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Data-informed inverse design by product usage information: a review, framework and outlook

  • Liang Hou
  • Roger J. JiaoEmail author
Article
  • 90 Downloads

Abstract

A significant body of knowledge exists on inverse problems and extensive research has been conducted on data-driven design in the past decade. This paper provides a comprehensive review of the state-of-the-art methods and practice reported in the literature dealing with many different aspects of data-informed inverse design. By reviewing the origins and common practice of inverse problems in engineering design, the paper presents a closed-loop decision framework of product usage data-informed inverse design. Specifically reviewed areas of focus include data-informed inverse requirement analysis by user generated content, data-informed inverse conceptual design for product innovation, data-informed inverse embodiment design for product families and product platforming, data-informed inverse analysis and optimization in detailed design, along with prevailing techniques for product usage data collection and analytics. The paper also discusses the challenges of data-informed inverse design and the prospects for future research.

Keywords

Inverse design Product usage information Data-informed design Data analytics Cyber-physical systems 

Notes

References

  1. Adagha, O., Levy, R. M., & Carpendale, S. (2017). Towards a product design assessment of visual analytics in decision support applications: A systematic review. Journal of Intelligent Manufacturing, 28(7), 1623–1633.CrossRefGoogle Scholar
  2. Agard, B., & Kusiak, A. (2004). Data-mining-based methodology for the design of product families. International Journal of Production Research, 42(15), 2955–2969.CrossRefGoogle Scholar
  3. Alam, M. H., & Lee, S. K. (2012). Semantic aspect discovery for online reviews. In ICDM’12 (pp. 816–821). Belgium: Brussels.Google Scholar
  4. Apte, C, Weiss, S., Grout, G., & Gordon Grout, W. (1999).Predicting defects in disk drive manufacturing: A case study in high-dimensional classification. In Proceedings of 9th IEEE conference on artificial intelligence for applications (pp. 212–218).Google Scholar
  5. Arrighi, P. A., Le Masson, P., & Weil, B. (2015). Addressing constraints creatively: how new design software helps solve the dilemma of originality and feasibility. Creativity and Innovation Management, 24(2), 247–260.CrossRefGoogle Scholar
  6. Arrighi, P. A., & Mougenot, C. (2019). Towards user empowerment in product design a mixed reality tool for interactive virtual prototyping. Journal of Intelligent Manufacturing.  https://doi.org/10.1007/s10845-016-1276-0.CrossRefGoogle Scholar
  7. Asuaje, M., Bakir, F., Kouidri, S., & Rey, R. (2004). Inverse design method for centrifugal impellers and comparison with numerical simulation tools. International Journal of Computational Fluid Dynamics, 18(2), 101–110.CrossRefGoogle Scholar
  8. Aswani, A., Shen, Z.-J. M., & Siddiq, A. (2019). Inverse optimization with noisy data. Operations Research, 66(3), 870–892.CrossRefGoogle Scholar
  9. Banks, H. T., & Bihari, K. L. (2001). Modelling and estimating uncertainty in parameter estimation. Inverse Problems, 17(1), 95.CrossRefGoogle Scholar
  10. Bayazit, N. (2004). Investigating design: A review of forty years of design research, Massachusetts Institute of Technology. Design Issues, 20(1), 16–29.CrossRefGoogle Scholar
  11. Bertsimas, D., Gupta, V., & Paschalidis, I. C. (2015). Data-driven estimation in equilibrium using inverse optimization. Mathematical Programming, 153(2), 595–633.CrossRefGoogle Scholar
  12. Bhagat, S., Goyal, A., & Lakshmanan, L. V. S. (2012). Maximizing product adoption in social networks. In Proceedings of the fifth ACM international conference on web search and data mining (pp. 603–612), ACM, Seattle, Washington.Google Scholar
  13. Bonaiuti, D., & Zangeneh, M. (2009). On the coupling of inverse design and optimization techniques for the multiobjective, multipoint design of turbomachinery blades. Journal of Turbomachinery, 131(2), 021014.CrossRefGoogle Scholar
  14. Borges, J. E. (1990). A three-dimensional inverse method for turbomachinery: Part 1—Theory. ASME Journal of Turbomachinery, 11, 346–354.CrossRefGoogle Scholar
  15. Boschetti, F. (2005). Dimensionality reduction and visualization of geoscientific images via locally linear embedding. Computers & Geosciences, 31(6), 689–697.CrossRefGoogle Scholar
  16. Carlson, J., & Murphy, R. R. (2003). Reliability analysis of mobile robots. In IEEE international conference on robotics and automation (pp. 274–281), Taipei, Taiwan.Google Scholar
  17. Cataldi, M., Ballatore, A., Tiddi, I., & Aufaure, M. A. (2013). Good location, terrible food: Detecting feature sentiment in user-generated reviews. Social Network Analysis and Mining, 3(4), 1149–1163.CrossRefGoogle Scholar
  18. Chattopadhyay, P., Mondal, S., Bhattacharya, C., Mukhopadhyay, A., & Ray, A. (2017). Dynamic data-driven design of lean premixed combustors for thermoacoustically stable operations. ASME Journal of Mechanical Design, 139(11), 111419.CrossRefGoogle Scholar
  19. Chen, M.-C. (2010). Configuration of cellular manufacturing systems using association rule induction. International Journal of Production Research, 41(2), 381–395.CrossRefGoogle Scholar
  20. Chen, W., Hoyle, C., & Wassenaar, H. (2013). A choice modeling approach for usage context-based design, decision-based design (pp. 255–285). London: Springer.Google Scholar
  21. Chen, L. H., & Ko, W. C. (2009). Fuzzy linear programming models for new product design using QFD with FMEA. Applied Mathematical Modelling, 33(2), 633–647.CrossRefGoogle Scholar
  22. Chen, L., & Qi, L. (2011). Social opinion mining for supporting buyers’ complex decision making: Exploratory user study and algorithm comparison. Social Network Analysis and Mining, 1(4), 301–320.CrossRefGoogle Scholar
  23. Chen, V. C. P., Tsui, K.-L., Barton, R. R., & Meckesheimer, M. (2006). A review on design, modeling and applications of computer experiments. IIE Transactions, 38(4), 273–291.CrossRefGoogle Scholar
  24. Cheng, J.-W., Chao, T., Chang, L., & Huang, B. (2004). A model-based virtual sensing approach for the injection molding process. Polymer Engineering & Science, 44(9), 1605–1614.CrossRefGoogle Scholar
  25. Chien, C.-F., Kerh, R., Lin, K.-Y., & Yu, A. P.-I. (2016). Data-driven innovation to capture user-experience product design: An empirical study for notebook visual aesthetics design. Computers & Industrial Engineering, 99, 162–173.CrossRefGoogle Scholar
  26. Chock, J. M. K., & Kapania, R. K. (2003). Load updating for finite element models. AIAA Journal., 41(9), 1667–1673.CrossRefGoogle Scholar
