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Exploratory study on the perception of additively manufactured end-use products with specific questionnaires and eye-tracking

  • Yuri BorgianniEmail author
  • Lorenzo Maccioni
  • Demis Basso
Original Paper
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Abstract

The outreach of application domains for Additive Manufacturing (AM) is expanding and end-use products represent their next frontier. Contextually, design methods are developed for exploiting the unique AM capabilities. They largely benefit from the knowledge about peculiarities, constraints and technical performances of the various AM processes and devices. However, while the mechanical properties of objects created with AM are widely studied, there is lack of research on emotional and perceptual aspects. This is of great relevance in the mentioned perspective of employing AM for end-use products. The paper aims to elucidate which perceptual mechanisms are activated when a user observes an object generated with AM instead of traditional technologies. An experiment has involved 43 participants who have evaluated ten pairs of objects, constituted by a commercial product and a replica made with Fused Deposition Modelling. Testers have answered a questionnaire, as well as their visual behavior has been recorded with eye-tracking glasses. Based on results, replicas suffer from poor attractiveness and especially low perceived quality. They have also given rise to more careful exploratory behaviors because they likely require a lengthier examination for testers’ assessment or they arouse curiosity. It can be inferred that Fused Deposition Modelling does not exhibit sufficient accuracy to achieve acceptability with reference to everyday products. Nevertheless, it is also deemed that limited improvements might compensate for the perception of technical unsuitability this technology engenders. This can be verified by repeating the experiment with more sophisticated and precise AM devices.

Keywords

End-use products Design for additive manufacturing Fused deposition modelling Eye-tracking 

Notes

Acknowledgements

The study is conducted in the frame of the projects AMDAPA and EYE-TRACK funded by the Free University of Bozen-Bolzano with the calls CRC2016 and CRC2017, respectively. The study has benefitted from the equipment of the Mechanical Lab and the Cognitive and Educational Sciences Lab at the Free University of Bozen-Bolzano, including FDM printer, 3D scanner and eye-tracking glasses, as well as other software and hardware. The authors are particularly grateful to the 43 volunteer participants who have contributed to the two-session experiment. Special thanks go to Filippo Nalli for his support in the preparatory part of the study and to Benedikt Mark for helping with the analysis of eye-tracking videos.

