Multi-criteria fuzzy decision support for conceptual evaluation in design of mechatronic systems: a quadrotor design case study

Research in Engineering Design - Tập 29 - Trang 329-349 - 2018
Abolfazl Mohebbi1, Sofiane Achiche1, Luc Baron1
1Department of Mechanical Engineering, École Polytechnique de Montréal, Montreal, Canada

Tóm tắt

Designing mechatronic systems is known to be a very complex and tedious process due to the high number of system components, their multi-physical aspects, the couplings between the different domains involved in the product, and the interacting design objectives. This inherent complexity calls for the crucial need of a systematic and multi-objective design thinking methodology to replace the often-used sequential design approach that tends to deal with the different domains and their corresponding design objectives separately leading to functional but not necessarily optimal designs. Thus, a new approach based on a multi-criteria profile for mechatronic systems is presented in this paper for the conceptual design stage. Additionally, to facilitate fitting the intuitive requirements for decision-making in the presence of interacting criteria, three different methods are proposed and compared using a case study of designing a vision-guided quadrotor drone system. These methods benefit from three different aggregation techniques such as Choquet integral, Sugeno integral and fuzzy-based neural network. To validate the decision yielded by the results of global concept score for each aggregation methods, a computer simulation of a visual servoing system on all design alternatives for quadrotor drone has been performed. It is shown that although the Sugeno fuzzy can be a useful aggregation function for decisions under uncertainty, but the approaches using Choquet fuzzy and fuzzy integral-based neural network seem to be more precise and reliable in a multi-criteria design problem where interaction between the objectives cannot be overlooked.

Tài liệu tham khảo

AB-019: Lifetime of DC Vibration Motors (MTTF & FIT) (2014) http://www.precisionmicrodrives.com/application-notes-technical-guides/application-bulletins/ab-019-lifetime-of-vibration-motors. Accessed Mar 2018 Anthony A, Jannett TC (2007) Measuring machine intelligence of an agent-based distributed sensor network system. In: Advances and innovations in systems, computing sciences and software engineering. Springer, Berlin, pp 531–535 Avigad G, Moshaiov A (2009) Set-based concept selection in multi-objective problems: optimality versus variability approach. J Eng Des 20(3):217–242 Bashir HA, Thomson V (1999) Estimating design complexity. J Eng Des 10(3):247–257 Behbahani S (2007) Practical and analytical studies on the development of formal evaluation and design methodologies for mechatronic systems. University of British Columbia. https://open.library.ubc.ca/cIRcle/collections/831/items/1.0080716 Behbahani S, de Silva CW (2007) Mechatronic design quotient as the basis of a new multicriteria mechatronic design methodology. Mechatron IEEE ASME Trans 12(2):227–232 Behbahani S, de Silva CW (2008) System-based and concurrent design of a smart mechatronic system using the concept of mechatronic design quotient (MDQ). Mechatron IEEE/ASME Trans 13(1):14–21 Bien Z et al (2002) Machine intelligence quotient: its measurements and applications. Fuzzy Sets Syst 127(1):3–16 Byun J-H, Elsayed EA (1996) A producibility index with process capability and manufacturing cost. In: 5th industrial engineering research conference proceedings. Minneapolis, MN, pp 381–386 Coelingh E, De Vries TJA, Koster R (2002) Assessment of mechatronic system performance at an early design stage. Mechatron IEEE/ASME Trans 7(3):269–279 Corbett J, Crookall J (1986) Design for economic manufacture. CIRP Ann Manuf Technol 35(1):93–97 Corke PI (1993) Visual control of robot manipulators—a review. Vis Servoing 7:1–31 De Silva CW (2004) Sensory information acquisition for monitoring and control of intelligent mechatronic systems. Int J Inf Acquis 1(1):89–99 de Silva C (2004) Sensory information acquisition for monitoring and control of intelligent mechatronic systems. Int J Inf Acquis 1(1):89–99 Engel A, Reich Y (2015) Advancing architecture options theory: six industrial case studies. Syst Eng 18(4):396–414 Engel A, Browning TR, Reich Y (2017) Designing products for adaptability: insights from four industrial cases. Decis Sci 48(5):875–917 Fazlollahtabar H, Mahdavi-Amiri N (2010) Design of an expert system to estimate cost in an automated jobshop manufacturing system. In: The 40th international conference on computers & indutrial engineering. Awaji, pp 1–6. https://doi.org/10.1109/ICCIE.2010.5668385 Ferreira IML, Gil PJS (2012) Application and performance analysis of neural networks for decision support in conceptual design. Expert Syst Appl 39(9):7701–7708 Freeman JA, Skapura DM (1991) Neural networks: algorithms, applications, and programming techniques. Addison-Wesley, Menlo Park Golmohammadi D (2011) Neural network application for fuzzy multi-criteria decision making problems. Int J Prod Econ 131(2):490–504 Grabisch M (1996) The application of fuzzy integrals in multicriteria decision making. Eur J Oper Res 89(3):445–456 Hecht-Nielsen R (1989) Theory of the backpropagation neural network. In: Neural networks. IJCNN, international joint conference Hegazy T, Ayed A (1998) Neural network model for parametric cost estimation of highway projects. J Constr Eng Manag 124(3):210–218 Hunt KJ et al (1992) Neural networks for control systems—a survey. Automatica 28(6):1083–1112 Janschek K (2012) Mechatronic systems design. Springer, Berlin Jian C, Song L (2004) A neural network approach-decision neural network (DNN) for preference assessment. Syst Man Cybern Part C Appl Rev IEEE Trans 34(2):219–225 Jones TL (2011) Handbook of reliability prediction procedures for mechanical equipment. Naval Surface Warfare Center, Carderock Division, West Bethesda Jung-Hsien C (1999) Choquet fuzzy integral-based hierarchical networks for decision analysis. Fuzzy Syst IEEE Trans 7(1):63–71 Kaushik A, Soni A, Soni R (1969) A simple neural network approach to software cost estimation. Glob J Comput Sci Technol III(I):22–30 Kim SW, Kim BK (1998) MIQ (machine intelligence quotient) for process control system. World Automation Congress, Anchorage Kim G, Seo D, Kang K (2005) Hybrid models of neural networks and genetic algorithms for predicting preliminary cost estimates. J Comput Civil Eng 19(2):208–211 Klement EP, Mesiar R, Pap E (2000) Triangular norms. Springer, Netherlands Krishnapuram R, Lee J (1992) Fuzzy-connective-based hierarchical aggregation networks for decision making. Fuzzy Sets Syst 46(1):11–27 Marichal JL (2000) An axiomatic approach of the discrete Choquet integral as a tool to aggregate interacting criteria. Fuzzy Syst IEEE Trans 8(6):800–807 Marichal JL (2002) Aggregation of interacting criteria by means of the discrete Choquet integral. In: Aggregation operators. Springer, Berlin, pp 224–244 Marichal JL, Roubens M (1998) Dependence between criteria and multiple criteria decision aid. In: Proceedings of 2nd international workshop on preferences and decision (TRENTO’98). Università di Trento, Trento Mileham A et al (1993) A parametric approach to cost estimating at the conceptual stage of design. J Eng Des 4(2):117–125 Mohebbi A, Keshmiri M, Xie WF (2014a) An eye-in-hand stereo visual servoing for tracking and catching moving objects. In: 33rd Chinese control conference (CCC), Nanjing Mohebbi A et al (2014b) Trends in concurrent, multi-criteria and optimal design of mechatronic systems: a review. In: Proceedings of the 2014 IEEE international conference on innovative design and manufacturing (ICIDM). Montreal, QC Mohebbi A, Achiche S, Baron L (2014c) Mechatronic multicriteria profile (MMP) for conceptual design of a robotic visual servoing system. In: ASME 2014 12th biennial conference on engineering systems design and analysis. American Society of Mechanical Engineers, Copenhagen Moulianitis VC, Aspragathos NA, Dentsoras AJ (2004) A model for concept evaluation in design––an application to mechatronics design of robot grippers. Mechatronics 14(6):599–622 Narukawa Y, Murofushi T (2004) Decision modelling using the Choquet integral. International conference on modeling decisions for artificial intelligence. Springer, Berlin Nasar SA (1981) Schaum’s outline of theory and problems of electric machines and electromechanics. McGraw-Hill Ryerson, Limited, Whitby Park HJ, Kim BK, Lim KY (2001) Measuring the machine intelligence quotient (MIQ) of human–machine cooperative systems. Syst Man Cybern Part A Syst Hum IEEE Trans 31(2):89–96 Reich Y, Ziv Av A (2005) Robust product concept generation. In: ICED 05: 15th international conference on engineering design: engineering design and the global economy. Engineers Australia, Melbourne Roy R (2003) Cost engineering: why, what and how? In: Roy R, Kerr C (eds) Decision engineering report series. Cranfield University, Bedfordshire, UK Rzevski G (2003) On conceptual design of intelligent mechatronic systems. Mechatronics 13(10):1029–1044 Takai S (2009) A case-based reasoning approach toward developing a belief about the cost of concept. Res Eng Des 20(4):255 Torry-Smith J, Achiche S, Mortensen N, Qamar A, Wikander J, During C (2011) Mechatronic design—still a considerable challenge. In: International design engineering technical conferences and computers and information in engineering conference. ASME, Washington, DC, pp 33–44 Torry-Smith JM et al (2012) Challenges in designing mechatronic systems. J Mech Des 135(1):011005 Ullman DG (2003) The mechanical design process. McGraw-Hill, Maidenheach Zhong X, Ichchou M, Saidi A (2010) Reliability assessment of complex mechatronic systems using a modified nonparametric belief propagation algorithm. Reliab Eng Syst Saf 95(11):1174–1185 Ziv-Av A, Reich Y (2005) SOS–subjective objective system for generating optimal product concepts. Des Stud 26(5):509–533