  27. CID. (2014). Center for inverse design. http://www.centerforinversedesign.org/. Accessed 16 May 2018.
  28. Colaço, M. J., & Orlande, H. R. B. (2009). Special issue on inverse problems, design and optimization (IPDO 2007) symposium. Inverse Problems in Science and Engineering, 17(1), 1.CrossRefGoogle Scholar
  29. Dambrosio, L., Pascazio, G., & Semeraro, S. (2008). Aerodynamic inverse design using fuzzy logic. Inverse Problems in Science and Engineering, 16(2), 249–268.CrossRefGoogle Scholar
  30. Daun, K. J., Howell, J. R., & Morton, D. P. (2003). Design of radiant enclosures using inverse and non-linear programming techniques. Inverse Problems in Engineering, 11(6), 541–560.CrossRefGoogle Scholar
  31. Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on world wide web. ACM: Budapest, Hungary.Google Scholar
  32. Demeulenaere, A., & Braembussche, R. (1998). Three-dimensional inverse method for turbomachinery blading design. Journal of Turbomachinery, 120(2), 247.CrossRefGoogle Scholar
  33. Dering, M. L., & Tucker, C. S. (2017). A convolutional neural network model for predicting a product’s function, given its form. ASME Journal of Mechanical Design, 139(11), 111408.CrossRefGoogle Scholar
  34. Di Barba, P., Dolezel, I., Karban, P., Kus, P., Mach, F., Mognaschi, M. E., et al. (2014). Multiphysics field analysis and multiobjective design optimization: A benchmark problem. Inverse Problems in Science and Engineering, 22(7), 1214–1225.CrossRefGoogle Scholar
  35. Ding, X., & Liu, B. (2007). The utility of linguistic rules in opinion mining. In SIGIR’07, Amsterdam, The Netherlands.Google Scholar
  36. Du, Y., Yu, Z., Yang, B., & Wang, Y. (2019). Modeling and simulation of time and value throughputs of data-aware workflow processes. Journal of Intelligent Manufacturing.  https://doi.org/10.1007/s10845-018-1394-y.CrossRefGoogle Scholar
  37. Egorov, I. N., Kretinin, G. V., Leshchenko, I. A., & Kuptzov, S. V. (2007). Multi-objective approach for robust design optimization problems. Inverse Problems in Science and Engineering, 15(1), 47–59.CrossRefGoogle Scholar
  38. Esfahani, P. M., Shafieezadeh-Abadeh, S., Hanasusanto, G. A., & Kuhn, D. (2018). Data-driven inverse optimization with incomplete information. Mathematical Programming, 167(1), 191–234.CrossRefGoogle Scholar
  39. Fainekos, E. G., & Giannakoglou, K. C. (2003). Inverse design of airfoils based on a novel formulation of the ant colony optimization method. Inverse Problems in Engineering, 11(1), 21–38.CrossRefGoogle Scholar
  40. Fang, X., Hu, P. J.-H., Li, Z., & Tsai, W. (2013). Predicting adoption probabilities in social networks. Information Systems Research, 24(1), 128–145.CrossRefGoogle Scholar
  41. Fang, K.-T., Li, R., & Sudjianto, A. (2005). Design and modeling for computer experiments. Computer science & data analysis series. Boca Raton: Chapman and Hall/CRC. ISBN 9781584885467.Google Scholar
  42. Fernández-Martínez, J. L., Mukerji, T., Gonzalo, E., & Fernández-Muñiz, Z. (2011). Uncertainty assessment for inverse problems in high dimensional spaces using particle swarm optimization and model reduction techniques. Mathematical and Computer Modelling, 54, 2889–2899.CrossRefGoogle Scholar
  43. Ferrise, F., Graziosi, S., & Bordegoni, M. (2017). Prototyping strategies for multisensory product experience engineering. Journal of Intelligent Manufacturing, 28(7), 1695–1707.CrossRefGoogle Scholar
  44. Gargama, H., & Chaturvedi, S. K. (2011). Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Transactions on Reliability, 60(1), 102–110.CrossRefGoogle Scholar
  45. Gavrus, A., Massoni, E., & Chenot, J. L. (1996). An inverse analysis using a finite element model for identification of rheological parameters. Journal of Materials Processing Technology, 60(1–4), 447–454.CrossRefGoogle Scholar
  46. Gelin, J. C., & Ghouati, O. (1994). An inverse method for determining viscoplastic properties of aluminium alloys. Journal of Materials Processing Technology, 45(1–4), 435–440.CrossRefGoogle Scholar
  47. Gengembre, E., Ladevie, B., Fudym, O., & Thuillier, A. (2012). A Kriging constrained efficient global optimization approach applied to low-energy building design problems. Inverse Problems in Science and Engineering, 20(7), 1101–1114.CrossRefGoogle Scholar
  48. Ghosh, D. D., Olewnik, A., & Lewis, K. (2016). Product “in-use” context identification using feature learning methods. In ASME international design engineering technical conferences and computers and information in engineering conference, Volume 1B: V01BT02A020, DETC2016-59645.Google Scholar
  49. Ghosh, D., Olewnik, A., & Lewis, K. (2017). Application of feature-learning methods toward product usage context identification and comfort prediction. Journal of Computing and Information Science in Engineering, 18(1), 011004.CrossRefGoogle Scholar
  50. Giannakoglou, K. C., Giotis, A., & Karakasis, M. K. (2001). Low-cost genetic optimization based on inexact pre-evaluations and the sensitivity analysis of design parameters. Inverse Problems in Engineering, 9(4), 389–412.CrossRefGoogle Scholar
  51. Giassi, A., Pediroda, V., Poloni, C., & Clarich, A. (2003). Three-dimensional inverse design of axial compressor stator blade using neural-networks and direct Navier–Stokes solver. Inverse Problems in Engineering, 11(6), 457–470.CrossRefGoogle Scholar
  52. Giess, M. D., Culley, S. J., & Shepherd, A. (2002). Informing design using data mining methods. In ASME international design engineering technical conferences and computers and information in engineering conference (pp. 207–215), Montreal, Canada.Google Scholar
