Fuzzified synthetic extent weighted average for appraisal of design concepts

Document Type: Research Paper

Author

Department of Mechanical Engineering, Federal University of Technology, Akure, Nigeria.

10.22105/riej.2020.214239.1111

Abstract

Decision-making models such as Analytical Hierarchy Process (AHP), Weighted Decision Matrix (WDM), Pugh Matrix and the likes have been able to assist in the decision process considering the objectives of each evaluation criteria in the alternatives. However, these models need to consider the qualitative and subjective nature of the design features. In order to reduce the unbalanced scale of judgment and the uncertainty associated with the crisp information in the decision process, fuzzified and hybridized models are necessary. Existing hybridized decision models applied for machine concept selection deploy several design features and sub-features at the conceptual product design, which thus make the decision making process to be tedious. In light of this, this article presents a hybridized decision-making model, which harness the comparative strength and computational integrity of fuzzy pairwise comparison matrix and fuzzy weighted average, to numerically analyse a reasonable amount of machine design features, thereby making decision making process less tedious. Design for reconfigurability and functionality which are peculiar to reconfigurable machines was introduced using a Reconfigurable Assembly Fixture (RAF) as a case study while other design features related to design concept evaluation were grouped under design for X. The result of the hybridized model shows that, concept three is the optimal design from four sets of designs. This is compared to previous publication using the RAF design concepts with different design features and sub-features. The comparison indicates that there is a close range in the final values of the designs due to the inclusion of several sub-features in the decision process which were not used in the previous study. 

Keywords

Main Subjects


[1]     Olabanji, O. M. (2018). Reconnoitering the suitability of fuzzified weighted decision matrix for design process of a reconfigurable assembly fixture. International journal of design engineering8(1), 38-56.

[2]     Renzi, C., Leali, F., Pellicciari, M., Andrisano, A. O., & Berselli, G. (2015). Selecting alternatives in the conceptual design phase: an application of Fuzzy-AHP and Pugh’s controlled convergence. International journal on interactive design and manufacturing (IJIDeM)9(1), 1-17.

[3]     Olabanji, O., Mpofu, K., & Battaïa, O. (2016). Design, simulation and experimental investigation of a novel reconfigurable assembly fixture for press brakes. The international journal of advanced manufacturing technology82(1-4), 663-679.

[4]     Benkamoun, N., Huyet, A. L., & Kouiss, K. (2013, October). Reconfigurable assembly system configuration design approaches for product change. Proceedings of 2013 international conference on industrial engineering and systems management (IESM) (pp. 1-8). IEEE.

[5]     Li, Z., Pasek, Z. J., & Adams, J. (2004). Reconfigurable fixtures: concept and examples.Symposium conducted at the meeting of USA symposium on flexible automation, Denver, Colorado.

[6]     Yu, K., Wang, S., Wang, Y., & Yang, Z. (2018). A flexible fixture design method research for similar automotive body parts of different automobiles. Advances in mechanical engineering10(2), 1687814018761272.

[7]     Renzi, C., & Leali, F. (2016). A multicriteria decision‐making application to the conceptual design of mechanical components. Journal of multi‐criteria decision analysis23(3-4), 87-111.

[8]     Girod, M., Elliott, A. C., Burns, N. D., & Wright, I. C. (2003). Decision making in conceptual engineering design: an empirical investigation. Proceedings of the institution of mechanical engineers, Part B: journal of engineering manufacture217(9), 1215-1228.

[9]     Kuo, T. C., Huang, S. H., & Zhang, H. C. (2001). Design for manufacture and design for ‘X’: concepts, applications, and perspectives. Computers & industrial engineering41(3), 241-260.

[10] Dombrowski, U., Schmidt, S., & Schmidtchen, K. (2014). Analysis and integration of design for X approaches in lean design as basis for a lifecycle optimized product design. Procedia CIRP15, 385-390.

[11] Skander, A., Roucoules, L., & Meyer, J. S. K. (2008). Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design. The international journal of advanced manufacturing technology37(5-6), 443-454.

[12] Harari, N. S., Fundin, A., & Carlsson, A. L. (2018). Components of the design process of flexible and reconfigurable assembly systems. Procedia manufacturing25, 549-556.

[13] Renzi, C., Leali, F., & Di Angelo, L. (2017). A review on decision-making methods in engineering design for the automotive industry. Journal of engineering design28(2), 118-143.

[14] Olabanji, O. M., & Mpofu, K. (2014). Comparison of weighted decision matrix, and analytical hierarchy process for CAD design of reconfigurable assembly fixture. Procedia CIRP23, 264-269.

[15] Okudan, G. E., & Shirwaiker, R. A. (2006, July). A multi-stage problem formulation for concept selection for improved product design. 2006 technology management for the global future-PICMET 2006 conference (Vol. 6, pp. 2528-2538). IEEE.

[16] Okudan, G. E., & Tauhid, S. (2008). Concept selection methods–a literature review from 1980 to 2008. International journal of design engineering1(3), 243-277.

[17] Wang, J. (2001). Ranking engineering design concepts using a fuzzy outranking preference model. Fuzzy sets and systems119(1), 161-170.

[18] Olabanji, O., & Mpofu, K. (2020). Pugh matrix and aggregated by extent analysis using trapezoidal fuzzy number for assessing conceptual designs. Decision science letters9(1), 21-36.

[19]      Goyal, K. K., Jain, P. K., & Jain, M. (2012). Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. International journal of production research50(15), 4175-4191.

[20] Youssef, A. M., & ElMaraghy, H. A. (2007). Optimal configuration selection for reconfigurable manufacturing systems. International journal of flexible manufacturing systems19(2), 67-106.

[21] Ashraf, M., & Hasan, F. (2018). Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints. The international journal of advanced manufacturing technology98(5-8), 2137-2156.

[22] Gupta, A., Jain, P. K., & Kumar, D. (2015). Configuration selection of reconfigurable manufacturing system based on performance. International journal of industrial and systems engineering20(2), 209-230.

[23] Rehman, A. U. (2017). Multi-criteria grey relational approach to evaluating reconfigurable manufacturing configurations. South African Journal of industrial engineering28(1), 36-46.

[24] Renna, P. (2017). Decision-making method of reconfigurable manufacturing systems’ reconfiguration by a Gale-Shapley model. Journal of manufacturing systems45, 149-158.

[25] Yi, G., Wang, Y., & Zhao, X. (2018). Evaluation and optimization of the design schemes of reconfigurable machine tools based on multiple-attribute decision-making. Advances in mechanical engineering10(12), 1687814018813054.

[26] Mokhtarian, M. N. (2011). A new fuzzy weighted average (FWA) method based on left and right scores: An application for determining a suitable location for a gas oil station. Computers & mathematics with applications61(10), 3136-3145.

[27] Wang, J. (2002). Improved engineering design concept selection using fuzzy sets. International journal of computer integrated manufacturing15(1), 18-27.

[28] Yeo, S. H., Mak, M. W., & Balon, S. A. P. (2004). Analysis of decision-making methodologies for desirability score of conceptual design. Journal of engineering design15(2), 195-208.

[29] Balin, A., Demirel, H., & Alarcin, F. (2016). A novel hybrid MCDM model based on fuzzy AHP and fuzzy TOPSIS for the most affected gas turbine component selection by the failures. Journal of marine engineering & technology15(2), 69-78.

[30] Alarcin, F., Balin, A., & Demirel, H. (2014). Fuzzy AHP and fuzzy TOPSIS integrated hybrid method for auxiliary systems of ship main engines. Journal of marine engineering & technology13(1), 3-11.

[31] Nazam, M., Xu, J., Tao, Z., Ahmad, J., & Hashim, M. (2015). A fuzzy AHP-TOPSIS framework for the risk assessment of green supply chain implementation in the textile industry. International journal of supply and operations management2(1), 548-568.

[32] Chakraborty, K., Mondal, S., & Mukherjee, K. (2017). Analysis of product design characteristics for remanufacturing using fuzzy AHP and axiomatic design. Journal of engineering design28(5), 338-368.

[33] Aryanezhad, M., Tarokh, M. J., Mokhtarian, M. N., & Zaheri, F. (2011). A fuzzy TOPSIS method based on left and right scores. International journal of industrial engineering & production research22(1), 53-60.

[34] [Amiri-Aref, M., Javadian, N., & Kazemi, M. (2012). A new fuzzy positive and negative ideal solution for fuzzy TOPSIS. WSEAS Transactions on Circuits and Systems11(3), 92-103.

[35] Mokhtarian, M. N., & Hadi-Vencheh, A. (2012). A new fuzzy TOPSIS method based on left and right scores: An application for determining an industrial zone for dairy products factory. Applied soft computing12(8), 2496-2505.

[36] Olabanji, O. M. (2015). Development of a reconfigurable assembly system for the assembly of press brakes (Doctoral Thesis in the Department of industrial Engineering, Tshwane University of Technology, Pretoria West, South Africa). Retrieved from https://books.google.com.ng/books?id=5VNDAQAACAAJ

[37]  Olabanji, O. M., & Mpofu, K. (2019). Adopting hybridized multicriteria decision model as a decision tool in engineering design. Journal of engineering, design and technology, 18(2).