article

A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN

Authors:

Ling-Feng Hsieh,

Pei-Hao TaiAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 37, Issue 1

Pages 716 - 726

https://doi.org/10.1016/j.eswa.2009.05.098

Published: 01 January 2010 Publication History

Abstract

Research in assembly planning can be categorised into three types of approach: graph-based, knowledge-based and artificial intelligence approaches. The main drawbacks of the above approaches are as follows: the first is time-consuming; in the second approach it is difficult to find the optimal solution; and the third approach requires a high computing efficiency. To tackle these problems, this study develops a novel approach integrated with some graph-based heuristic working rules, robust back-propagation neural network (BPNN) engines via Taguchi method and design of experiment (DOE), and a knowledge-based engineering (KBE) system to assist the assembly engineers in promptly predicting a near-optimal assembly sequence. Three real-world examples are dedicated to evaluating the feasibility of the proposed model in terms of the differences in assembly sequences. The results show that the proposed model can efficiently generate BPNN engines, facilitate assembly sequence optimisation and allow the designers to recognise the contact relationships, assembly difficulties and assembly constraints of three-dimensional (3D) components in a virtual environment type.

References

[1]

Response surface methodology: A neural network approach. European Journal of Operational Research. v101. 65-73.

[2]

Bourjault, A. (1984). Contribution á une approche méthodologique de l'assemblage automatisé: Elaboration automatique des séquences opératoires, Ph.D. Thesis, Faculté des Sciences et des Techniques de l'Université de Franche-Comté, France.

[3]

Chen, C.L.P. (1990). Neural computation for planning and/or precedence-constraint robot assembly sequences. In Proceedings of the International Conference on Neural Networks (pp. 127-142). San Diego, CA.

[4]

Neural network in detecting the change of process mean value and variance. Journal of the Chinese Institute of Industrial Engineers. v12 i3. 215-223.

[5]

A neural-network approach for an automatic LED inspection system. Expert Systems with Applications. v33 i3. 531-537.

Digital Library

[6]

Optimization of assembly plan through a three-stage integrated approach. International Journal of Computer Applications in Technology. v19 i1. 28-38.

Digital Library

[7]

Optimizing assembly planning through a three-stage integrated approach. International Journal of Production Economics. v88. 243-256.

[8]

A three-stage integrated approach for assembly sequence planning using neural networks. Expert Systems with Applications. v34. 1777-1786.

Digital Library

[9]

Simplified generation of all mechanical assembly sequences. IEEE Transactions on Robotics and Automation. v3 i6. 640-658.

[10]

An information criterion for optimal neural network selection. IEEE Transaction on Neural Network. v2 i5. 490-497.

Digital Library

[11]

Haykin, S. (1999). Neural Networks: A comprehensive foundation. Ontario, Canada: Prentice Hall.

Digital Library

[12]

Representations of mechanical assembly sequences. IEEE Transactions on Robotics and Automation. v7 i2. 211-227.

[13]

A correct and complete algorithm for the generation of mechanical assembly sequence. IEEE Transactions on Robotics and Automation. v7 i2. 228-240.

[14]

A neural network based computational scheme for generating optimized robotic assembly sequences. Engineering Application Artificial Intelligence. v8 i2. 129-145.

[15]

Progress in supervised neural networks. IEEE Signal Processing Magazine. 8-39.

[16]

Manufacturing process for engineering materials. 2nd ed. Addison-Wesley Publishing Company.

[17]

Optimal design of neural network using the Taguchi method. Neurocomputing. v7. 225-245.

[18]

Intelligent assembly planning on triaxial products. Concurrent Engineering: Research and Applications. v1 i2. 311-319.

[19]

Design for manufacture and design for'X': Concepts, applications, and perspectives. Computers&Industrial Engineering. v41. 241-260.

[20]

A systematic approach for automatic assembly sequence plan generation. International Journal of Advanced Manufacturing Technology. v24. 752-763.

[21]

Lee, S. (1989). Disassembly planning by subassembly extraction. In Proceedings of the third ORSA/TIMS conference on flexible manufacturing systems (pp. 383-388). Cambridge.

