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Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value

  • Amaninder Singh Gill (a1), Joshua D. Summers (a1) and Cameron J. Turner (a1)

Abstract

Benchmarking function modeling and representation approaches requires a direct comparison, including the inferencing support by the different approaches. To this end, this paper explores the value of a representation by comparing the ability of a representation to support reasoning based on varying amounts of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional pruning rules into vocabulary, grammatical, and topological classes and applying them to function structures available from an external design repository. The original and pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using the graph complexity connectivity method. The accuracy is found to be inversely related to the amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduces the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified.

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Copyright

Corresponding author

Reprint requests to: Joshua D. Summers, Department of Mechanical Engineering, Clemson University, 203 Fluor Daniel Engineering Innovation Building, Clemson, SC 29634-0921, USA. E-mail: jsummer@clemson.edu

References

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Ameri, F., Summers, J.D., Mocko, G.M., & Porter, M. (2008). Engineering design complexity: an investigation of methods and measures. Research in Engineering Design 19(2), 161179.
Bashir, H.A., & Thomson, V. (2001). An analogy-based model for estimating design effort. Design Studies 22, 157167.
Bashir, H.A., & Thomson, V. (2004). Estimating design effort for GE hydro project. Computers and Industrial Engineering 46, 195204.
Bohm, M., Stone, R., & Szykman, S. (2005). Enhancing virtual product representations for advanced design repository systems. Journal of Computing and Information Science in Engineering 5(4), 360372.
Bohm, M.R., Vucovich, J.P., & Stone, R.B. (2008). Using a design repository to drive concept generation. Journal of Computing and Information Science in Engineering 8(1), 14502.
Bradley, S.R., & Agogino, A.M. (1994). An intelligent real time design methodology for component selection: an approach to managing uncertainty. Journal of Mechanical Design 116(4), 980988.
Braha, D., & Maimon, O. (1998). The measurement of a design structural and functional complexity. IEEE Transactions on Systems, Man, and Cybernetics: Part A: Systems and Humans 28(4), 527535.
Caldwell, B.W., & Mocko, G.M. (2008). Towards rules for functional composition. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., pp. 319–328. Brooklyn, NY: ASME.
Caldwell, B.W., & Mocko, G.M. (2012). Validation of function pruning rules through similarity at three level of abstraction. Journal of Mechanical Design 134(4), 41008. doi:10.1115/1.4006264
Caldwell, B.W., Ramachandran, R., & Mocko, G.M. (2012). Assessing the use of function models and interaction models through concept sketching. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., pp. 633–646. Chicago: ASME.
Caldwell, B.W., Sen, C., Mocko, G.M., & Summers, J.D. (2010). An empirical study of the expressiveness of the functional basis. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 25(3), 273.
Caldwell, B.W., Thomas, J., Sen, C., Mocko, G.M., & Summers, J.D. (2012). The effects of language and pruning on function structure interpretability. Journal of Mechanical Design 134(6), 61001. doi:10.1115/1.4006442
Chakrabarti, A., Shea, K., Stone, R., Cagan, J., Campbell, M., Hernandez, N.V., & Wood, K.L. (2011). Computer-based design synthesis research: an overview. Journal of Computing and Information Science in Engineering 11(2), 21003. doi:10.1115/1.3593409
Chandrasekaran, B., Goel, A.K., & Iwasaki, Y. (1993). Functional representation as design rationale. Computer 26(1). doi:10.1109/2.179157
Chandrasekaran, B., & Josephson, J. (2000). Function in device representation. Engineering With Computers 16(3–4), 162177.
Gero, J.S., & Kannengiesser, U. (2004). The situated function-behaviour-structure framework. Design Studies 25(4), 373391. doi:10.1016/j.destud.2003.10.010
Gill, A., & Summers, J.D. (2016). Impact of level of detail and information content on accuracy of function structure-based market price prediction models. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2016-59662. Charlotte, NC: ASME.
Hirtz, J., Stone, R., McAdams, D., Szykman, S., & Wood, K. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13(2), 6582.
Kohzadi, N., Boyd, M.S., Kermanshahi, B., Kaastra, I., & Khozadi, N. (1996). A comparison of artificial neural networks and time series models for forecasting commodity prices. Neurocomputing 10(2), 161181.
Kurfman, M.A., Stone, R.B., van Wie, M., Wood, K.L., & Otto, K.N. (2000). Theoretical underpinnings of functional modeling: preliminary experimental studies. Proc. Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DTM-14563. Baltimore, MD: ASME.
Kurtoglu, T., Campbell, M., Bryant, C., Stone, R., & McAdams, D. (2005). Deriving a component basis for computational functional synthesis. Proc. Int. Conf. Engineering Design, Vol. 5. Melbourne, Australia: Design Society.
Lucero, B., Linsey, J., & Turner, C.J. (2016). Frameworks for organising design performance metrics. Journal of Engineering Design 27(4–6), 175204.
Lucero, B., Viswanathan, V.K., Linsey, J.S., & Turner, C.J. (2014). Identifying critical functions for use across engineering design domains. Journal of Mechanical Design 136(12), 121101.
Mathieson, J.L., Arlitt, R., Summers, J.D., Stone, R., Shanthakumar, A., & Sen, C. (2011). Complexity as a surrogate mapping between function models and market value. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2011-47481. Washington, DC: ASME.
Mathieson, J.L., & Summers, J.D. (2010). Complexity metrics for directional node-link system representations: theory and applications. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2010-28561. Montreal: ASME.
Mathieson, J.L., Wallace, B.A., & Summers, J.D. (2013). Assembly time modelling through connective complexity metrics. International Journal of Computer Integrated Manufacturing 26(10), 955967. doi:10.1080/0951192X.2012.684706
Messer, M., Panchal, J.H., Allen, J.K., Mistree, F., Krishnamurthy, V., Klein, B., & Yoder, P.D. (2008). Designing embodiment design processes using a value-of-information-based approach with applications for integrated product and materials design. Proc. ASME 2008 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., pp. 823–840. Brooklyn, NY: ASME.
Miller, M., Mathieson, J., Summers, J.D., & Mocko, G.M. (2012). Representation: structural complexity of assemblies to create neural network based assembly time estimation models. Proc. Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2012-71337. Chicago: ASME.
Mohinder, C.V.S., Gill, A., & Summers, J.D. (2016). Using graph complexity connectivity method to predict information from design representations. Proc. Design Computing and Cognition'16 (Gero, J.S., Ed.), Paper No. 73. Cham, Switzerland: Springer.
Mohinder, C.V.S., Sudarshan, S., & Summers, J.D. (2014). Structural complexity metrics applied against product graphs. Proc. Design Computing and Cognition'14 (Gero, J.S., Ed.), p. 51. London: Springer.
Montecchi, T., & Russo, D. (2015). FBOS: function/behaviour-oriented search. Procedia Engineering 131, 140149.
Nagel, R.L., Bohm, M.R., Linsey, J.S., & Riggs, M.K. (2015). Improving students’ functional modeling skills: a modeling approach and a scoring rubric. Journal of Mechanical Design 137(5), 51102.
Namouz, E.Z., & Summers, J.D. (2013). Complexity connectivity metrics—predicting assembly times with abstract low fidelity assembly CAD models. In Smart Product Engineering (Abramivici, M., & Stark, R., Ed.), pp. 777786. Bochum, Germany: Springer.
Namouz, E.Z., & Summers, J.D. (2014). Comparison of graph generation methods for structural complexity based assembly time estimation. Journal of Computing and Information Science in Engineering 14(2), 02100310210039. doi:10.1115/1.4026293
