Published June 10, 2021 | Version v1
Journal article Open

Development of product complexity index in 3D models using a hybrid feature recognition method with rule-based and graph-based methods

Creators

  • 1. Universitas Indonesia

Description

A machining process is very dependent on the model created. The more complicated the model, the greater the design difficulty and the greater the machining process. Reduced production costs can help a company increase profits. A focus on production cost can be achieved in a number of ways, the first of which is by replacing materials or changing the design. It is better to reduce product costs during the design stage than during the manufacturing stage. The main objective of this research is to develop an application that can recognize features in a CAD program and calculate the complexity index of shapes in real time. In this study, the prismatic features and slab features classified by Jong-Yun Jung were used. The feature recognition method applied in this study is a hybrid of the rule-based and graph-based methods, which uses the STL file developed by Sunil and Pande to obtain all the information needed. Then, the results are extracted from feature recognition data and are used to calculate the product complexity index of the model being studied. This study applied the product complexity index, following the model developed earlier by El Maraghy. Validation is performed by comparing the software count with the complexity index calculated with the STEP method by Hendri and Sholeh et al. This research develops a program that recognizes features in CAD software and calculates the index complexity of shapes in real time. This will allow designers to calculate the expected complexity value during the design process. As a result, the estimated production cost can be seen early on. Finally, this software is tested for calculating the index values for the complexity of a combined features model. The use of eight slots and eight pockets as a benchmark scoring for shape produces a more accurate product complexity index

Files

Development of product complexity index in 3D models using a hybrid feature recognition method with rule-based and graph-based methods.pdf

Files (1.4 MB)

Additional details

References

  • Roy, R., Souchoroukov, P., Shehab, E. (2011). Detailed cost estimating in the automotive industry: Data and information requirements. International Journal of Production Economics, 133 (2), 694–707. doi: https://doi.org/10.1016/j.ijpe.2011.05.018
  • Farineau, T., Rabenasolo, B., Castelain, J. M., Meyer, Y., Duverlie, P. (2001). Use of Parametric Models in an Economic Evaluation Step During the Design Phase. The International Journal of Advanced Manufacturing Technology, 17 (2), 79–86. doi: https://doi.org/10.1007/s001700170195
  • Shehab, E. M., Abdalla, H. S. (2001). Manufacturing cost modelling for concurrent product development. Robotics and Computer-Integrated Manufacturing, 17 (4), 341–353. doi: https://doi.org/10.1016/s0736-5845(01)00009-6
  • Wasim, A., Shehab, E., Abdalla, H., Al-Ashaab, A., Sulowski, R., Alam, R. (2012). An innovative cost modelling system to support lean product and process development. The International Journal of Advanced Manufacturing Technology, 65 (1-4), 165–181. doi: https://doi.org/10.1007/s00170-012-4158-4
  • Budiono, H. D. S., Kiswanto, G., Soemardi, T. P. (2014). Method and Model Development for Manufacturing Cost Estimation during the Early Design Phase Related to the Complexity of the Machining Processes. International Journal of Technology, 5 (2), 183. doi: https://doi.org/10.14716/ijtech.v5i2.402
  • Fischer, P., Heingärtner, J., Duncan, S., Hora, P. (2020). On part-to-part feedback optimal control in deep drawing. Journal of Manufacturing Processes, 50, 403–411. doi: https://doi.org/10.1016/j.jmapro.2019.10.019
  • Mali, R. A., Gupta, T. V. K., Ramkumar, J. (2021). A comprehensive review of free-form surface milling– Advances over a decade. Journal of Manufacturing Processes, 62, 132–167. doi: https://doi.org/10.1016/j.jmapro.2020.12.014
  • Boothroyd, G., Dewhurst, P., Knight, W. A. (2010). Product Design for Manufacture and Assembly. CRC Press, 712. doi: https://doi.org/10.1201/9781420089288
  • Limon-Leyva, P. A., Balvantín, A. J., Diosdado-De-la-Peña, J. A., Figueroa-Díaz, R. A., Rojas-Mancera, E., Ramirez, V. A. (2020). Parametric optimization of roll-hemming process in oblique planes with linear and non-linear trajectories. Journal of Manufacturing Processes, 50, 123–131. doi: https://doi.org/10.1016/j.jmapro.2019.12.019
  • Budiono, H. D. S., Sholeh, M., Kiswanto, G., Soemardi, T. P. (2014). Application of Semi Automatic Model of Product Complexity Index Calculation by Identification and Recognition of Geometric Features Information. Applied Mechanics and Materials, 493, 576–582. doi: https://doi.org/10.4028/www.scientific.net/amm.493.576
  • Kresnha, P. (2010). Feature Recognition Model Facet 3D using Fuzzy Geometry Based on Drive Point on Multiaxis CAM System. Universitas Indonesia.
  • Shah, J. J., Anderson, D., Kim, Y. S., Joshi, S. (2000). A Discourse on Geometric Feature Recognition From CAD Models. Journal of Computing and Information Science in Engineering, 1 (1), 41–51. doi: https://doi.org/10.1115/1.1345522
  • Abouel Nasr, E. S., Kamrani, A. K. (2006). A new methodology for extracting manufacturing features from CAD system. Computers & Industrial Engineering, 51 (3), 389–415. doi: https://doi.org/10.1016/j.cie.2006.08.004
  • Jones, T. J., Reidsema, C., Smith, A. (2006). Automated Feature Recognition System for supporting conceptual engineering design. International Journal of Knowledge-Based and Intelligent Engineering Systems, 10 (6), 477–492. doi: https://doi.org/10.3233/kes-2006-10606
  • Shahin, T. M. M. (2008). Feature-Based Design – An Overview. Computer-Aided Design and Applications, 5 (5), 639–653. doi: https://doi.org/10.3722/cadaps.2008.639-653
  • Salomons, O. W., van Slooten, F., Jonker, H. G., van Houten, F. J. A. M., Kals, H. J. J. (1995). Interactive feature definition. IFIP Advances in Information and Communication Technology, 144–160. doi: https://doi.org/10.1007/978-0-387-34834-6_8
  • Sreevalsan, P. C., Shah, J. J. (1992). Unification of form feature definition methods. Proceedings of the IFIP WG 5.2 Working Conference on Intelligent Computer Aided Design, 83–106.
  • Pratt, M. J., Wilson, P. R. (1985). Requirements for support of form features in a solid modelling system. Final Report, CAM-I Report R-85-ASPP-01.
  • Jung, J.-Y. (2002). Manufacturing cost estimation for machined parts based on manufacturing features. Journal of Intelligent Manufacturing, 13, 227–238. doi: https://doi.org/10.1023/A:1016092808320
  • Sunil, V. B., Agarwal, R., Pande, S. S. (2010). An approach to recognize interacting features from B-Rep CAD models of prismatic machined parts using a hybrid (graph and rule based) technique. Computers in Industry, 61 (7), 686–701. doi: https://doi.org/10.1016/j.compind.2010.03.011
  • ElMaraghy, W. H., Urbanic, R. J. (2003). Modelling of Manufacturing Systems Complexity. CIRP Annals, 52 (1), 363–366. doi: https://doi.org/10.1016/s0007-8506(07)60602-7
  • Sunil, V. B., Pande, S. S. (2008). Automatic recognition of features from freeform surface CAD models. Computer-Aided Design, 40 (4), 502–517. doi: https://doi.org/10.1016/j.cad.2008.01.006
  • ISO 2768. General Geometrical Tolerances and Technical Drawings. Available at: https://www.plianced.com/compliance-wiki/iso-2768-general-geometrical-tolerances-and-technical-drawings/