hamade, R. F. (2009). Profiling the desirable CAD trainee: Technical background, personality attributes, and learning preferences. Journal of Mechanical Design, 131(12), 121009–121019. https://doi.org/10.1115/1.4000455 .Hekman, K. A., & Gordon, M. T. (2013). Automated grading of first year student CAD work. Paper presented at the 120th ASEE annual conference and exposition 2013, Atlanta, GA, June 23–26. Paper ID #6379.Hepworth, A., Tew, K., Trent, M., Ricks, R., Jensen, C. G., & Red, E. R. (2014). Model consistency and conflict resolution with data preservation in multi-user computer aided design. Journal of Computing and Information Science in Engineering, 14(2), 021008. https://doi.org/10.1115/1.4026553 .Jackson, C., & Buxton, M. (2007). The design reuse benchmark report: Seizing the opportunity to shorten product development. Boston: Aberdeen Group.Joan-Arinyo, R., Soto-Riera, A., Vila-Marta, S., & Vilaplana-PastĂł, J. (2003). Transforming an under-constrained geometric constraint problem into a well-constrained one. Paper presented at proceedings of ACM SM03, Seatle, June 16–20.Kirstukas, S. J. (2016). Development and evaluation of a computer program to assess student CAD models. Paper presented at ASEE annual conference and exposition, New Orleans, June 26.Kramer, G. (1991). Using degrees of freedom analysis to solve geometric constraint systems. Paper presented at proceedings of the first ACM symposium on solid modeling foundations and CAD/CAM applications 1991, Austin, June 05–07.Kwon, S., Kim, B. C., Mun, D., & Han, S. (2015). Graph-based simplification of feature-based three-dimensional computer-aided design models for preserving connectivity. Journal of Computing and Information Science in Engineering, 15(3), 031010. https://doi.org/10.1115/1.4030748 .Leea, J. Y., & Kimb, K. (1998). A 2-D geometric constraint solver using DOF-based graph reduction. Computer-Aided Design, 30(11), 883–896. https://doi.org/10.1016/S0010-4485(98)00045-1 .Mata Burgarolas, N. (1997). Solving incidence and tangency constraints in 2D. Technical report LSI-97-3R, Departament LiSI, Universitat PolitĂšcnica de Catalunya.Petrina, S. (2003). Two cultures of technical courses and discourses: The case of computer aided design. International Journal of Technology and Design Education, 13, 47–73.Race, P. (2001). The lecturers toolkit—A practical guide to learning, teaching and assessment. Great Britain: Glasgow.Red, E., French, D., Jensen, G., Walker, S. S., & Madsen, P. (2013). Emerging design methods and tools in collaborative product development. Journal of Computing and Information Science in Engineering, 13(3), 031001. https://doi.org/10.1115/1.4023917 .Robertson, B. F., Walther, J., & Radcliffe, D. (2007). Creativity and the use of CAD tools: Lessons for engineering design education from industry. Journal of Mechanical Design, 129(7), 753–760. https://doi.org/10.1115/1.2722329 .Stone, B., Salmon, J., Eves, K., Killian, M., Wright, L., Oldroyd, J., et al. (2017). A multi-user computer-aided design competition: Experimental findings and analysis of team-member dynamics. Journal of Computing and Information Science in Engineering, 17(3), 031003. https://doi.org/10.1115/1.4035674 .Summers, J. D., & Shah, J. J. (2010). Mechanical engineering design complexity metrics: Size, coupling, and solvability. Journal of Mechanical Design, 132(2), 21004–21015. https://doi.org/10.1115/1.4000759 .Szewczyk, J. (2003). Difficulties with the novices’ comprehension of the computer-aided design (CAD) interface: Understanding visual representations of CAD tools. Journal of Engineering Design, 14(2), 169–185. https://doi.org/10.1080/0954482031000091491

    Segmentation by Tangent Filter

    No full text
    International audienceImage contour detection is fundamental to many image analysis applications, including image segmentation, object recognition and classification.In this paper, the main idea is to treat the pixel with a special mask for each pixel based on a criterion the orientation of the gradient. The masks used are inspired by the operators of Laplacian. The pixel processed is not in the center of the mask but on its tangent. The results obtained are very positive on certain types of images

    SNW-MAC: an Asynchronous Protocol Leveraging Wake-up Receivers for Data Gathering in Star Networks

    No full text
    International audienceA widespread approach to extend lifetime of battery-powered wireless sensor nodes is duty-cycling, which consists in periodically switching on and off node transceiver. However, energy waste in idle listening periods is still a bottleneck. These periods can be completely removed using emerging ultra-low power wake-up receivers, which continuously listen to the channel with negligible power consumption. In this paper, an asynchronous medium access control protocol is proposed for data gathering in a star network topology. The protocol exploits state-of-the-art wake-up receivers to minimize the energy required to transmit a packet and to make collisions impossible. The proposed approach has been implemented on a real hardware platform and tested in-field. Experimental results demonstrate the benefits of the proposed approach in terms of energy efficiency, power consumption and throughput, which can be up to more than two times higher compared to traditional schemes

    A concise and persistent feature to study brain resting‐state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative

    No full text
    Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI
    corecore