References

  1. 1.
    Chiarello, F., Trivelli, L., Bonaccorsi, A., Fantoni, G.: Extracting and mapping industry 4.0 technologies using wikipedia. Comput. Ind. 100, 244–257 (2018)CrossRefGoogle Scholar
  2. 2.
    Huang, Y., Leu, M.C., Mazumder, J., Donmez, A.: Additive manufacturing: current state, future potential, gaps and needs, and recommendations. J. Manuf. Sci. Eng. 137, 014001 (2015)CrossRefGoogle Scholar
  3. 3.
    Booth, J.W., Alperovich, J., Chawla, P., Ma, J., Reid, T.N., Ramani, K.: The design for additive manufacturing worksheet. J. Mech. Des. 139, 100904 (2017)CrossRefGoogle Scholar
  4. 4.
    Beyer, C.: Strategic implications of current trends in additive manufacturing. J. Manuf. Sci. Eng. 136, 064701 (2014)CrossRefGoogle Scholar
  5. 5.
    Guo, N., Leu, M.C.: Additive manufacturing: technology, applications and research needs. Front. Mech. Eng. 8, 215–243 (2013)CrossRefGoogle Scholar
  6. 6.
    Gao, W., Zhang, Y., Ramanujan, D., Ramani, K., Chen, Y., Williams, C.B., Wang, C.C.L., Shin, Y.C., Zhang, S., Zavattieri, P.D.: The status, challenges, and future of additive manufacturing in engineering. Comput. Aided Des. 69, 65–89 (2015)CrossRefGoogle Scholar
  7. 7.
    Campbell, I., Bourell, D., Gibson, I.: Additive manufacturing: rapid prototyping comes of age. Rap. Prototyp. J. 18, 255–258 (2012)CrossRefGoogle Scholar
  8. 8.
    Yoo, B., Ko, H., Chun, S.: Prosumption perspectives on additive manufacturing: reconfiguration of consumer products with 3D printing. Rap. Prototyp. J. 22, 691–705 (2016)CrossRefGoogle Scholar
  9. 9.
    Kudus, S.I.A., Campbell, R.I., Bibb, R.: Customer perceived value for self-designed personalised products made using additive manufacturing. Int. J. Ind. Eng. Manag. 7, 183–193 (2016)Google Scholar
  10. 10.
    Beltrametti, L., Gasparre, A.: Industrial 3D printing in Italy. Int. J. Manuf. Technol. Manag. 32, 43–64 (2018)CrossRefGoogle Scholar
  11. 11.
    Abdelall, E.S., Frank, M.C., Stone, R.T.: A study of design fixation related to additive manufacturing. J. Mech. Des. 140, 041702 (2018)CrossRefGoogle Scholar
  12. 12.
    Klahn, C., Leutenecker, B., Meboldt, M.: Design for additive manufacturing-Supporting the substitution of components in series products. Procedia CIRP 21, 138–143 (2014)CrossRefGoogle Scholar
  13. 13.
    Doubrovski, Z., Verlinden, J.C., Geraedts, J.M.: Optimal design for additive manufacturing: opportunities and challenges. In: ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 635–646. American Society of Mechanical Engineers (2011)Google Scholar
  14. 14.
    Quinlan, H.E., Hasan, T., Jaddou, J., Hart, A.J.: Industrial and consumer uses of additive manufacturing: a discussion of capabilities, trajectories, and challenges. J. Ind. Ecol. 21, S15–S20 (2017)CrossRefGoogle Scholar
  15. 15.
    Avnet, M.S., Elwany, A.: Additive manufacturing of complex products by DSM-based analysis of architectures. In IIE Annual Conference, p. 2872. Institute of Industrial and Systems Engineers (IISE) (2015)Google Scholar
  16. 16.
    Krimi, I., Lafhaj, Z., Ducoulombier, L.: Prospective study on the integration of additive manufacturing to building industry—case of a French construction company. Addit. Manuf. 16, 107–114 (2017)CrossRefGoogle Scholar
  17. 17.
    Wang, Q., Sun, X., Cobb, S., Lawson, G., Sharples, S.: 3D printing system: an innovation for small-scale manufacturing in home settings?–early adopters of 3D printing systems in China. Int. J. Prod. Res. 54, 6017–6032 (2016)CrossRefGoogle Scholar
  18. 18.
    Pradel, P., Zhu, Z., Bibb, R., Moultrie, J.: Investigation of design for additive manufacturing in professional design practice. J. Eng. Des. 29, 165–200 (2018)CrossRefGoogle Scholar
  19. 19.
    Arbeláez, J.C., Osorio-Gómez, G.: Crowdsourcing Augmented Reality Environment (CARE) for aesthetic evaluation of products in conceptual stage. Comput. Ind. 99, 241–252 (2018)CrossRefGoogle Scholar
  20. 20.
    Yang, S., Zhao, Y.F.: Additive manufacturing-enabled design theory and methodology: a critical review. Int. J. Adv. Manuf. Technol. 80, 327–342 (2015)CrossRefGoogle Scholar
  21. 21.
    Fuwen, H., Jiajian, C., Yunhua, H.: Interactive design for additive manufacturing: a creative case of synchronous belt drive. Int. J. Inter. Des. Manuf. (IJIDeM) 12, 889–901 (2018)CrossRefGoogle Scholar
  22. 22.
    Thompson, M.K., Moroni, G., Vaneker, T., Fadel, G., Campbell, R.I., Gibson, I., Bernard, A., Schulz, J., Graf, P., Ahuja, B., Martina, F.: Design for additive manufacturing: trends, opportunities, considerations, and constraints. CIRP Ann. 65, 737–760 (2016)CrossRefGoogle Scholar
  23. 23.
    Wang, Y., Blache, R., Zheng, P., Xu, X.: A Knowledge Management System to Support Design for Additive Manufacturing Using Bayesian Networks. J. Mech. Des. 140, 051701 (2018)CrossRefGoogle Scholar
  24. 24.
    Rohde, J., Jahnke, U., Lindemann, C., Kruse, A., Koch, R.: Standardised product development for technology integration of additive manufacturing. Vir. Phys. Prototyp. 19, 141–147 (2019)CrossRefGoogle Scholar
  25. 25.
    Markou, F., Segonds, F., Rio, M., Perry, N.: A methodological proposal to link Design with Additive Manufacturing to environmental considerations in the Early Design Stages. Int. J. Inter. Des. Manuf. (IJIDeM) 11, 799–812 (2017)CrossRefGoogle Scholar
  26. 26.
    Carfagni, M., Fiorineschi, L., Furferi, R., Governi, L., Rotini, F.: The role of additive technologies in the prototyping issues of design. Rap. Prototyp. J. 24, 1101–1116 (2018)CrossRefGoogle Scholar
  27. 27.
    Borgianni, Y., Rauch, E., Maccioni, L., Mark, B.G.: User experience analysis in industry 4.0-the use of biometric devices in engineering design and manufacturing. In: 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 192–196 (2018)Google Scholar
  28. 28.
    Park, J., DeLong, M.: User perceptions of technology adoption and implementation: a case study of footwear production in a global market. Fas. Pract. 1, 87–108 (2009)CrossRefGoogle Scholar
  29. 29.
    Medola, F.O., Fortulan, C.A., Purquerio, B.D.M., Elui, V.M.C.: A new design for an old concept of wheelchair pushrim. Disabil. Rehabil. Assist. Technol. 7, 234–241 (2012)CrossRefGoogle Scholar
  30. 30.
    Di Franco, P.D.G., Camporesi, C., Galeazzi, F., Kallmann, M.: 3d printing and immersive visualization for improved perception of ancient artifacts. PRESENCE: Teleoper. Virt. Environ. 24, 243–264 (2015)CrossRefGoogle Scholar
  31. 31.
    Ramírez, E.R.R., Voerman, S., Andreae, H.: Designed for delight: surprising visual-tactile experiences using 3D printing in lighting design. In: Proceedings of the Conference on Design and Semantics of Form and Movement-Sense and Sensitivity, DeSForM 2017. InTech (2017)Google Scholar
  32. 32.
    Rias, A.L., Bouchard, C., Segonds, F., Vayre, B., Abed, S.: Design for additive manufacturing: supporting intrinsic-motivated creativity. Emot. Eng. 5, 99–116 (2017)Google Scholar
  33. 33.
    Cao, H., Scudder, C., Howard, C., Piro, K., Tattersall, H., Frett, J.: Locally produced textiles: product development and evaluation of consumers’ acceptance. Int. J. Fash. Des. Technol. Educ. 7, 189–197 (2014)CrossRefGoogle Scholar
  34. 34.
    Becattini, N., Borgianni, Y., Cascini, G., Rotini, F.: What surprised you? A questionnaire to map unexpectedness through FBS variables. In: 4th International Conference on Design Creativity, pp. 1–10 (2016)Google Scholar
  35. 35.
    Perez Mata, M., Ahmed-Kristensen, S., Brockhoff, P.B., Yanagisawa, H.: Investigating the influence of product perception and geometric features. Res. Eng. Design 28, 357–379 (2017)CrossRefGoogle Scholar
  36. 36.
    Chen, H.Y., Chang, H.C.: Extraction of potential dimensions for consumers’ psychological perceptions regarding perfume bottle form. J. Des. Res. 16, 47–63 (2018)Google Scholar
  37. 37.
