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Cognitive shape similarity assessment for 3D part search

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Abstract

Mass customization aims to satisfy diverse customer requirements with high product variety while maintaining reasonable manufacturing cost and lead time. Allowing customers to perceive product differentiation is a critical factor for most design methods developed for mass customization. This study examines 3D part search from the human cognitive perspective. We designed and conducted a quasi-factorial experiment to understand how structured variations of four factors—the shape, type, dimension, and location of the feature volume of a part model—affect human judgment of part similarity. The corresponding factorial similarity values were computed with different shape signatures in the form of the feature adjacency graph. The human responses were obtained by paired comparisons of test parts, and quantified as the cognitive similarity. Statistical analysis of the experimental results showed that the type and shape factors played an important role in the subjects’ judgments. Back-propagation neural networks were trained to model the correlations between the cognitive and the factorial similarity values. The performance of the networks validates our idea of incorporating human cognition into assessment of 3D part similarity. This study presents a systematic approach for personalized part search that reflects individual perception of shape similarity.

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References

  • As’ari, M. A., Sheikh, U. U., & Supriyanto, E. (2014). 3D shape descriptor for object recognition based on Kinect-like depth image. Image and Vision Computing, 32(4), 260–269.

    Article  Google Scholar 

  • Chen, Y., Huang, J., Zhang, Z., & Xie, Y. (2013). A part affordance-based approach for capturing detailed design knowledge. Computer-Aided Design, 45(12), 1617–1629.

    Article  Google Scholar 

  • Cheng, H. C. (2010). 3D similar part search and its applications. Ph.D. Dissertation, National Tsing Hua University.

  • Cheng, H. C., Lo, C. H., Chu, C. H., & Kim, Y. S. (2011). Shape similarity measurement for 3D mechanical part using D2 shape distribution and negative feature decomposition. Computers in Industry, 62(3), 269–280.

    Article  Google Scholar 

  • Chu, C. H., Cheng, H. C., Wang, E., & Kim, Y. S. (2009). ANN-based 3D part search with different levels of detail (LOD) in negative feature decomposition. Expert Systems with Applications, 36, 10905–10913.

    Article  Google Scholar 

  • Chu, C. H., & Hsu, Y. C. (2006). Similarity assessment of 3D mechanical components for design reuse. Robotics & CIM, 22(4), 332–341.

    Google Scholar 

  • Clark, D. E. R., Corney, J. R., Mill, F., Rea, H. J., & Sherlock, A. (2006). Benchmarking shape signatures against human perceptions of geometric similarity. Computer-Aided Design, 38(9), 1038–1051.

    Article  Google Scholar 

  • Goh, B. (1993). Taguchi methods: Some technical, cultural and pedagogical perspectives. Quality and Reliability Engineering International, 9, 185–202.

    Article  Google Scholar 

  • Gold, S., & Rangarajan, A. (1996). A graduated assignment algorithm for graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(4), 377–388.

    Article  Google Scholar 

  • Jayanti, S., Kalyanaraman, Y., Iyer, N., & Ramani, K. (2006). Developing an engineering shape benchmark for CAD models. Computer-Aided Design, 38(9), 939–953.

    Article  Google Scholar 

  • Lefkoff-Hagius, R., & Mason, C. H. (1993). Characteristic, beneficial, and image attributes in consumer judgments of similarity and preference. Journal of Consumer Research, 20(1), 100–110.

    Article  Google Scholar 

  • Li, M., Zhang, Y. F., Fuh, J. Y., & Qiu, Z. M. (2011). Design reusability assessment for effective CAD model retrieval and reuse. International Journal of Computer Applications in Technology, 40(1–2), 3–12.

  • Liaw, C. F. (2014). A fast heuristic to minimize number of tardy jobs in preemptive open shops. Journal of Industrial and Production Engineering, 31(1), 27–35.

    Article  Google Scholar 

  • Lin, C. C., Kang, J. R., & Hsu, T. H. (2015). A memetic algorithm with recovery scheme for nurse preference scheduling. Journal of Industrial and Production Engineering, 32(2), 83–95.

    Article  Google Scholar 

  • Liu, Y. J., Luo, X., Joneja, A., Ma, C. X., Fu, X. L., & Song, D. (2013). User-adaptive sketch-based 3-D CAD model retrieval. Automation Science and Engineering, IEEE Transactions, 10(3), 783–795.

    Article  Google Scholar 

  • Mavridou, E., Kehagias, D. D., Tzovaras, D., & Hassapis, G. (2013). Mining affective needs of automotive industry customers for building a mass-customization recommender system. Journal of Intelligent Manufacturing, 24(2), 251–265.

    Article  Google Scholar 

  • Osada, R., Funkhouser, T., Chazelle, B., & Dobkin, D. (2002). Shape distributions. ACM Transactions on Graphics, 21(4), 807–832.

    Article  Google Scholar 

  • Rea, H. J., Sung, R., Corney, J. R., Clark, D. E. R., & Taylor, N. K. (2005). Interpreting three-dimensional shape distribution. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 219(6), 553–566.

    Article  Google Scholar 

  • Regli, W. C., & Circirello, V. A. (2000). Managing digital libraries for computer-aided design. Computer-Aided Design, 32(2), 119–132.

    Article  Google Scholar 

  • Smith, S., Smith, G. C., Jiao, R., & Chu, C. H. (2013). Mass customization in the product life cycle. Journal of Intelligent Manufacturing, 24(5), 877–885.

    Article  Google Scholar 

  • Theologou, P., Pratikakis, I., & Theoharis, T. (2014). A review on 3D object retrieval methodologies using a part-based representation. Computer-Aided Design and Applications, 11(6), 670–684.

    Article  Google Scholar 

  • You, C. F., & Tsai, Y. L. (2010). 3D solid model retrieval for engineering reuse based on local feature correspondence. The International Journal of Advanced Manufacturing Technology, 46(5–8), 649–661.

    Article  Google Scholar 

  • Zhang, M., Zhang, L., Mathiopoulos, P. T., Ding, Y., & Wang, H. (2013). Perception-based shape retrieval for 3D building models. ISPRS Journal of Photogrammetry and Remote Sensing, 75, 76–91.

    Article  Google Scholar 

  • Zhu, K., Wong, Y. S., Loh, H. T., & Lu, W. F. (2012). 3D CAD model retrieval with perturbed Laplacian spectra. Computers in Industry, 63(1), 1–11.

    Article  Google Scholar 

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Correspondence to Chih-Hsing Chu.

Appendices

Appendix 1: Factor level settings of the test and validation parts

Part no.

Factor levels

Dimension

Type

Location

Shape

Test parts (from P1 to P27)

   P1

1

1

1

1

   P2

1

1

2

2

   P3

1

1

3

3

   P4

1

2

1

2

   P5

1

2

2

3

   P6

1

2

3

1

   P7

1

3

1

3

   P8

1

3

2

1

   P9

1

3

3

2

   P10

2

1

1

2

   P11

2

1

2

3

   P12

2

1

3

1

   P13

2

2

1

3

   P14

2

2

2

1

   P15

2

2

3

2

   P16

2

3

1

1

   P17

2

3

2

2

   P18

2

3

3

3

   P19

3

1

1

3

   P20

3

1

2

1

   P21

3

1

3

2

   P22

3

2

1

1

   P23

3

2

2

2

   P24

3

2

3

3

   P25

3

3

1

2

   P26

3

3

2

3

   P27

3

3

3

1

Validation parts (P28–P33)

   P28

1

1

2

1

   P29

1

2

1

3

   P30

2

3

3

1

   P31

2

2

1

2

   P32

3

1

2

3

   P33

3

3

3

2

Appendix 2: 3D mechanical parts for the test

figure b

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Chu, CH., Lo, CH. & Cheng, HC. Cognitive shape similarity assessment for 3D part search. J Intell Manuf 28, 1679–1694 (2017). https://doi.org/10.1007/s10845-016-1211-4

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  • DOI: https://doi.org/10.1007/s10845-016-1211-4

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