Surface defects classification using artificial neural networks in vision based polishing robot

Anton Satria Prabuwono, Adnan Rachmat Anom Besari, Ruzaidi Zamri, Md Dan Md Palil, [No Value] Taufik

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    One of the highly skilled tasks in manufacturing is the polishing process. The purpose of polishing is to get uniform surface roughness. In order to reduce the polishing time and to cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper proposes a vision system to measure surface defects that have been classified to some level of surface roughness. Artificial neural networks are used to classify surface defects and to give a decision in order to drive the actuator of the arm robot. Force and rotation time have been chosen as output parameters of artificial neural networks. The results show that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects classification using a vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotics, especially in the polishing process.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages599-608
    Number of pages10
    Volume7102 LNAI
    EditionPART 2
    DOIs
    Publication statusPublished - 2011
    Event4th International Conference on Intelligent Robotics and Applications, ICIRA 2011 - Aachen
    Duration: 6 Dec 20118 Dec 2011

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume7102 LNAI
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other4th International Conference on Intelligent Robotics and Applications, ICIRA 2011
    CityAachen
    Period6/12/118/12/11

    Fingerprint

    Surface Defects
    Polishing
    Surface defects
    Artificial Neural Network
    Robot
    Robots
    Neural networks
    Surface Roughness
    Robotics
    Surface roughness
    Surface measurement
    Vision System
    Shortage
    Measurement System
    Computer Vision
    Computer vision
    Actuator
    Actuators
    Manufacturing
    Classify

    Keywords

    • artificial neural networks
    • polishing robot
    • surface defects
    • vision sensor

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Prabuwono, A. S., Besari, A. R. A., Zamri, R., Md Palil, M. D., & Taufik, N. V. (2011). Surface defects classification using artificial neural networks in vision based polishing robot. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 7102 LNAI, pp. 599-608). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7102 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-25489-5_58

    Surface defects classification using artificial neural networks in vision based polishing robot. / Prabuwono, Anton Satria; Besari, Adnan Rachmat Anom; Zamri, Ruzaidi; Md Palil, Md Dan; Taufik, [No Value].

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7102 LNAI PART 2. ed. 2011. p. 599-608 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7102 LNAI, No. PART 2).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Prabuwono, AS, Besari, ARA, Zamri, R, Md Palil, MD & Taufik, NV 2011, Surface defects classification using artificial neural networks in vision based polishing robot. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 7102 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7102 LNAI, pp. 599-608, 4th International Conference on Intelligent Robotics and Applications, ICIRA 2011, Aachen, 6/12/11. https://doi.org/10.1007/978-3-642-25489-5_58
    Prabuwono AS, Besari ARA, Zamri R, Md Palil MD, Taufik NV. Surface defects classification using artificial neural networks in vision based polishing robot. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 7102 LNAI. 2011. p. 599-608. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-25489-5_58
    Prabuwono, Anton Satria ; Besari, Adnan Rachmat Anom ; Zamri, Ruzaidi ; Md Palil, Md Dan ; Taufik, [No Value]. / Surface defects classification using artificial neural networks in vision based polishing robot. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7102 LNAI PART 2. ed. 2011. pp. 599-608 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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    abstract = "One of the highly skilled tasks in manufacturing is the polishing process. The purpose of polishing is to get uniform surface roughness. In order to reduce the polishing time and to cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper proposes a vision system to measure surface defects that have been classified to some level of surface roughness. Artificial neural networks are used to classify surface defects and to give a decision in order to drive the actuator of the arm robot. Force and rotation time have been chosen as output parameters of artificial neural networks. The results show that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects classification using a vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotics, especially in the polishing process.",
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