    Schmitt, R., Köhler, M., Durá, J.V., Pineda, J.A.D.: Objectifying user attention and emotion evoked by relevant perceived product components. J. Sens. Sens. Syst. 3, 315–324 (2014)CrossRefGoogle Scholar
  38. 38.
    Ueda, K.: Cognitive mechanism in selecting new products: a cognitive neuroscience perspective. Emot. Eng. 5, 31–41 (2017)Google Scholar
  39. 39.
    Meiselman, H.L.: Emotion Measurement. Elsevier, Amsterdam (2016)CrossRefGoogle Scholar
  40. 40.
    Laurans, G., Desmet, P.M., Hekkert, P.: Assessing emotion in human-product interaction: an overview of available methods and a new approach. Int. J. Prod. Dev. 16, 225–242 (2012)CrossRefGoogle Scholar
  41. 41.
    Evans, J.R., Mathur, A.: The value of online surveys: a look back and a look ahead. Int. Res. 28, 854–887 (2018)Google Scholar
  42. 42.
    Sylcott, B., Cagan, J., Tabibnia, G.: Understanding consumer tradeoffs between form and function through metaconjoint and cognitive neuroscience analyses. J. Mech. Des. 135, 101002 (2013)CrossRefGoogle Scholar
  43. 43.
    Yılmaz, B., Korkmaz, S., Arslan, D.B., Güngör, E., Asyalı, M.: H: like/dislike analysis using EEG: determination of most discriminative channels and frequencies. Comput. Methods Programs Biomed. 113, 705–713 (2014)CrossRefGoogle Scholar
  44. 44.
    Telpaz, A., Webb, R., Levy, D.J.: Using EEG to predict consumers’ future choices. J. Mark. Res. 52, 511–529 (2015)CrossRefGoogle Scholar
  45. 45.
    Goucher-Lambert, K., Moss, J., Cagan, J.: Inside the mind: using neuroimaging to understand moral product preference judgments involving sustainability. J. Mech. Des. 139, 041103 (2017)CrossRefGoogle Scholar
  46. 46.
    Desmet, P.M.: Design for mood: twenty activity-based opportunities to design for mood regulation. Int. J. Des. 9, 2015 (2015)Google Scholar
  47. 47.
    Lee, N., Broderick, A.J., Chamberlain, L.: What is ‘neuromarketing’? A discussion and agenda for future research. Int. J. Psychophysiol. 63, 199–204 (2007)CrossRefGoogle Scholar
  48. 48.
    dos Santos, R.D.O.J., de Oliveira, J.H.C., Rocha, J.B., Giraldi, J.D.M.E.: Eye tracking in neuromarketing: a research agenda for marketing studies. Int. J. Psychol. Stud. 7, 32 (2015)CrossRefGoogle Scholar
  49. 49.
    Lohmeyer, Q., Meboldt, M.: The integration of quantitative biometric measures and experimental design research. In: Experimental Design Research, pp. 97–112. Springer, Cham (2016)Google Scholar
  50. 50.
    Duchowski, A.T.: Eye tracking methodology. Theory and Practice. Springer, Berlin (2007)zbMATHGoogle Scholar
  51. 51.
    Carbon, C.C., Hutzler, F., Minge, M.: Innovativeness in design investigated by eye movements and pupillometry. Psychol. Sci. 48, 173 (2006)Google Scholar
  52. 52.
    Khalighy, S., Green, G., Scheepers, C., Whittet, C.: Quantifying the qualities of aesthetics in product design using eye-tracking technology. Int. J. Ind. Ergon. 49, 31–43 (2015)CrossRefGoogle Scholar
  53. 53.
    Ho, C.H., Lu, Y.N.: Can pupil size be measured to assess design products? Int. J. Ind. Ergon. 44, 436–441 (2014)CrossRefGoogle Scholar
  54. 54.
    Guo, F., Ding, Y., Liu, W., Liu, C., Zhang, X.: Can eye-tracking data be measured to assess product design?: visual attention mechanism should be considered. Int. J. Ind. Ergon. 53, 229–235 (2016)CrossRefGoogle Scholar
  55. 55.
    Du, P., MacDonald, E.F.: Eye-tracking data predict importance of product features and saliency of size change. J. Mech. Des. 136, 081005 (2014)CrossRefGoogle Scholar
  56. 56.
    She, J., MacDonald, E.F.: Exploring the effects of a product’s sustainability triggers on pro-environmental decision-making. J. Mech. Des. 140, 011102 (2018)CrossRefGoogle Scholar
  57. 57.
    Seshadri, P., Bi, Y., Bhatia, J., Simons, R., Hartley, J., Reid, T.: Evaluations that matter: customer preferences using industry-based evaluations and eye-gaze data. In: ASME 2016 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers (2016)Google Scholar