  53. Goto, A., Nohmi, M., & Sakurai, T. (2002). Hydrodynamic design system for pumps based on 3-D CAD, CFD, and inverse design method. Journal of Fluids Engineering-Transactions of the ASME, 124(2), 329–335.CrossRefGoogle Scholar
  54. Goto, A., & Zangeneh, M. (2002). Hydrodynamic design of pump diffuser using inverse design method and CFD. Journal of Fluids Engineering-Transactions of the ASME, 124(2), 319–329.CrossRefGoogle Scholar
  55. Green, M. G., Palani, R. P. K., & Wood, K. L. (2004). Product usage context: improving customer needs gathering and design target setting. In ASME design engineering technical conference, DETC/DTM2004-57498.Google Scholar
  56. Green, M. G., Tan, J., Linsey, J. S., Seepersad, C. C., & Wood, K. L. (2005). Effects of product usage context on consumer product preferences. In ASME IDETC/CIE conference, DETC2005-85438.Google Scholar
  57. Guimarães, F. G., & Ramírez, J. A. (2006). Improving the design of clustered neural fuzzy models for optimization. Inverse Problems in Science and Engineering, 14(6), 609–621.CrossRefGoogle Scholar
  58. Gupta, R. K., Belkadi, F., Buergy, C., Bitte, F., Da Cunha, C., Buergin, J., et al. (2018). Gathering, evaluating and managing customers’ feedback during aircraft production. Computers & Industrial Engineering, 115, 559–572.CrossRefGoogle Scholar
  59. Gusel, L., & Brezocnik, M. (2006). Modeling of impact toughness of cold formed material by genetic programming. Computational Materials Science, 37(4), 476–482.CrossRefGoogle Scholar
  60. Hacioglu, A., & Ozkol, I. (2005). Inverse airfoil design by an accelerated genetic algorithm via distribution strategies. Inverse Problems in Science and Engineering, 13(6), 563–579.CrossRefGoogle Scholar
  61. Harutunian, V., Morales, J. C., & Howell, J. R. (1995). Radiation exchange within an enclosure of diffusegray surfaces: the inverse problem. In Proceedings of the ASME/AIChE national heat transfer conference, Portland, Oregon.Google Scholar
  62. Hashash, Y. M. A., Song, H., Jung, S., & Ghaboussi, J. (2009). Extracting inelastic metal behaviour through inverse analysis: a shift in focus from material models to material behavior. Inverse Problems in Science and Engineering, 17(1), 35–50.CrossRefGoogle Scholar
  63. He, L., Chen, W., & Conzelmann, G. (2012a). Impact of vehicle usage on consumer choice of hybrid electric vehicles. Transportation Research Part D: Transport and Environment, 17(3), 208–214.CrossRefGoogle Scholar
  64. He, L., Chen, W., Hoyle, C., & Yannou, B. (2012b). Choice Modeling for usage context-based design. ASME Journal of Mechanical Design, 134(3), 031007-1.CrossRefGoogle Scholar
  65. He, L., & Shan, P. (2012). Three-dimensional aerodynamic optimization for axial-flow compressors based on the inverse design and the aerodynamic parameters. Journal of Turbomachinery - Transactions of the ASME, 134(3), 031004.CrossRefGoogle Scholar
  66. He, L., Wang, M., Chen, W., & Conzelmann, G. (2014). Incorporating social impact on new product adoption in choice modeling: A case study in green vehicles. Transportation Research Part D: Transport and Environment, 32, 421–434.CrossRefGoogle Scholar
  67. Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In KDD’04 (pp. 168–177), Seattle, WA.Google Scholar
  68. Hu, X., & Wu, B. (2009). Classification and summarization of pros and cons for customer reviews (pp. 73–76), Milano, Italy.Google Scholar
  69. Huang, C. C., Liang, W. Y., & Yi, S. R. (2017). Cloud-based design for disassembly to create environmentally friendly products. Journal of Intelligent Manufacturing, 28(5), 1203–1218.CrossRefGoogle Scholar
  70. Hyman, P. (2012). Researchers struggle to measure Big Data’s impact. ACM Communications, November 13.Google Scholar
  71. Hyun, K. H., Lee, J. H., & Kim, M. (2017). The gap between design intent and user response identifying typical and novel car design elements among car brands for evaluating visual significance. Journal of Intelligent Manufacturing, 28(7), 1729–1741.CrossRefGoogle Scholar
  72. Igba, J., Alemzadeh, K., Gibbons, P. M., & Henningsen, K. (2015a). A framework for optimising product performance through feedback and reuse of in-service experience. Robotics and Computer-Integrated Manufacturing, 36, 2–12.CrossRefGoogle Scholar
  73. Igba, J., Alemzadeh, K., & Henningsen, K. (2015b). Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends. Renewable and Sustainable Energy Reviews, 50(October), 144–159.CrossRefGoogle Scholar
  74. Igusa, T., Liu, H., Schafer, B., & Naiman, D. Q. (2003). Bayesian classification trees and clustering for rapid generation and selection of design alternatives. In Proceedings of NSF design, service and manufacturing grantees and research conference, Birmingham, AL.Google Scholar
  75. Isermann, R. (2005). Model-based fault-detection and diagnosis-status and applications. Annual Reviews in Control, 29(1), 71–85.CrossRefGoogle Scholar
  76. Issa, H., Ostrosi, E., Lenczner, M., & Habib, R. (2017). Fuzzy holons for intelligent multi-scale design in cloud-based design for configurations. Journal of Intelligent Manufacturing, 28(5), 1219–1247.CrossRefGoogle Scholar
  77. Jagtap, S., & Johnson, A. (2011). In-service information required by engineering designers. Research in Engineering Design, 22(4), 207–221.CrossRefGoogle Scholar
  78. Jahangirian, A., & Shahrokhi, A. (2009). Inverse design of transonic airfoils using genetic algorithm and a new parametric shape method. Inverse Problems in Science and Engineering, 17(5), 681–699.CrossRefGoogle Scholar
  79. Jeong, S., Obayashi, S., & Nakahashi, K. (1999). Inverse optimization of supersonic wing design with twist specification. Inverse Problems in Engineering, 7(6), 519–535.CrossRefGoogle Scholar