Nix, A.A., Sherrett, B., & Stone, R.B. (2011). A function based approach to TRIZ. Proc. ASME Int. Design Engineering Technical Conf., pp. 29–31, Washington, DC, August 28–31.
Otto, K., & Wood, K. (2001). Product Design Techniques in Reverse Engineering and New Product Development. Upper Saddle River, NJ: Prentice Hall.
Owensby, J.E., Namouz, E.Z., Shanthakumar, A., & Summers, J.D. (2012). Representation: extracting mate complexity from assembly models to automatically predict assembly times. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2012-70995. Chicago: ASME.
Owensby, J.E., & Summers, J.D. (2014). Assembly time estimation: assembly mate based structural complexity metric predictive modeling. Journal of Computing and Information Science in Engineering 14(1), 011004.1011004.12. doi:10.1115/1.4025808
Pahl, G., Beitz, W., Blessing, L., Feldhusen, J., Grote, K.-H.H., & Wallace, K. (2013). Engineering Design: A Systematic Approach, Vol. 11, 3rd ed. London: Springer-Verlag.
Panchal, J.H., Paredis, C.J.J., Allen, J.K., & Mistree, F. (2008). A value-of-information based approach to simulation model refinement. Engineering Optimization 40(3), 223251.
Patel, A., Andrews, P., & Summers, J.D. (2016). Evaluating the use of artificial neural networks to predict assembly defects. Proc. Int. Design Engineering Conf. Computers in Engineering Conf., Paper No. DETC2016-59664. Charlotte, NC: ASME.
Perzyk, M., Biernacki, R., & Kochański, A. (2005). Modeling of manufacturing processes by learning systems: the naïve Bayesian classifier versus artificial neural networks. Journal of Materials Processing Technology 164, 14301435.
Qian, L., & Gero, J.S. (1996). Function-behavior-structure paths and their role in analogy-based design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 289312.
Radhakrishnan, R., & McAdams, D.A. (2005). A methodology for model selection in engineering design. Journal of Mechanical Design 127(3), 378387.
Rosen, D.W., & Summers, J.D. (2012). Mechanical Engineering Modeling Language (MEml): Necessary research directions. Proc. Int. Conf. Innovative Design and Manufacturing, Taipei, Taiwan.
Russo, D., Montecchi, T., & Liu, Y. (2012). Functional-based search for patent technology transfer. Proc. ASME 2012 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., pp. 529–539. Chicago: ASME.
Russo, D., & Rizzi, C. (2014). A function oriented method for competitive technological intelligence and technology forecasting. Proc. 2014 Int. ICE Conf. Engineering, Technology, pp. 1–9. Bergamo, Italy: IEEE.
Schultz, J., Mathieson, J., Summers, J.D., & Caldwell, B. (2014). Limitations to function structures: a case study in morphing airfoil design. Proc. ASME Int. Design Engineering Technical Conf. Information in Engineering Conf. Buffalo, NY: ASME.
Sen, C., Caldwell, B.W., Summers, J.D., & Mocko, G.M. (2010). Evaluation of the functional basis using an information theoretic approach. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(1), 87.
Sen, C., Summers, J.D., & Mocko, G.M. (2010). Topological information content and expressiveness of function models in mechanical design. Journal of Computing and Information Science in Engineering 10(3), 31003. doi:10.1115/1.3462918
Shah, J.J., & Runger, G. (2011). Misuse of information—theoretic dispersion measures as design complexity metrics. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2011/DTM-48295. Washington, DC: ASME.
Sharda, R., & Patil, R.B. (1992). Connectionist approach to time series prediction: an empirical test. Journal of Intelligent Manufacturing 3, 317323.
Shimomura, Y., Yoshioka, M., Takeda, H., Umeda, Y., & Tomiyama, T. (1998). Representation of design object based on the functional evolution process model. Journal of Mechanical Design 120(2), 221229.
Shtub, A., & Versano, R. (1999). Estimating the cost of steel pipe bending: a comparison between neural networks and regression analysis. International Journal of Production Economics 62, 201207.