  58. 58.
    Piqueras-Fiszman, B., Velasco, C., Salgado-Montejo, A., Spence, C.: Using combined eye tracking and word association in order to assess novel packaging solutions: a case study involving jam jars. Food Qual. Prefer. 28, 328–338 (2013)CrossRefGoogle Scholar
  59. 59.
    Bin, Q., Suihuai, Y., Weiping, H.: Product cognitive style based on Kansei engineering and visual track experiments. J. Appl. Sci. 13, 2341–2345 (2013)CrossRefGoogle Scholar
  60. 60.
    Khushaba, R.N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B.E., Townsend, C.: Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst. Appl. 40, 3803–3812 (2013)CrossRefGoogle Scholar
  61. 61.
    Mussgnug, M., Lohmeyer, Q., Meboldt, M.: Raising designers’ awareness of user experience by mobile eye tracking records. In: DS 78: Proceedings of the 16th International conference on Engineering and Product Design Education (E&PDE14), Design Education and Human Technology Relations (2014)Google Scholar
  62. 62.
    Abraham-Murali, L., Littrell, M.A.: Consumers’ perceptions of apparel quality over time: an exploratory study. Cloth. Text. Res. J. 13, 149–158 (1995)CrossRefGoogle Scholar
  63. 63.
    Artacho-Ramirez, M.A., Diego-Mas, J.A., Alcaide-Marzal, J.: Influence of the mode of graphical representation on the perception of product aesthetic and emotional features: an exploratory study. Int. J. Ind. Ergon. 38, 942–952 (2008)CrossRefGoogle Scholar
  64. 64.
    Regina, F., Lavecchia, F., Galantucci, L.M.: Preliminary study for a full colour low cost open source 3D printer, based on the combination of fused deposition modelling (FDM) or fused filament fabrication (FFF) and inkjet printing. Int. J. Interact. Des. Manuf. (IJIDeM). 12, 979–993 (2018)CrossRefGoogle Scholar
  65. 65.
    Gibson, I., Rosen, D., Stucker, B.: Additive Manufacturing–3D Printing, Rapid Prototyping, and Direct Digital Manufacturing. Springer, Berlin (2015)Google Scholar
  66. 66.
    Hegdé, J.: Time course of visual perception: coarse-to-fine processing and beyond. Prog. Neurobiol. 84, 405–439 (2008)CrossRefGoogle Scholar
  67. 67.
    Camargo, F.R., Henson, B.: Beyond usability: designing for consumers’ product experience using the Rasch model. J. Eng. Des. 26, 121–139 (2015)CrossRefGoogle Scholar
  68. 68.
    McMullen, P.A., Jolicoeur, P.: The spatial frame of reference in object naming and discrimination of left-right reflections. Mem. Cogn. 18, 99–115 (1990)CrossRefGoogle Scholar
  69. 69.
    Mawad, F., Trías, M., Giménez, A., Maiche, A., Ares, G.: Influence of cognitive style on information processing and selection of yogurt labels: insights from an eye-tracking study. Food Res. Int. 74, 1–9 (2015)CrossRefGoogle Scholar
  70. 70.
    Meyerding, S.G.: Combining eye-tracking and choice-based conjoint analysis in a bottom-up experiment. J. Neurosci. Psychol. Econ. 11, 28 (2018)CrossRefGoogle Scholar
  71. 71.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics. 33, 159–174 (1977)CrossRefzbMATHGoogle Scholar
  72. 72.
    Pinheiro, J. C., Bates, D. M.: Linear mixed-effects models: basic concepts and examples. In: Mixed-effects models in S and S-Plus, pp. 3–56 (2000)Google Scholar
  73. 73.
    Bates, D., Maechler, M., Bolker, B., Walker, S.: lme4: linear mixed-effects models using Eigen and S4. R package version. 1, 1–23 (2014)Google Scholar
  74. 74.
    Basso, D., Bisiacchi, P.S., Cotelli, M., Farinello, C.: Planning times during Travelling Salesman’s problem: differences between closed head injury and normal subjects. Brain Cogn. 46, 38–42 (2001)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Faculty of Science and TechnologyFree University of Bozen-BolzanoBolzanoItaly
  2. 2.Faculty of EducationFree University of Bozen-BolzanoBrixenItaly
  3. 3.Centro de Investigación en Neuropsicología y Neurociencias CognitivasUniversidad Catolica del MauleTalcaChile

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