  80. Jiao, R. J. (2011). Prospect of design for mass customization and personalization. In Proceedings of the ASME international design engineering technical conferences & computers and information in engineering conference, DETC2011-48919, Washington, DC.Google Scholar
  81. Jiao, R. J., & Tseng, M. M. (2013). On equilibrium solutions to joint optimization problems in engineering design. CIRP Annals - Manufacturing Technology, 62(1), 155–158.CrossRefGoogle Scholar
  82. Jiao, Y., & Yang, Y. (2019). A product configuration approach based on online data. Journal of Intelligent Manufacturing.  https://doi.org/10.1007/s10845-018-1406-y.CrossRefGoogle Scholar
  83. Jiao, R. J., Zhou, F., & Chu, C. H. (2017). Decision theoretic modeling of affective and cognitive needs for product experience engineering: key issues and a conceptual framework. Journal of Intelligent Manufacturing, 28(7), 1755–1767.CrossRefGoogle Scholar
  84. Jiao, R. J., Zhou, F., Du, J. (2016). Key issues of incorporating social network effects in product portfolio planning. In IEEE international conference on industrial engineering and engineering management (pp. 1898–1902), Indonesia.Google Scholar
  85. Jin, J., Liu, Y., Ji, P., & Kwong, C. K. (2018). Review on recent advances of information mining from big consumer opinion data for product design. ASME Journal of Computing and Information Science in Engineering.  https://doi.org/10.1115/1.4041087.CrossRefGoogle Scholar
  86. Jin, J., Liu, Y., Ji, P., & Liu, H. (2016). Understanding big consumer opinion data for market-driven product design. International Journal of Production Research, 54(10), 3019–3041.CrossRefGoogle Scholar
  87. Jindal, N., & Liu, B. (2006). Mining comparative sentences and relations. In Proceedings of the 21st national conference on Artificial intelligence (Vol. 2). AAAI Press: Boston, Massachusetts.Google Scholar
  88. Jo, Y., & Oh, A. H. (2011). Aspect and sentiment unification model for online review analysis. In WSDM’11 (pp. 815–824), Hong Kong.Google Scholar
  89. Kai, G., Saha, B., & Saxena, A. (2008). A comparison of three data-driven techniques for prognostics. In The 62nd meeting of the society for machinery failure prevention technology (pp. 119–131).Google Scholar
  90. Kamath, C. (2012). Final report: MINDES—data mining for inverse design, LLNL-TR-583076, Lawrence Livermore National Laboratory.Google Scholar
  91. Kannan, K., Goyal, M., & Jacob, G. T. (2013). Modeling the impact of review dynamics on utility value of a product. Social Network Analysis and Mining, 3(3), 401–418.CrossRefGoogle Scholar
  92. Keshavarz, A., Wang, Y., & Boyd, S. (2011). Imputing a convex objective function. In IEEE international symposium on intelligent control (pp. 613–619).Google Scholar
  93. Kim, P., & Ding, Y. (2005). Optimal engineering system design guided by data-mining methods. Technometrics, 47(3), 336–348.CrossRefGoogle Scholar
  94. Kim, H., Liu, Y., Wang, C. L., & Wang, Y. (2017). Special issue: Data-driven design (D3). ASME Journal of Mechanical Design, 139(11), 110301–110301-3.CrossRefGoogle Scholar
  95. Kim, J. S., & Park, W. G. (2000). Optimized inverse design method for pump impeller. Mechanics Research Communications, 27(4), 465–473.CrossRefGoogle Scholar
  96. Kiritsis, D., Bufardi, A., & Xirouchakis, P. (2003). Research issues on product lifecycle management and information tracking using smart embedded systems. Advanced Engineering Informatics, 17(3), 189–202.CrossRefGoogle Scholar
  97. Koen, P. A. (2004). The fuzzy front end for incremental, platform, and breakthrough products. In K. B. Kahn (Ed.), The PDMA handbook of new product development. Hoboken, NJ: Wiley.Google Scholar
  98. Kong, X. T. R., Luo, H., Huang, G. Q., & Yang, X. (2019). Industrial wearable system the human-centric empowering technology in Industry 4.0. Journal of Intelligent Manufacturing.  https://doi.org/10.1007/s10845-018-1416-9.CrossRefGoogle Scholar
  99. Kusiak, A. (2009). Innovation: A data-driven approach. International Journal of Production Economics, 122(1), 440–448.CrossRefGoogle Scholar
  100. Kusiak, A., & Smith, M. (2007). Data mining in design of products and production systems. Annual Reviews in Control, 31(1), 147–156.CrossRefGoogle Scholar
  101. La Torre, D., Kunze, H., Mendivil, F., Galan, M. R., & Zaki, R. (2015). Editorial on inverse problems theory and application to science and engineering 2015. Mathematical Problems in Engineering, 2015, 796094.CrossRefGoogle Scholar
  102. Lee, J., & AbuAli, M. (2011). Innovative Product Advanced Service Systems (I-PASS): Methodology, tools, and applications for dominant service design. International Journal of Advanced Manufacturing Technology, 52(9–12), 1161–1173.CrossRefGoogle Scholar
  103. Lee, K.-Y., Choi, Y.-S., Kim, Y.-L., & Yun, J.-H. (2008). Design of axial fan using inverse design method. Journal of Mechanical Science and Technology, 22(10), 1883–1888.CrossRefGoogle Scholar
  104. Lee, J., & Kao, H.-A. (2014). Dominant innovation design for smart products-service systems (PSS): Strategies and case studies. In Annual SRII global conference (pp. 305–310).Google Scholar
  105. Li, H., Bhowmick, S. S., & Sun, A. (2010). Affinity-driven prediction and ranking of products in online product review sites. In CIKM’10 (pp. 1745–1748), Toronto, ON.Google Scholar
  106. Li, Z., Wang, Y., & Wang, K. (2019a). A data-driven method based on deep belief networks for backlash error prediction in machining centers. Journal of Intelligent Manufacturing.  https://doi.org/10.1007/s10845-017-1380-9.CrossRefGoogle Scholar