[22]

An integrated approach to automated assembly planning for three-dimensional mechanical products. International Journal of Production Research. v31 i5. 1201-1227.

[23]

Inspection of defects in optical fibers based on back-propagation neural networks. Optics Communications. v198 i4-6. 369-378.

[24]

Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study. Environmental Modelling&Software. v13. 179-191.

[25]

Assembly sequence planning and optimisation using genetic algorithms. Applied Soft Computing. v2 i3. 223-253.

[26]

Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Transaction on Neural Network. v5. 865-872.

Digital Library

[27]

Onoda, T. (1995). Neural network information criterion for optimal number of hidden units. In Proceedings of the IEEE international conference on neural networks (Vol. 1, pp. 270-280).

[28]

Santos, M. S.&Ludermir, B. (1999). Using factorial design to optimize neural networks. In International joint conference on IEEE neural networks (Vol. 2, pp. 857-861). Washington, DC.

[29]

A neural predictor to analyze the effects of metal matrix composite structure (6063 Al/SiCp MMC) on journal bearing. Industrial Lubrication and Tribology. v58 i2. 95-109.

[30]

An ANN-based element extraction method for automatic mesh generation. Expert Systems with Applications. v29. 193-206.

Cited By

Hong QChen HZhang BFuhlbrigge T(2018)Assembly Control Parameter Learning for Complex Robotic Assembly Processes2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)10.1109/ROBIO.2018.8665309(2526-2530)Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1109/ROBIO.2018.8665309
Kretschmer RPfouga ARulhoff SStjepandi J(2017)Knowledge-based design for assembly in agile manufacturing by using Data Mining methodsAdvanced Engineering Informatics10.1016/j.aei.2016.12.00633:C(285-299)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.aei.2016.12.006
(2016)An advanced immune based strategy to obtain an optimal feasible assembly sequenceAssembly Automation10.5555/3206468.320647236:2(127-137)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.5555/3206468.3206472
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A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 37, Issue 1

January, 2010

907 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2009.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 January 2010

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Cited By

Hong QChen HZhang BFuhlbrigge T(2018)Assembly Control Parameter Learning for Complex Robotic Assembly Processes2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)10.1109/ROBIO.2018.8665309(2526-2530)Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1109/ROBIO.2018.8665309
Kretschmer RPfouga ARulhoff SStjepandi J(2017)Knowledge-based design for assembly in agile manufacturing by using Data Mining methodsAdvanced Engineering Informatics10.1016/j.aei.2016.12.00633:C(285-299)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.aei.2016.12.006
(2016)An advanced immune based strategy to obtain an optimal feasible assembly sequenceAssembly Automation10.5555/3206468.320647236:2(127-137)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.5555/3206468.3206472
Pontes FAmorim GBalestrassi PPaiva AFerreira J(2016)Design of experiments and focused grid search for neural network parameter optimizationNeurocomputing10.1016/j.neucom.2015.12.061186:C(22-34)Online publication date: 19-Apr-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.12.061
Ahmad MMondal S(2016)Dynamic supplier selection model under two-echelon supply networkExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.08.04365:C(255-270)Online publication date: 15-Dec-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.08.043
Bahubalendruni MBiswal BKumar MNayak R(2015)Influence of assembly predicate consideration on optimal assembly sequence generationAssembly Automation10.1108/AA-03-2015-02235:4(309-316)Online publication date: 7-Sep-2015
https://dl.acm.org/doi/10.1108/AA-03-2015-022
Quintana-Amate SBermell-Garcia PTiwari A(2015)Transforming expertise into Knowledge-Based Engineering toolsKnowledge-Based Systems10.1016/j.knosys.2015.04.00284:C(89-97)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.knosys.2015.04.002
Ou LXu X(2013)Relationship matrix based automatic assembly sequence generation from a CAD modelComputer-Aided Design10.1016/j.cad.2013.04.00245:7(1053-1067)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.cad.2013.04.002
Hong C(2012)Using the Taguchi method for effective market segmentationExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.11.04039:5(5451-5459)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.11.040

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