Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T21:44:26.519Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of a multi-user requirements axiomatic design decision support tool for manufacturing process selection

Published online by Cambridge University Press:  16 May 2024

Edward Abela ,
Philip Farrugia ,
Pierre Vella ,
Glenn Cassar  and
Maria Victoria Gauci
Edward Abela*
Affiliation:
University of Malta, Malta
Philip Farrugia
Affiliation:
University of Malta, Malta
Pierre Vella
Affiliation:
University of Malta, Malta
Glenn Cassar
Affiliation:
University of Malta, Malta
Maria Victoria Gauci
Affiliation:
University of Malta, Malta
*
edward.abela@um.edu.mt

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Manufacturing process selection presents numerous challenges to designers, including product complexity, consideration of production volumes and part tolerances. This paper introduces a design support tool based on the axiomatic design model to systematically transform requirements into functions and technological capabilities. The results from an evaluation of the implemented prototype tool in the field of medical device design demonstrates its usefulness in selecting the most suitable candidate manufacturing process for a given artifact, while taking into account multiple user requirements.

Keywords

early design phaseaxiomatic designrequirements management
Type
Design Methods and Tools
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 483 - 492
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Abela, E., Farrugia, P., Gauci, M., Vella, P., Cassar, G., & Balzan, E. (2022). A Novel User-Centred Framework for the Holistic Design of Therapeutic Medical Devices. Proceedings of the Design Society, 2, 1199–1208. https://doi.org/10.1017/pds.2022.122CrossRefGoogle Scholar
Abela, E., Vella, P., Farrugia, P., Cassar, G., Gauci, M. V., & Balzan, E. (2023). An axiomatic design methodology for manufacturing process selection based on multi-user requirements mapping. https://doi.org/10.14733/cadaps.2023.S6.62-74CrossRefGoogle Scholar
Agard, B., & Kusiak, A. (2005). Data Mining for Selection of Manufacturing Processes. Data Mining and Knowledge Discovery Handbook (pp. 1159–1166), https://doi.org/10.1007/0-387-25465-X_54CrossRefGoogle Scholar
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. https://www.researchgate.net/publication/235356393CrossRefGoogle Scholar
Byun, H. S., & Lee, K. H. (2005). A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method. The International Journal of Advanced Manufacturing Technology, 26(11), 1338–1347. https://doi.org/10.1007/s00170-004-2099-2CrossRefGoogle Scholar
Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions (pp. xv, 403). Sage Publications, Inc.Google Scholar
Eddy, D., Krishnamurty, S., Grosse, I., & Steudel, M. (2019). Early design stage selection of best manufacturing process. Journal of Engineering Design, 31, 1–36. https://doi.org/10.1080/09544828.2019.1662894CrossRefGoogle Scholar
Ferrer, I., Rios, J., & Ciurana, J. (2009). An approach to integrate manufacturing process information in part design phases. Journal of Materials Processing Technology, 209(4), 2085–2091. https://doi.org/10.1016/j.jmatprotec.2008.05.009CrossRefGoogle Scholar
Fiorineschi, L., Frillici, F., & Rotini, F. (2016, January 1). Re-Design the Design Task Through TRIZ Tools.Google Scholar
Fleury, S., & Chaniaud, N. (2023). Multi-user centered design: Acceptance, user experience, user research and user testing. Theoretical Issues in Ergonomics Science, 1–16. https://doi.org/10.1080/1463922X.2023.2166623CrossRefGoogle Scholar
Giachetti, R. (1997). Decision Support System for Material and Manufacturing Process Selection. Journal of Intelligent Manufacturing, 9. https://doi.org/10.1023/A:1008866732609Google Scholar
Hernández-Castellano, P., Martínez-Rivero, M. D., Marrero-Alemán, M. D., & Suárez-García, L. (2019). Manufacturing Process Selection Integrated in the Design Process: University and Industry. Procedia Manufacturing, 41, 1079–1086. https://doi.org/10.1016/j.promfg.2019.10.036CrossRefGoogle Scholar
ISO. (2015). IEC 62366-1:2015, Medical devices Part 1: Application of usability engineering to medical devices. ISO. https://www.iso.org/standard/63179.htmlGoogle Scholar
Jaksic, D., Candrlic, S., & Poscic, P. (2022). From User Requirements to Document Repository Enriched with Metadata – a Case Study. Procedia Computer Science, 204, 760–767. https://doi.org/10.1016/j.procs.2022.08.092CrossRefGoogle Scholar
Kersten, W. C., Diehl, J. C., & Engelen, J. M. L. van. (2018). Facing complexity through varying the clarification of the design task: How a multi-contextual approach can empower design engineers to address complex challenges. FormAkademisk, 11(4), https://doi.org/10.7577/formakademisk.2621CrossRefGoogle Scholar
Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. 2.Google Scholar
Lee, C.-H. (1992). A knowledge-based systems approach for manufacturing process selection in design [Ph.D.]. Ohio State University.Google Scholar
Liu, W., Zhu, Z., & Ye, S. (2020). A decision-making methodology integrated in product design for additive manufacturing process selection. Rapid Prototyping Journal, 26(5), 895–909. https://doi.org/10.1108/RPJ-06-2019-0174CrossRefGoogle Scholar
Mabkhot, M., Al-Samhan, M., & Hidri, A., L. (2019). An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection. Advances in Materials Science and Engineering, 2019, 1–18. https://doi.org/10.1155/2019/2505183CrossRefGoogle Scholar
Milicevic, A., Jackson, D., Gligoric, M., & Marinov, D. (2013). Model-based, event-driven programming paradigm for interactive web applications. 17–36. https://doi.org/10.1145/2509578.2509588CrossRefGoogle Scholar
Minguella-Canela, J., & Buj Corral, I. (2020). Decision Support Models for the Selection of Production Strategies in the Paradigm of Digital Manufacturing, Based on Technologies, Costs and Productivity Levels, IntechOpen. https://doi.org/10.5772/intechopen.89535CrossRefGoogle Scholar
Nagy, L., Ruppert, T., & Abonyi, J. (2021). Ontology-Based Analysis of Manufacturing Processes: Lessons Learned from the Case Study of Wire Harness Production. Complexity, 2021, https://doi.org/10.1155/2021/8603515CrossRefGoogle Scholar
Park, H.-S., & Tran, N.-H. (2017). A Decision Support System for Selecting Additive Manufacturing Technologies. Proceedings of the 2017 International Conference on Information System and Data Mining, 151–155. https://doi.org/10.1145/3077584.3077606CrossRefGoogle Scholar
Thompson, M. (2013). Improving the requirements process in Axiomatic Design Theory. CIRP Annals - Manufacturing Technology, 62, 115–118. https://doi.org/10.1016/j.cirp.2013.03.114CrossRefGoogle Scholar
Tipton, E., Hallberg, K., Hedges, L., & Chan, W. (2016). Implications of Small Samples for Generalization: Adjustments and Rules of Thumb. Evaluation Review, 41. https://doi.org/10.1177/0193841X16655665Google ScholarPubMed
Vasileiou, K., Barnett, J., Thorpe, S., & Young, T. (2018). Characterising and justifying sample size sufficiency in interview-based studies: Systematic analysis of qualitative health research over a 15-year period. BMC Medical Research Methodology, 18(1), 148. https://doi.org/10.1186/s12874-018-0594-7CrossRefGoogle Scholar
Wortmann, N., Jürgenhake, C., Seidenberg, T., Dumitrescu, R., & Krause, D. (2019). Methodical Approach for Process Selection in Additive Manufacturing. 1, 779–788. https://doi.org/10.1017/dsi.2019.82Google Scholar
Yadav, H., Paruthi, R., & Gupta, V. (2014). Impact of Event Driven Programing Paradigm on Real World.Google Scholar
Yip, M. H., Phaal, R., & Probert, D. R. (2019). Integrating Multiple Stakeholder Interests into Conceptual Design. Engineering Management Journal, 31(3), 142–157. https://doi.org/10.1080/10429247.2019.1570456CrossRefGoogle Scholar
Zhang, Y., Xu, Y., & Bernard, A. (2014). A new decision support method for the selection of RP process: Knowledge value measuring. International Journal of Computer Integrated Manufacturing, 27(8), 747–758. https://doi.org/10.1080/0951192X.2013.834474CrossRefGoogle Scholar