Application on concurrent product design and process planning for a Bicycle design

Subject Areas : Mathematical Optimization

Abbas Al-Refaie ¹

1 - Department of Industrial Engineering, University of Jordan, Amman, Jordan

Received: 2021-01-27 Accepted : 2021-06-23 Published : 2021-04-01

Keywords: Fuzzy Goal Programming, Simulation, product design, Optimization, Process design,

Abstract :

A high degree of uncertainty is incurred during the early product design and process planning stages of a bicycle. Consequently, this research presents an optimization procedure for the design of critical components of a bicycle frame and planning of their corresponding processes using simulation and fuzzy goal programming (FGP). For this frame, the reliability, dependability, mass, and fatigue factor were the main quality responses. Initially, the critical bicycle’s frame components with their corresponding design parameters and tolerances were identified via technical knowledge. Designed experimentation based on the Taguchi’s array was conducted by simulation with twenty replicates for various combinations of design parameters and tolerances of the key frame components. Then, satisfactory regression models were formulated to relate each quality response with design parameters and tolerances and then inserted in the optimization model. The design parameters and tolerances and processes' means and tolerances were expressed in terms of fuzzy membership functions and their relevant goals and constraints were included in the optimization model. Finally, the objective functions were minimizing the negative and positive deviation from desired goals and maximizing process capability indices. Results showed that the FGP optimization procedure effectively achieved the desired targets of the bicycle’s quality responses and process capability indices. In conclusion, the proposed procedure can be used for optimal concurrent product and process design in a wide range of industrial applications.

References:

[1] Phadke M. (1989). Quality engineering using robust design. Prentice Hall, Englewood Cliffs: NJ.

[2] Mcadams DA and Wood KL. (2000). Tuning parameter tolerance design: foundations, methods, and measures. Research in Engineering Design 12(3):152-162.

 

[3] Jeang A, and Chang CL. (2002). Concurrent optimization of parameter and tolerance design via computer simulation and statistical method. International Journal of Advanced Manufacturing Technology 19 (6):432–441.

 

[4] Jeang A. (2003). Robust Computer-Aided Parameter and Tolerance Determination for an Electronic Circuit Design. International Journal of Production Research 41 (5):883–895.

 

[5] Singh PK, Jain PK, Jain SC. (2004). A genetic algorithm-based solution to optimal Tolerance synthesis of mechanical assemblies with alternative manufacturing processes: Focus on complex tolerance problems. International Journal of Production Research 42(24): 5185- 5215.

 

[6] Agyapong-Kodua K, Brown R, Darlington R and Ratchev S. (2012). An integrated product–process design methodology for cost-effective product realization. International Journal of Computer Integrated Manufacturing 25 (9): 814–828.

 

[7] Jeang A. (2013). Concurrent product and process parameters determination under deterioration process. Applied Mathematics and Computation 219: 9132-9141.
[8] Jeang A. (2014). Robust optimization for Simultaneous process mean, process tolerance and product specification determination. International journal of Computer Integrated Manufacturing 27(11): 1008-1021.
[9] Jeang A and Lin Y. (2014). Product and process parameters determination for quality and cost. International Journal of systems Science 45(10): 2042-2054.
[10] Chen CH. and Chou CY. (2017). Optimum process mean, standard deviation and specification limits settings under the Burr distribution. Engineering Computations 34 (1): 66-76.
[11] Al-Refaie A, Haddadin M and Qabaja A. (2019). Optimal parameters and tolerances in concurrent product and process design using simulation and fuzzy goal programming", International Journal of Quality & Reliability Management 37(2): 372-392.
[12] Al-Refaie A, Diabat A. (2013). Optimizing convexity defect in a tile industry using fuzzy goal programming. Measurement 46(8): 2807-2815.
[13] Al-Refaie A, Diabat A, Li M-H. (2014). Optimizing tablets’ quality with multiple responses using fuzzy goal programming. Journal of Process Mechanical Engineering 228: 115-126.
[14] Al-Refaie A. (2015). Optimal performance of plastic pipes’ extrusion process using Min-Max model in fuzzy goal programming. Journal of Process Mechanical Engineering 231(4): 888-898.
[15] Al-Refaie A. (2014). FGP model to optimize performance of tableting process with three quality responses. Transactions of the Institute of Measurement and Control 36(3), 336-346. DOI:
10.1177/0142331213500807.
[16] Al-Refaie A. (2014). Optimization of multiple responses in the Taguchi method using fuzzy regression Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM 28(1), 99-107. DOI: 10.1017/S0890060413000528
[17] Al-Refaie A. (2015). Optimizing multiple quality responses in the Taguchi method using fuzzy goal programming: Modeling and applications. International Journal of Intelligent Systems 30(6), 651-675. DOI: 10.1002/int.21722.
[18] Jeang A. & Liang F. (2012). An innovation funnel process for set-based conceptual design via DOE exploration, DEA selection and computer simulation. International Journal of Production Research 50:23, 6792- 6810. DOI: 10.1080/00207543.2011.625050.

[19] Jeang A., Liang F., and Chung C.-P. (2008). Robust product development for multiple quality characteristics using computer experiments and an optimization technique. International Journal of Production Research 46(12), 3415–3439.