A framework for similarity recognition of CAD models

Abstract A designer is mainly supported by two essential factors in design decisions. These two factors are intelligence and experience aiding the designer by predicting the interconnection between the required design parameters. Through classification of product data and similarity recognition between new and existing designs, it is partially possible to replace the required experience for an inexperienced designer. Given this context, the current paper addresses a framework for recognition and flexible retrieval of similar models in product design. The idea is to establish an infrastructure for transferring design as well as the required PLM (Product Lifecycle Management) know-how to the design phase of product development in order to reduce the design time. Furthermore, such a method can be applied as a brainstorming method for a new and creative product development as well. The proposed framework has been tested and benchmarked while showing promising results.

[1]  Leila De Floriani,et al.  Geometric modeling of solid objects by using a face adjacency graph representation , 1985, SIGGRAPH.

[2]  Sung-Hyuk Cha.  Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .

[3]  Herwart Opitz,et al.  A classification system to describe workpieces , 1970 .

[4]  Shih-Wen Hsiao,et al.  A decision support system for product family design , 2014, Inf. Sci..

[5]  Jie Lu,et al.  Content-based retrieval in hybrid peer-to-peer networks , 2003, CIKM '03.

[6]  Karthik Ramani,et al.  Developing an engineering shape benchmark for CAD models , 2006, Comput. Aided Des..

[7]  Susan Shannon.  Trends In Computer Science , 2004 .

[8]  Gavriel Salvendy,et al.  Handbook of industrial engineering , 2001 .

[9]  Gavriel Salvendy,et al.  Handbook of industrial engineering : technology and operations management , 2001 .

[10]  T. Warren Liao,et al.  Integration of a feature-based CAD system and an ART1 neural model for GT coding and part family forming , 1994 .

[11]  Angappa Gunasekaran,et al.  A decision support system for integrating manufacturing and product design into the reconfiguration of the supply chain networks , 2012, Decis. Support Syst..

[12]  Simon King,et al.  Asynchronous Articulatory Feature Recognition Using Dynamic Bayesian Networks , 2004 .

[13]  William C. Regli,et al.  Benchmarking CAD search techniques , 2005, SPM '05.

[14]  John W. Boyse,et al.  Solid Modeling by Computers: From Theory To Applications , 2011 .

[15]  Dieter Roller.  Design by features: an approach to high level shape manipulations , 1989 .

[16]  홍태식,et al.  Similarity comparison of mechanical parts to reuse existing designs , 2006 .

[17]  Rainer Stark,et al.  Multi-criteria Decision Making as a Tool for Sustainable Product Development – Benefits and Obstacles , 2015 .

[18]  Ashish Verma,et al.  Keyword Search using Modified Minimum Edit Distance Measure , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[19]  Michael M. Marefat,et al.  Similarity-Based Retrieval of CAD Solid Models for Automated Reuse of Machining Process Plans , 2007, 2007 IEEE International Conference on Automation Science and Engineering.

[20]  Guo Li,et al.  A New CAD Models Retrieval Method Based on Shape Similarity , 2009 .

[21]  Jerry Y. H. Fuh,et al.  Toward Effective Mechanical Design Reuse: CAD Model Retrieval Based on General and Partial Shapes , 2009 .

[22]  Raymond J. Mooney,et al.  Adaptive duplicate detection using learnable string similarity measures , 2003, KDD '03.

[23]  Pu Jiantao,et al.  3D model retrieval based on 2D slice similarity measurements , 2004 .

[24]  John W. Boyse,et al.  Solid Modeling by Computers , 1984, Springer US.

[25]  Dieter Roller,et al.  Beyond Similarity Comparison: Intelligent Data Retrieval for CAD/CAM Designs , 2013 .

[26]  Mark Henderson,et al.  Automated Group Technology Part Coding From a Three-Dimensional CAD Database , 1988 .

[27]  R. Allen Miller,et al.  A database system of mechanical components based on geometric and topological similarity. Part I: representation , 2003, Comput. Aided Des..

[28]  Nallan C. Suresh,et al.  A neural network system for shape-based classification and coding of rotational parts , 1991 .

[29]  Sheena S. Iyengar,et al.  Eternal Quest for the Best: Sequential (vs. Simultaneous) Option Presentation Undermines Choice Commitment , 2013 .

[30]  Dieter Roller,et al.  Automated Rule-based System for Opitz Feature Recognition and Code Generation from STEP , 2016 .

[31]  Dinesh Manocha,et al.  Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling, Beijing, China, June 4-6, 2007 , 2007, Symposium on Solid and Physical Modeling.

[32]  R. Allen Miller,et al.  A database system of mechanical components based on geometric and topological similarity. Part II: indexing, retrieval, matching, and similarity assessment , 2003, Comput. Aided Des..

[33]  M. Lepper,et al.  The Construction of Preference: When Choice Is Demotivating: Can One Desire Too Much of a Good Thing? , 2006 .

[34]  Yusheng Liu,et al.  Design reuse oriented partial retrieval of CAD models , 2010, Comput. Aided Des..

[35]  Thomas A. Funkhouser,et al.  The Princeton Shape Benchmark , 2004, Proceedings Shape Modeling Applications, 2004..