  107. Li, Y., Wang, Z., Zhong, X., & Zou, F. (2019b). Identification of influential function modules within complex products and systems based on weighted and directed complex networks. Journal of Intelligent Manufacturing.  https://doi.org/10.1007/s10845-018-1396-9.CrossRefGoogle Scholar
  108. Liang, Z.-Y., Cui, P., & Zhang, G.-B. (2009). An inverse design method for 2D airfoil. Thermophysics and Aeromechanics, 17(1), 51–56.CrossRefGoogle Scholar
  109. Lim, D.-J. (2016). Inverse DEA with frontier changes for new product target setting. European Journal of Operational Research, 254(2), 510–516.CrossRefGoogle Scholar
  110. Lim, J., Choi, S., Lim, C., & Kim, K. (2017). SAO-based semantic mining of patents for semi-automatic construction of a customer job map. Sustainability, 9(8), 1386.CrossRefGoogle Scholar
  111. Lim, C. H., Kim, M. J., Heo, J. Y., & Kim, K. J. (2018). Design of informatics-based services in manufacturing industries: Case studies using large vehicle-related databases. Journal of Intelligent Manufacturing, 29(3), 497–508.CrossRefGoogle Scholar
  112. Lin, C. J., & Cheng, L. Y. (2017). Product attributes and user experience design: how to convey product information through user-centered service. Journal of Intelligent Manufacturing, 28(7), 1743–1754.CrossRefGoogle Scholar
  113. Lin, K.-Y., Chien, C.-F., & Kerh, R. (2016). UNISON framework of data-driven innovation for extracting user experience of product design of wearable devices. Computers & Industrial Engineering, 99, 487–502.CrossRefGoogle Scholar
  114. Lin, C., & He, Y. (2009). Joint sentiment/topic model for sentiment analysis. In CIKM’09 (pp. 375–384), Hong Kong.Google Scholar
  115. Lin, C., He, Y., & Everson, R. (2010). A comparative study of bayesian models for unsupervised sentiment detection. In CONLL’10 (pp. 144–152), Uppsala, Sweden.Google Scholar
  116. Lin, Y., Tang, P., Zhang, W. J., & Yu, Q. (2005). Artificial neural network modelling of driver handling behaviour in a driver-vehicle-environment system. International Journal of Vehicle Design, 37(1), 24–45.CrossRefGoogle Scholar
  117. Lin, Y., Zhang, W. J., & Watson, G. (2003). Using eye movement parameters for evaluating human–machine interface frameworks under normal control operation and fault detection situations. International Journal of Human Computer Studies, 59(6), 837–873.CrossRefGoogle Scholar
  118. Liu, G.-L. (2000). A new generation of inverse shape design problem in aerodynamics and aerothermoelasticity: concepts, theory and methods. International Journal of Aircraft Engineering and Aerospace Technology, 22(4), 334–344.CrossRefGoogle Scholar
  119. Liu, J. (2001a). Optimal experimental designs for linear inverse problems. Inverse Problems in Engineering, 9(3), 287–314.CrossRefGoogle Scholar
  120. Liu, J. (2001b). Optimal experimental designs for linear inverse problems. Inverse Problems in Engineering, 9(3), 287–314.CrossRefGoogle Scholar
  121. Liu, Y., Jiang, C., & Zhao, H. (2018). Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums. Decision Support Systems, 105, 1–12.CrossRefGoogle Scholar
  122. Liu, L., Kuo, S. M., & Zhou, M. C. (2009). Virtual sensing techniques and their applications. In IEEE international conference on networking, sensing and control (pp. 31–36), Okayama, Japan.Google Scholar
  123. Lo, C. H., Chu, C. H., Yanagisawa, H., & Jiao, R. J. (2017). Editorial: Scientific advances in product experience engineering. Journal of Intelligent Manufacturing, 28(7), 1581–1584.CrossRefGoogle Scholar
  124. Lou, S., Feng, Y., Zheng, H., Gao, Y., & Tan, J. (2019). Data-driven customer requirements discernment in the product lifecycle management via intuitionistic fuzzy sets and electroencephalogram. Journal of Intelligent Manufacturing.  https://doi.org/10.1007/s10845-018-1395-x.CrossRefGoogle Scholar
  125. Lützenberger, J., Klein, P., Hribernik, K., & Thoben, K.-D. (2016). Improving product-service systems across life cycle improving product-service systems by exploiting information from the usage phase. A case study. Procedia CIRP, 47(2016), 376–381.CrossRefGoogle Scholar
  126. Ma, H., Chu, X., Lyu, G., & Xue, D. (2017). An integrated approach for design improvement based on analysis of time-dependent product usage data. ASME Journal of Mechanical Design, 139(11), 111401.CrossRefGoogle Scholar
  127. Ma, H. Z., Chu, X. N., Xue, D. Y., & Chen, D. P. (2016). Identification of to-be-improved components for redesign of complex products and systems based on fuzzy QFD and FMEA. Journal of Intelligent Manufacturing, 9, 99.  https://doi.org/10.1007/s10845-016-1269-z.CrossRefGoogle Scholar
  128. Ma, J., & Kim, H. M. (2016). Product family architecture design with predictive, data-driven product family design method. Research in Engineering Design, 27(1), 5–21.CrossRefGoogle Scholar
  129. Maheswari, V. M., Siromoney, A., & Mehata, K. M. (2002). Mining web usage graphs using example search space. International Journal of Computational Intelligence and Applications, 2(2), 209–220.CrossRefGoogle Scholar
  130. Mahnken, R., & Stein, E. (1994). The identification of parameters for visco-plastic models via finite-element methods and gradient methods. Modelling and Simulation in Materials Science and Engineering, 2(3A), 597–616.CrossRefGoogle Scholar
  131. Michopoulos, J. G., & Furukawa, T. (2008). Towards hierarchical design optimization for simultaneous composite material characterization and adjustment of the corresponding physical experiments. Inverse Problems in Science and Engineering, 16(6), 763–775.CrossRefGoogle Scholar
  132. MINDES. (2012). MINDES: Data mining for inverse design project web page. https://computation.llnl.gov/casc/StarSapphire/MINDES.html. Accessed 01 May 2018.
