Open Access Open Access  Restricted Access Subscription or Fee Access

Development of Visual Identification System for Classification of Part Family for Medium Scale Industries


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/ireme.v11i10.13400

Abstract


The identification of industrial components based on their part family is very much required for the production planning and sequencing. In the present paper, a visual identification system is proposed for identifying the engineering works (or parts or components) based on their part families in group technology, computer vision and artificial intelligence techniques. The computer vision system is responsible for extracting the features of the component. The developed software provides the part family classification code by using a group technology method on the component from the data obtained by the vision system. The classification code can be used for further processing. The algorithms and the design details are deliberated. In this paper, the identification of part families by categorization with vision system is discussed, and it has been used to identify the part families obtaining a 100% accuracy.
Copyright © 2017 Praise Worthy Prize - All rights reserved.

Keywords


Part Family Formation; Expert Systems; Artificial Intelligent; Computer Programming; Computer Vision

Full Text:

PDF


References


M. Spilka, A. Kania, R. Nowosielski, Integration of management systems on the chosen example, Journal of Achievements in Materials and Manufacturing Engineering, Vol. 35, n. 2, pp.204-210, 2009.

M. Dudek-Burlikowska, D. Szewieczek, The Poka-Yoke method as an improving quality tool of operations in the process, Journal of Achievements in Materials and Manufacturing Engineering, Vol. 36, n. 1, pp. 95-102, 2009.

M. Tolouei-Rad, An approach towards fully integration of CAD and CAM technologies, Journal of Achievements in Materials and Manufacturing Engineering, Vol. 18, pp.31-36, 2006.

N. Ismail, F. Musharavati, A. S. M. Hamouda, A. R. Ramli, Manufacturing process planning optimisation in reconfigurable multiple parts flow lines, Journal of Achievements in Materials and Manufacturing Engineering, Vol. 3 1, n. 2, pp. 671-677, 2008.

M. Kazerooni, L. Loung, K. Abhary, A. Kazerooni, Fluency Quality: A new performance measure for evaluation of clustering techniques in cellular manufacturing system design, Proceedings of the 9th International Conference “Flexible Automation and Intelligent Manufacturing”, Tilburg, Netherlands, 1999, pp. 357-362.

Lenka Debarova, Denisa Krchova, Ivan Kuric, “Group Technology in context of the product classification”, Advances in Science and Technology Research Journal, Vol 8, n. 21, pp. 78-81, 2014.

Anonymous, “How to predict the benefit of Group Technology”, Prod. Engineer, pp. 51-54, Feb, 1980.

K Arun Prasath, R Deepak Joel Johnson, “Concept of Group Technology accomplishment in the field of cellular manufacturing systems”, International Research Journal of Engineering and Technology, Vol. 2, n. 6, Sep-2015, pp. 991-996.

I. Kuric, J. Kuba, New methods of product classification for computer aided process planning systems, Eng. Rev. Vol. 2, n. 1, pp.13-17, 2007.

A. I. Edinbarough, P. Radhakrishnan, Visual identification of industrial components using part family classification coding system, Computers in Industry, Vol. 26, pp.85-91, 1995.
http://dx.doi.org/10.1016/0166-3615(95)80008-5

Di Nardo, M., Madonna, M., Santillo, L., Safety Management System: a System Dynamics Approach to Manage Risks in a Process Plant, (2016) International Review on Modelling and Simulations (IREMOS), 9 (4), pp. 256-264.
http://dx.doi.org/10.15866/iremos.v9i4.9688

Al Hazza, M., Abu Bakar, A., Adesta, E., Taha, A., Real Time Handling System to Enhance the Productivity Based on the Layout Improvement, (2016) International Review on Modelling and Simulations (IREMOS), 9 (6), pp. 459-463.
http://dx.doi.org/10.15866/iremos.v9i5.9645

Batischev, V., Kuzmin, M., Pischukhin, A., Solovyov, N., System of Computer Vision for Cold-Rolled Metal Quality Control, (2016) International Review of Automatic Control (IREACO), 9 (4), pp. 259-263.
http://dx.doi.org/10.15866/ireaco.v9i4.9870

Salazar, G., Ramos, O., Amaya, D., Control Algorithm for an Industrial Robotic Arm Using Computer Vision, (2015) International Review of Mechanical Engineering (IREME), 9 (2), pp. 182-189.
http://dx.doi.org/10.15866/ireme.v9i2.5457

Rosario, J., Kubiak, D., Oliveira, E., Silveira, A., Melo, L., Supervision and Control Architecture for CNC Machine TOOL Using Rapid Prototyping, (2015) International Review of Mechanical Engineering (IREME), 9 (3), pp. 212-222.
http://dx.doi.org/10.15866/ireme.v9i3.3088

Demidova, L., Sokolova, Y., Nikulchev, E., Use of Fuzzy Clustering Algorithms Ensemble for SVM Classifier Development, (2015) International Review on Modelling and Simulations (IREMOS), 8 (4), pp. 446-457.
http://dx.doi.org/10.15866/iremos.v8i4.6825


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize