Performance Analysis of Neural Network Model for Automated Visual Inspection with Robotic Arm Controller System

Authors

  • A. F. Kadmin Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
  • K. A. A. Aziz Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
  • Soufhwee A. R. Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
  • S. S. Abd Razak Faculty of Electronic & Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
  • M. Z. Salehan Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
  • N. A. Abdul Hadi Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
  • R. A. Hamzah Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
  • W. N. Abd Rashid Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. fauzan@utem.edu.my

Keywords:

Automated Visual Inspection, Neural Network, Product Identification, Radial Basis Function, Robotic Arm Controller,

Abstract

The concept of Automated Visual Inspection (AVI) have emerged as a powerful platform for industrial machine vision where it used to inspect a large number of products rapidly. However, a major problem with this kind of application is the quality produced by the recognition process. In this paper, a system with the capability of identifying and categorizing a product based on image processing has been implemented. The image was processed by using Radial Basis Function (RBF) based on output center and spread values optimization. Robotic arm controller developed for pick and place the product based on their categories. Two performance measures are used to validate the model classification range and the spread values. The results of this project indicate that the model used able to identify the product and classify it according to their shape.

References

R. Gruna and J. Beyerer, “Feature–specific Illumination Patterns for Automated Visual Inspection,” Instrumentation and Measurement Technology (I2MTC), 2012, pp: 360-365.

KAA Hamzah, R.A., Abd Ghani, S. F., Kadmin, A. F., Aziz, “A Practical Method For Camera Calibration In Stereo Vision Mobile Robot Navigation,” Research and Development (SCOReD), IEEE Student Conference, 2012, pp. 104-108.

K. A. A. Aziz, M. H. Mustafa, N. M. Z. Hashim, N. R. M. Nuri, A. F. Kadmin and A. Salleh, “Smart Android Wheelchair Controller Design,” International Journal for Advance Research in Engineering and Technology (IJARET), 2015, 3(3): 42-48.

D. Bajer, B. Zoric and G. Martinovic, “Effectiveness of Differential Evolution in training RBF Networks for Classification,” International Conference on Smart System & Technologies, 2016, pp. 179-184.

P. S. Lengare and M. E. Rane, “Human Hand Tracking using Matlab to Control Arduino based Robotic Arm,” International Conference on Pervasive Computing (ICPC), 2015, pp. 1-4.

R. Reddy and S. R. Nagaraja, “Integration of Robotic Arm with Vision System,” IEEE International Conference on Computational Intelligence and Computing Research, 2014, pp. 1-5.

J. M. Keller, D. Liu and D. B. Fogel, “Radial-Basis Function Networks,” Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems and Evolutionary Computation. 2016, pp. 50-84

R. A. Hamzah, S. F. A. Ghani, A. Din, K. A. A. Aziz, “Visualization Of Image Distortion On Camera Calibration For Stereo Vision Application,” IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2012, pp. 28-33.

Yong Shen Goh, “Robotic Arm,” Faculty of Electrical Engineering, Universiti Teknologi Malaysia: First Degree Thesis, 2010, pp. 1-4.

Men Wee Sam, “Robotic Arm with Image Processing,” Faculty of Electrical Engineering, Universiti Teknologi Malaysia: First Degree Thesis, 2010, pp. 1-4.

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Published

2018-05-31

How to Cite

Kadmin, A. F., Aziz, K. A. A., A. R., S., Abd Razak, S. S., Salehan, M. Z., Abdul Hadi, N. A., Hamzah, R. A., & Abd Rashid, W. N. (2018). Performance Analysis of Neural Network Model for Automated Visual Inspection with Robotic Arm Controller System. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-2), 19–22. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3952

Issue

Vol. 10 No. 2-2: Innovative and Sustainable Technologies for Societal Wellbeing II

Section

Articles

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