  133. Moro, S., Cortez, P., & Rita, P. (2017). A framework for increasing the value of predictive data-driven models by enriching problem domain characterization with novel features. Neural Computing and Applications, 28, 1515–1523.CrossRefGoogle Scholar
  134. Mota Soares, C. M., Orlande, H. R. B., & Herskovits, J. (2010). Special issue on the international conference on engineering optimization (EngOpt 2008). Inverse Problems in Science and Engineering, 18(4), 437.CrossRefGoogle Scholar
  135. Moura Neto, F. D., & Silva Neto, A. (2013). An introduction to inverse problems with Applications. Berlin: Springer. ISBN 978-3-642-32557-1.Google Scholar
  136. Murray, P. W., Agard, B., & Barajas, M. A. (2017). Market segmentation through data mining: A method to extract behaviors from a noisy data set. Computers & Industrial Engineering, 109, 233–252.CrossRefGoogle Scholar
  137. Neaga, E. I., & Harding, J. A. (2005). An enterprise modeling and integration framework based on knowledge discovery and data mining. International Journal of Production Research, 43(6), 1089–1108.CrossRefGoogle Scholar
  138. Nguyen Tuan, L., Könke, C., Bettzieche, V., & Lahmer, T. (2018). Uncertainty assessment in the results of inverse problems: Applied to damage detection in masonry dams. International Journal of Reliability and Safety, 12, 2–23.CrossRefGoogle Scholar
  139. Nicholson, D. M., Lackey, S. J., Arnold, R., & Scott, K. (2005). Augmented cognition technologies applied to training: A roadmap for the future. In D. D. Schmorrow (Ed.), Foundations of augmented cognition (pp. 931–940). Mahwah, NJ: Erlbaum.Google Scholar
  140. Nikfar, M., Ashrafizadeh, A., & Mayeli, P. (2015). Inverse shape design via a new physical-based iterative solution strategy. Inverse Problems in Science and Engineering, 23(7), 1138–1162.CrossRefGoogle Scholar
  141. Nili-Ahmadabadi, M., Durali, M., Hajilouy-Benisi, A., & Ghadak, F. (2009). Inverse design of 2-D subsonic ducts using flexible string algorithm. Inverse Problems in Science and Engineering, 17(8), 1037–1057.CrossRefGoogle Scholar
  142. Olson, T., Mahajan, S., & Pappas, P. (2016). How to leverage product usage analytics to drive success, PULSE 2016, https://www.gainsight.com/pulse/2016/. Accessed 16 July 2018.
  143. Opresnik, D., Hirsch, M., Zanetti, C., & Taisch, M. (2013). Information—The hidden value of servitization. In Advances in production management systems. Sustainable production and service supply chains (pp. 49–56). Springer.Google Scholar
  144. Padmanabhan, S., Hubner, J. P., Kumar, A. V., & Ifju, P. G. (2006). Load and boundary condition calibration using full-field strain measurement. Experimental Mechanics, 46(5), 569–578.CrossRefGoogle Scholar
  145. Padmanabhan, S., & Kumar, A. V. (2007). Inverse problem for estimation of loads and support compliances from structural response data. AIAA Journal, 45(6), 1199–1207.CrossRefGoogle Scholar
  146. Pahl, G., Beitz, W., Feldhusen, J., & Grote, K.-H. (2007). Engineering design—A systematic approach (3rd ed.). London: Springer.Google Scholar
  147. Partala, T., & Kallinen, A. (2012). Understanding the most satisfying and unsatisfying user experiences: Emotions, psychological needs, and context. Interacting with Computers, 24(1), 25–34.CrossRefGoogle Scholar
  148. Perkins, J. D., Paudel, T. R., Zakutayev, A., Ndione, P. F., Parilla, P. A., Young, D. L., et al. (2011). Inverse design approach to hole doping in ternary oxides: Enhancing p-type conductivity in cobalt oxide spinels. Physical Review B, 84(20), 205207.CrossRefGoogle Scholar
  149. Pierret, S. (1997). Turbomachinery blade design using a Navier–Stokes solver and artificial neural network. VKI Lecture Series, 5.Google Scholar
  150. Polpinij, J., & Ghose, A. K. (2008). An ontology-based sentiment classification methodology for online consumer reviews. In WI-IAT’08 (pp. 518–524), Sydney, Australia.Google Scholar
  151. Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard Business Review, 93(10), 96–114.Google Scholar
  152. Pricop-Jeckstadt, M. (2018). Nonlinear Tikhonov regularization in Hilbert scales with balancing principle tuning parameter in statistical inverse problems. Inverse Problems in Science and Engineering.  https://doi.org/10.1080/17415977.2018.1454918.CrossRefGoogle Scholar
  153. Reinhart, R. F., Shareef, Z., & Steil, J. J. (2017). Hybrid analytical and data-driven modeling for feed-forward robot control. Sensors, 8(17), E311.  https://doi.org/10.3390/s17020311.CrossRefGoogle Scholar
  154. Ruschel, E., Alves Portela Santos, E., & de Freitas Rocha Loures, E. (2018). Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing. Journal of Intelligent Manufacturing.  https://doi.org/10.1007/s10845-018-1434-7.CrossRefGoogle Scholar
  155. Rybak, J. M. (2006). Remote condition monitoring using open-system wireless technologies. Sound and Vibration, 40(2), 16–20.Google Scholar
  156. Schmorrow, D. D., & Fidopiastis, C. M. (2016). Foundations of augmented cognition: Neuroergonomics and operational neuroscience. In The 10th international conference, AC 2016, held as part of HCI International 2016, Toronto, ON, Canada.Google Scholar
  157. Schütz, W., & Schäfer, R. (2001). Bayesian networks for estimating the user’s interests in the context of a configuration task. In Workshop on machine learning for user modeling, Sonthoven, Bavaria, Germany.Google Scholar
  158. Schwabacher, M., Ellman, T., & Hirsh, H. (2001). Learning to set up numerical optimizations of engineering designs. In D. Braha (Ed.), Data mining for design and manufacturing (pp. 87–125). Boston, MA: Kluwer Academic.CrossRefGoogle Scholar
  159. Searls, D., Dishongh, T., & Dujari, P. (2001). A strategy for enabling data driven product decisions through a comprehensive understanding of the usage environment. In The Pacific Rim/ASME international electronic packaging technical conference and exhibition (pp. 8–13), Maui, HI.Google Scholar
  160. Shao, G., Brodsky, A., Shin, S. J., & Kim, D. B. (2017). Decision guidance methodology for sustainable manufacturing using process analytics formalism. Journal of Intelligent Manufacturing, 28(2), 455–472.CrossRefGoogle Scholar
  161. Shin, J. H., Kiritsis, D., & Xirouchakis, P. (2015). Design modification supporting method based on product usage data in closed-loop PLM. International Journal of Computer Integrated Manufacturing, 28(6), 551–568.CrossRefGoogle Scholar
  162. Shkarayev, S., Krashantisa, R., & Tessler, A. (2001). An inverse interpolation method utilizing in-flight strain measurements for determining loads and structural response of aerospace vehicles. In The 3rd international workshop on structural health monitoring, September 12–14, Stanford, California.Google Scholar
  163. Siddhartha, A., & Dagli, C. H. (2013). Augmented cognition in Human–System interaction through coupled action of body sensor network and agent based modeling. Procedia Computer Science, 16, 20–28, ISSN 1877-0509.Google Scholar
  164. Smith Schneider, P., Mossi, A. C., França, F. H. R., De Sousa, F. L., & Silva Neto, A. J. (2009). Application of inverse analysis to illumination design. Inverse Problems in Science and Engineering, 17(6), 737–753.CrossRefGoogle Scholar
  165. Sobieczky, H., Dulikravich, G. S, & Dennis, B. H. (2002). Parameterised geometry formulation for inverse design and optimization. In Proceedings of 4th international conference on inverse problems in engineering, Rio de Janeiro, Brazil.Google Scholar
  166. Soemarwoto, B. I. (1995). Robust inverse shape design in aerodynamics. Inverse Problems in Engineering, 1(2), 153–177.CrossRefGoogle Scholar
  167. Stone, R. B., Tumer, I. Y., & Wie, M. V. (2005). The function-failure design method. ASME Journal of Mechanical Design, 127(3), 397–407.CrossRefGoogle Scholar
  168. Suh, N. P. (2001). Axiomatic design: Advances and applications. Oxford: Oxford University Press.Google Scholar
  169. Sultan, I. A. (2008). Inverse geometric design for a class of rotary positive displacement machines. Inverse Problems in Science and Engineering, 16(2), 127–139.CrossRefGoogle Scholar
  170. Takahashi, S., Obayashi, S., & Nakahashi, K. (1998). Inverse optimization of transonic wing design using multiobjective genetic algorithms. Inverse Problems in Engineering, 6(4), 317–330.CrossRefGoogle Scholar
  171. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., et al. (2018). Digital twin-driven product design framework. International Journal of Production Research.  https://doi.org/10.1080/00207543.2018.1443229.CrossRefGoogle Scholar
  172. Tarantola, A. (2005). Inverse problem theory and methods for model parameter estimation. Philadelphia: Society for Industrial and Applied Mathematics, SIAM. ISBN 978-0-89871-572-9.Google Scholar
  173. Thürer, M., Pan, Y. H., Qu, T., Luo, H., Li, C. D., & Huang, G. Q. (2019). Internet of Things (IoT) driven Kanban system for reverse logistics solid waste collection. Journal of Intelligent Manufacturing, 9, 99.  https://doi.org/10.1007/s10845-016-1278-y.CrossRefGoogle Scholar
  174. Tiow, W. T., & Zangeneh, M. (2002). Application of a three-dimensional viscous transonic inverse method to NASA rotor 67. Proceedings of the Institution of Mechanical Engineers. Part A, Journal of Power and Energy, 216(A3), 243–255.CrossRefGoogle Scholar
  175. Torabi, S. H. R., Alibabaei, S., Bonab, B. B., Sadeghi, M. H., & Faraji, G. (2017). Design and optimization of turbine blade preform forging using RSM and NSGA II. Journal of Intelligent Manufacturing, 28(6), 1409–1419.CrossRefGoogle Scholar
  176. Torra, V. (2003). Trends in information fusion in data mining. In V. Torra (Ed.), Information fusion in data mining, studies in fuzziness and soft computing. Berlin: Springer.CrossRefGoogle Scholar
  177. Tsai, K. M., & Luo, H. J. (2017). An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm. Journal of Intelligent Manufacturing, 28(2), 473–487.CrossRefGoogle Scholar
  178. Tsao, Y. C., & Chen, P. (2017). Design for product experience: a study on the analepsis construction of product use. Journal of Intelligent Manufacturing, 28(7), 1645–1666.CrossRefGoogle Scholar
  179. Tuarob, S., Tucker, C. S., Salathe, M., & Ram, N. (2013). Discovering health-related knowledge in social media using ensembles of heterogeneous features. In Proceedings of the 22nd ACM international conference on conference on information & knowledge management (pp. 1685–1690), ACM, New York.Google Scholar
  180. Tucker, C. S., & Kim, H. M. (2009). Data-driven decision tree classification for product portfolio design optimization. Journal of Computing and Information Science in Engineering, 9(4), 041004.CrossRefGoogle Scholar
  181. Tucker, C., & Kim, H. M. (2011). Predicting emerging product design trend by mining publicity available customer review data. In: ICED’11 (pp. 43–52), Copenhagen, Denmark.Google Scholar
  182. van Horn, D., Olewnik, A., & Lewis, K. (2012). Design analytics: Capturing, understanding, and meeting customer needs using big data, ASME Paper No. DETC2012-71038.Google Scholar
  183. Vichare, N., Rodgers, P., Eveloy, V., & Pecht, M. (2007). Environment and usage monitoring of electronic products for health assessment and product design. Quality Technology & Quantitative Management, 4(2), 235–250.CrossRefGoogle Scholar
  184. Vogel, C. R. (2002). Computational methods for inverse problems. Frontiers in applied mathematics series. Philadelphia: Society for Industrial and Applied Mathematics, SIAM. ISBN 978-0-89871-550-7.Google Scholar
  185. Volpe, E. V., Oliveira, G. L., Santos, L. C. C., Hayashi, M. T., & Ceze, M. A. B. (2009). Inverse aerodynamic design applications using the MGM hybrid formulation. Inverse Problems in Science and Engineering, 17(2), 245–261.CrossRefGoogle Scholar
  186. Wang, L. (2011). Product design selection using online customer reviews. Ph.D. Dissertation, University of Maryland.Google Scholar
  187. Wang, M., & Chen, W. (2015). A data-driven network analysis approach to predicting customer choice sets for choice modeling in engineering design. ASME Journal of Mechanical Design, 137(7), 071410.CrossRefGoogle Scholar
  188. Wang, S., Hou, L., Lee, J., & Bu, X. J. (2017a). Evaluating wheel loader operating conditions based on radar chart. Automation in Construction, 84(Dec), 42–49.CrossRefGoogle Scholar
  189. Wang, F., Li, H., & Liu, A. (2018). A novel method for determining the key customer requirements and innovation goals in customer collaborative product innovation. Journal of Intelligent Manufacturing, 29(1), 211–225.CrossRefGoogle Scholar
  190. Wang, P., Tao, K., Gao, C., Ning, X., Gu, S., & Deng, B. (2017). Eliciting big data requirement from big data itself: A task-directed approach. In IEEE 6th international workshop on software mining (pp. 17–23).Google Scholar
