For Libraries For Publication Open Access Downloads About Us Contact Us
Paper Titles
An Approach to Optimum Route and Site Selection of a Steam Gathering System for Geothermal Power Plants Using Multiple Weight Distance Transform
p.1386
An Overbooking Model for Air Cargo Industry under Stochastic Booking Request and Show-Up Rate
p.1393
A Productivity Improvement of a Cookware Assembly Line
p.1398
Performance Improvement Using Simulation and Line Balancing; Application in Departure Terminal at Airport
p.1403
Using Taguchi Method to Optimize the Performance of Flexible Manufacturing Systems: An Empirical Model
p.1410
New View to Quality Assessment and Decision Making
p.1417
Cost Effective Maintenance of Wind Turbines Using Finite State Markov Model
p.1422
Using DOE for Implementation of Six Sigma to Reduce Non-Coaxiality Defect in Connecting Rods
p.1427
Implementation of Lean Manufacturing Principles in the Process Industry: A Case Study
p.1431

Using Taguchi Method to Optimize the Performance of Flexible Manufacturing Systems: An Empirical Model

Article Preview

Abstract:

Increasing global competition, shrinking product life cycles, and increasing product mix are defining a new manufacturing environment in world markets. This paper presents a case problem using Taguchi Method to find optimum design parameters for a Flexible Manufacturing System (FMS). A L8 array, signal-to-noise (S/N) ratio and analysis of variance (ANOVA) are employed to study performance characteristics of selected manufacturing system design parameters (e.g. layout, AGVs, buffers, and routings) with consideration of product mix demand. Various design and performance parameters are evaluated and compared for the original and the improved FMS. The results obtained by this method may be useful to other researchers for similar types of applications.

You might also be interested in these eBooks
Mechanical and Electrical Technology VII View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 799-800)

Pages:

1410-1416

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.799-800.1410

Citation:

Cite this paper

Online since:

October 2015

Authors:

Guanghsu A. Chang*, William R. Peterson

Keywords:

Analysis of Variance (ANOVA), Flexible Manufacturing Systems (FMS), Manufacturing Lead Time (MLT), Material Handling System (MHS), Orthogonal Array (OA), Signal-To-Noise (s/n) Ratio, Simulation, Taguchi Method

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] W.H. Yang, Y.S. Tang, (1998) Design optimization of cutting parameters for turning operations based on Taguchi method, Journal of Materials Processing Technology, 84, 122-129.

DOI: 10.1016/s0924-0136(98)00079-x

Google Scholar

[2] Unal, Resit, Edwin B. Dean, (1991) Taguchi Approach to Design Optimization for Quality and Cost: An Overview, The 1991Annual Conference of the International Society of Parametric Analysis.

Google Scholar

[3] Athreya, Srinivas, T.D. VEnkatesh, (2012).

Google Scholar

[4] Thamizhmanii S., S. Saparudin, S. Hasan (2007) Analyses of Surface Roughness by Turning Process Using Taguchi Method, Journal of Achievements in Materials and Manufacturing Engineering, V. 20, issues 1-2, pp.503-506.

Google Scholar

[5] N. Nalbant, H. Gokkaya, G. Sur, Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Materials and Design.

DOI: 10.1016/j.matdes.2006.01.008

Google Scholar

[6] Sijo M. T and Biju. N (2010), Taguchi Method for Optimization of Cutting Parameters in Turning Operations, Proceedings. of. International Conference on Advances in Mechanical Engineering (2010).

Google Scholar

[7] Rama and Padmanabhan. G (2012).

Google Scholar

[8] Puneetmangla, Nishant kr. Singh, Yashvirsingh (2011) Study of effect of turning parameters on work piece Hadness using Taguchimethod,. ICAM, pp- 695-700.

Google Scholar

[9] Groover, Milell P. Automation, Production Systems, and Computer-Integrated Manufacturing. Third edition, Prentice Hall, Pearson.

Google Scholar

[10] Harrell, C.; Ghosh, B.K.; and Bowden, R. (2000). Simulation Using ProModel. Boston: McGraw-Hill.

Google Scholar

[11] Smith, Jeffrey S. (2003) Survey on the use of Simulation for Manufacturing System Design and Operation, Journal of Manufacturing Systems Vol. 22/No. 2, p.157 – 171.

DOI: 10.1016/s0278-6125(03)90013-6

Google Scholar

[12] Anderson, M. and Olsson, G. (1998). A simulation-based decision support approach for operational capacity planning in a customer order driven assembly line., Proc. of 1998 Winter Simulation Conf., Washington, DC, pp.935-941.

DOI: 10.1109/wsc.1998.745793

Google Scholar

[13] Han, Kwan Hee, et al, Parameter-Driven Rapid Vitrual Prototyping of Flexible Manufacturing System, International Journal of Methematics and Computer in Simulation, Issue 4, Volume 6, (2012).

Google Scholar

[14] Prakash, Avneesh and Mingyun Chen. (1995) A Simulation Study of Flexible Manufacturing Systems, Journal of Computers and Industrial Engineering, Vol. 28, No. 1, pp.191-199.

DOI: 10.1016/0360-8352(94)00037-n

Google Scholar

[15] Safitra, Muflih; Ali Ahmad, and Abdulrahman Al-Ahmari. (2014).

Google Scholar

[16] Chan, F.T.S. (1995). Using simulation to predict system performance: a case study of an electro-phonetic deposition plant., Integrated Manufacturing Systems (v6, n5), pp.27-38.

DOI: 10.1108/09576069510093451

Google Scholar

[17] Al-Kahtani, Mohammed, et al., (2014) Cost-Benefit Analysis of Flexible Manufacturing Systems, Proceedings of 2014 International Conference on Industrial Engineering and Operations Management, Bali, Indonesia, January 7-9, (2014).

Google Scholar

[18] Negahban, Ashkan and Jeffrey S. Smith. (2014) Simulation for Manufacturing System Design and Operation: Literature Review and Analysis, Journal of Manufacturing Systems, v. 33 (2014) pp.241-261.

DOI: 10.1016/j.jmsy.2013.12.007

Google Scholar

© 2025 Trans Tech Publications Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For open access content, terms of the Creative Commons licensing CC-BY are applied.
Scientific.Net is a registered trademark of Trans Tech Publications Ltd.