Singh, G., Balaji, S., Shah, J.J., Corman, D., Howard, R., Mattikalli, R., & Stuart, D. (2012). Evaluation of network measures as complexity petrics. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2012-70483. Chicago: ASME.
Sinha, K., & de Weck, O.L. (2013 a). A network-based structural complexity metric for engineered complex systems. Proc. 2013 IEEE Int. Systems Conf. (SysCon), pp. 426–430, Orlano, FL, August 12–15.
Sinha, K., & de Weck, O.L. (2013 b). Structural complexity quantification for engineered complex systems and implications on system architecture and design. Proc. ASME 2013 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. V03AT03A044-V03AT03A044. Portland, OR: ASME.
Sridhar, S., Fazelpour, M., Gill, A.S., & Summers, J.D. (2016 a). Accuracy and precision analysis of the graph complexity connectivity method. Procedia CIRP 44, 163168. doi:10.1016/j.procir.2016.02.029
Sridhar, S., Fazelpour, M., Gill, A., & Summers, J.D. (2016 b). Precision analysis of the graph complexity connectivity method: assembly and function model. Proc. CIRP CATS 2016, p. 1095. Gothenburg, Sweden: CIRP.
Stone, R.B., & Wood, K.L. (2000). Development of a functional basis for design. Journal of Mechanical Design 122, 359.
Summers, J.D. (2005). Reasoning in engineering pesign. Proc. Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DTM-85334. Long Beach, CA: ASME.
Summers, J.D., Eckert, C., & Goel, A.K. (2013). Function in engineering: benchmarking representations and models. Proc. Int. Conf. Engineering Design. Seoul: Design Society.
Summers, J.D., Miller, M.G., Mathieson, J.L., Mocko, G.M., Summers, J.D., Mathieson, J.L., & Mocko, G.M. (2014). Manufacturing assembly time estimation using structural complexity metric trained artificial neural networks. Journal of Computing and Information Science in Engineering 14(1), 11005. doi:10.1115/1.4025809
Summers, J.D., & Rosen, D.W. (2013). Mechanical engineering modeling language: foundations. Proc. Int. Conf. Engineering Design. Seoul: Design Society.
Summers, J.D., & Shah, J.J. (2004). Representation in engineering design: a framework for classification. Proc. Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DTM-57514. Salt Lake, UT: ASME.
Szykman, S., Racz, J., & Sriram, R.D. (1999). The representation of function in computer-based design. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf. Las Vegas, NV: ASME.
Thomke, S.H. (1998). Managing experimentation in the design of new products. Management Science 44(6), 743762.
Tu, J.V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology 49(11), 12251231.
Ullman, D.G. (2010). The Mechanical Design Process, 4th ed. New York: McGraw-Hill.
Ulrich, K., & Eppinger, S.D. (2008). Product Design and Development, 4th ed. New York: McGraw-Hill.
Umeda, Y., Ishii, M., Yoshioka, M., Shimomura, Y., & Tomiyama, T. (1996). Supporting conceptual design based on the function-behavior-state modeler. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 10(4), 275288.
Umeda, Y., & Tomiyama, T. (1997). Functional reasoning in design. IEEE Expert-Intelligent Systems and Their Applications 12(2), 4248. doi:10.1109/64.585103
Vargas-Hernandez, N., & Shah, J.J. (2004). 2nd-CAD: a tool for conceptual systems design in electromechanical domain. Journal of Computing and Information Science in Engineering 4(1), 2836.
Visotsky, D., Patel, A., & Summers, J.D. (2017). Using design requirements for environmental assessment of products: a historical based method. Procedia CIRP 61, 6974.
Vucovich, J., Bhardwaj, N., Hoi-Hei, H., Ramakrishna, M., Thakur, M., & Stone, R. (2006). Concept generation algorithms for repository-based early pesign. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2006-99466. Philadelphia, PA: ASME.
Zhang, G., Patuwo, B.E., & Hu, M.Y. (1998). Forecasting with artificial neural network: the state of the art. International Journal of Forecasting 14(1), 3562.

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