  191. Wang, Y., Yagola, A. G., & Yang, C. (2012). Computational methods for applied inverse problems. de Gruyter/Higher Education Press. ISBN-13: 978-3110259049.Google Scholar
  192. Wang, L., Youn, B. D., Azarm, S., & Kannan, P. K. (2011). Customer-driven product design selection using web based user-generated content. In ASME IDETC’11 (pp. 405–419), Washington, DC.Google Scholar
  193. Wei, Q., Zhang, J., & Zhang, X. (2000). An inverse DEA model for inputs/outputs estimate. European Journal of Operational Research, 121(1), 151–163.CrossRefGoogle Scholar
  194. West, R. M., & Lesnic, D. (2007). Editorial: inverse problems in engineering. In: Selected papers from the 5th international conference on inverse problems in engineering: Theory and practice 2005, measurement science and technology, 18(1).Google Scholar
  195. Wu, D., Zhang, L. L., & Jiao, R. J. (2013). SysML-based design chain information modeling for variety management in production reconfiguration. Journal of Intelligent Manufacturing, 24, 575–596.CrossRefGoogle Scholar
  196. Xu, X., Tan, S., Liu, Y., Cheng, X., & Lin, Z. (2012). Towards jointly extracting aspects and aspect-specific sentiment knowledge. In: CIKM’12 (pp. 1895–1899).Washington, DC.Google Scholar
  197. Yang, C. C., Wong, Y. C., & Wei, C.-P. (2009). Classifying web review opinions for consumer product analysis. In: ICEC’09, pp. 57–63, Taiwan.Google Scholar
  198. Yannou, B., Yvars, P.-A., Hoyle, C., & Chen, W. (2013). Set-based design by simulation of usage scenario coverage. Journal of Engineering Design, 24(8), 575–603.CrossRefGoogle Scholar
  199. Yin, J., Li, J., Wang, D., & Wei, X. (2014). A simple inverse design method for pump turbine. IOP Conference Series Earth and Environmental Science, 22(1), 012030.CrossRefGoogle Scholar
  200. Yin, J., & Wang, D. (2014). Review on applications of 3D inverse design method for pump. Chinese Journal of Mechanical Engineering, 27(3), 520–527.CrossRefGoogle Scholar
  201. Yu, L., Kokenyesi, R. S., Keszler, D. A., & Zunger, A. (2013). Inverse design of high absorption thin-film photovoltaic materials. Advanced Energy Materials, 3(1), 43–48.CrossRefGoogle Scholar
  202. Zagibalov, T., & Carroll, J. (2008). Automatic seed word selection for unsupervised sentiment classification of chinese text. In: COLING’08 (pp. 1073–1080), Manchester, UK.Google Scholar
  203. Zakutayev, A., Zhang, X., Nagaraja, A., Yu, L., Lany, S., Mason, T. O., et al. (2013). Theoretical prediction and experimental realization of new stable V-IX-IV semiconductors using the inverse design approach. Journal of the American Chemical Society, 135(27), 10048–10054.CrossRefGoogle Scholar
  204. Zangeneh, M., Goto, A., & Harada, H. (1998). On the design criteria for suppression of secondary flows in centrifugal and mixed flow impellers. Journal of Turbomachinery-Transactions of the ASME, 120(4), 723–735.CrossRefGoogle Scholar
  205. Zangeneh, M., Goto, A., & Harada, H. (1999). On the role of three-dimensional inverse design methods in turbomachinery shape optimization. Proceedings of the Institution of Mechanical Engineers. Part C, Journal of Mechanical Engineering Science, 213(1), 27–42.CrossRefGoogle Scholar
  206. Zhan, Y., Tan, K. H., Li, Y., & Tse, Y. K. (2016). Unlocking the power of big data in new product development. Annals of Operations Research, 9, 99.  https://doi.org/10.1007/s10479-016-2379-x.CrossRefGoogle Scholar
  207. Zhang, W. J. (1994). An integrated environment for CADCAM of mechanical systems. PhD Thesis, TU Delft, The Netherlands.Google Scholar
  208. Zhang, L., Chu, X., Chen, H., & Xue, D. (2017). Identification of performance requirements for design of smartphones based on analysis of the collected operating data. ASME Journal of Mechanical Design, 139(11), 111418.CrossRefGoogle Scholar
  209. Zhang, J., & Farritor, S. (2004). Using a neural network to determine fitness in genetic design. Inverse Problems in Science and Engineering, 12(6), 629–642.CrossRefGoogle Scholar
  210. Zhang, C., & Ma, Y. (2012). Ensemble machine learning: Methods and applications. Berlin: Springer.CrossRefGoogle Scholar
  211. Zhang, Y., & Pennacchiotti, M. (2013). Predicting purchase behaviors from social media. In: Proceedings of the 22nd international conference on World Wide Web, International World Wide Web Conferences Steering Committee (pp. 1521–1532), Rio de Janeiro, Brazil.Google Scholar
  212. Zhao, H., Icoz, T., Jaluria, Y., & Knight, D. (2007). Application of data-driven design optimization methodology to a multi-objective design optimization problem. Journal of Engineering Design, 18(4), 343–359.CrossRefGoogle Scholar
  213. Zhao, J., Song, J., Montazeri, A., Gupta, M. M., Lin, Y., Wang, C., et al. (2018). Mining affective words to capture customer’s affective response to apparel products. Textile Research Journal, 88(12), 1426–1436.CrossRefGoogle Scholar
  214. Zheng, P., Xu, X., & Chen, C.-H. (2018). A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment. Journal of Intelligent Manufacturing.  https://doi.org/10.1007/s10845-018-1430-y.CrossRefGoogle Scholar
  215. Zhou, F., Jiao, R. J., & Lei, B. (2015a). A linear threshold-hurdle model for product adoption prediction incorporating social network effects. Information Sciences, 307(June), 95–109.CrossRefGoogle Scholar
  216. Zhou, F., Jiao, R. J., & Linsey, J. (2015b). Latent customer needs elicitation by use case analogical reasoning from sentiment analysis of online product reviews. ASME Journal of Mechanical Design, 137(7), 071401.CrossRefGoogle Scholar
  217. Zhou, F., Jiao, R. J., Yang, J. X., & Lei, B. (2017). Augmenting feature model through customer preference mining by hybrid sentiment analysis. Expert Systems with Applications, 89(8), 306–317.CrossRefGoogle Scholar
  218. Zhou, F., Xu, Q., & Jiao, R. J. (2011). Fundamentals of product ecosystem design for user experience. Research in Engineering Design, 22(1), 43–61.CrossRefGoogle Scholar
  219. Zhou, F., Xu, Q., Jiao, R. J., & Helander, M. G. (2013). Emotion prediction from physiological signals: A comparison study between visual and auditory elicitors. Interacting with Computers.  https://doi.org/10.1093/iwc/iwt039.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical and Electrical EngineeringXiamen UniversityXiamenChina
  2. 2.School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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