Design and implementation of intelligent electronic component inspection based on PLC and vision system

Authors

DOI:

https://doi.org/10.30656/jsmi.v7i2.6511

Keywords:

Automatic visual inspection, Intelligent terminals electronics component, Logic programming, PLC

Abstract

Customer demands for product quality are increasingly complex, requiring better inspection accuracy. It is not enough if done manually because it requires high costs and varying operator accuracy. Automatic vision inspect­ion was developed to check the product quality of terminal-type electronic components To solve this problem. Design intelligent inspection uses a conveyor driven by a stepper motor, a photosensor to calculate product distance, guides position to direct the product, a vision camera to detect product quality, cylinder ejection for product selection, and PLC as a control system. The process of detecting normal and abnormal product quality is carried out using computer logic control, then separating the ab­normal product into the reject box through the ejection cylinder. The machine speed is 60 pieces/minute. The system evaluation results are carried out on three parts of the system: the success rate on the vision camera is 100%, automatic product sorting through the cylinder ejection rate success is 100%, and the success rate for product positioning is 97.5%. This research provides a useful reference for developing intelligent automatic inspection technology in electronic components.

Downloads

Download data is not yet available.

References

N. Herakovic, Robot vision in industrial assembly and quality control processes. INTECH Open Access Publisher London, UK, 2010, [Online]. Available: https://www.intechopen.com/chapters/10595.

F. Frustaci, S. Perri, G. Cocorullo, and P. Corsonello, ‘An embedded machine vision system for an in-line quality check of assembly processes’, Procedia Manuf., vol. 42, pp. 211–218, 2020, doi: https://doi.org/10.1016/j.promfg.2020.02.072.

Z. F. Ikatrinasari and K. Kosasih, ‘Improving Quality Control Process Through Value Stream Mapping’, Int. J. Eng. Technol., vol. 7, no. 2, pp. 219–225, May 2018, doi: https://doi.org/10.14419/ijet.v7i2.29.13321.

S. Liu, Z. Xing, Z. Wang, S. Tian, and F. R. Jahun, ‘Development of machine-vision system for gap inspection of muskmelon grafted seedlings’, PLoS One, vol. 12, no. 12, p. e0189732, Dec. 2017, doi: https://doi.org/10.1371/journal.pone.0189732.

N. Ansari, S. S. Ratri, A. Jahan, M. Ashik-E-Rabbani, and A. Rahman, ‘Inspection of paddy seed varietal purity using machine vision and multivariate analysis’, J. Agric. Food Res., vol. 3, p. 100109, Mar. 2021, doi: https://doi.org/10.1016/j.jafr.2021.100109.

N. N. S. A. Rahman, N. M. Saad, A. R. Abdullah, M. R. M. Hassan, M. Basir, and N. S. M. Noor, ‘Automated Real-Time Vision Quality Inspection Monitoring System’, Indones. J. Electr. Eng. Comput. Sci., vol. 11, no. 2, pp. 775–783, Aug. 2018, doi: https://doi.org/10.11591/ijeecs.v11.i2.pp775-783.

B. Huang et al., ‘Research and implementation of machine vision technologies for empty bottle inspection systems’, Eng. Sci. Technol. an Int. J., vol. 21, no. 1, pp. 159–169, 2018, doi: https://doi.org/10.1016/j.jestch.2018.01.004.

Y. Yang, M. Gao, K. Yin, Z. Wu, and Y. Li, ‘An automatic visual inspection system for cone surface defects’, J. Comput. Methods Sci. Eng., vol. 15, no. 2, pp. 269–276, Jun. 2015, doi: https://doi.org/10.3233/JCM-150541.

O. Semeniuta, S. Dransfeld, K. Martinsen, and P. Falkman, ‘Towards increased intelligence and automatic improvement in industrial vision systems’, Procedia CIRP, vol. 67, pp. 256–261, 2018, doi: https://doi.org/10.1016/j.procir.2017.12.209.

Q. Zhou, R. Chen, B. Huang, C. Liu, J. Yu, and X. Yu, ‘An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods’, Sensors, vol. 19, no. 3. 2019, doi: https://doi.org/10.3390/s19030644.

L. Xu, X. He, X. Li, and M. Pan, ‘A machine-vision inspection system for conveying attitudes of columnar objects in packing processes’, Measurement, vol. 87, pp. 255–273, 2016, doi: https://doi.org/10.1016/j.measurement.2016.02.048.

N. Bao, H. Kuang, A. Simeone, L. Zhu, and Y. Fan, ‘A machine vision-based automatic inspection system for power station coal bunkers maintenance’, Procedia CIRP, vol. 103, pp. 250–255, 2021, doi: https://doi.org/10.1016/j.procir.2021.10.040.

H. Herlambang, H. H. Purba, and C. Jaqin, ‘Development of Machine Vision to Increase the Level of Automation in Indonesia Electronic Component Industry’, J. Eur. des Systèmes Autom., vol. 54, no. 2, pp. 253–262, Apr. 2021, doi: https://doi.org/10.18280/jesa.540207.

V. Torkzadeh and S. Toosizadeh, ‘Automatic visual inspection system for quality control of the sandwich panel and detecting the dipping and buckling of the surfaces’, Meas. Control, vol. 52, no. 7–8, pp. 804–813, Jun. 2019, doi: https://doi.org/10.1177/0020294019847706.

A. Zeiler, A. Steinboeck, M. Vincze, M. Jochum, and A. Kugi, ‘Vision-based inspection and segmentation of trimmed steel edges’, IFAC-PapersOnLine, vol. 52, no. 14, pp. 165–170, 2019, doi: https://doi.org/10.1016/j.ifacol.2019.09.182.

R. Ciobanu, D. Rizescu, and C. Rizescu, ‘Automatic Sorting Machine Based on Vision InspectionAutomatic Sorting Machine Based on Vision Inspection’, Int. J. Model. Optim., vol. 7, pp. 286–290, Oct. 2017, doi: https://doi.org/10.7763/IJMO.2017.V7.599.

A. Vandone, S. Baraldo, D. Anastassiou, A. Marchetti, and A. Valente, ‘3D vision system integration on Additive Manufacturing machine for in-line part inspection’, Procedia CIRP, vol. 95, pp. 72–77, 2020, doi: https://doi.org/10.1016/j.procir.2020.01.191.

A. Butterfield and J. Szymanski, A Dictionary of Electronics and Electrical Engineering. Oxford University Press, 2018, doi: https://doi.org/10.1093/acref/9780198725725.001.0001.

T. Czimmermann et al., ‘Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY’, Sensors, vol. 20, no. 5. 2020, doi: https://doi.org/10.3390/s20051459.

T. H. Febriana and H. Hasbullah, ‘Analysis and Defect Improvement Using FTA, FMEA, and MLR Through DMAIC Phase: Case Study in Mixing Process Tire Manufacturing Industry’, J. Eur. des Systèmes Autom., vol. 54, no. 5, pp. 721–731, Oct. 2021, doi: https://doi.org/10.18280/jesa.540507.

X. Xie, ‘A review of recent advances in surface defect detection using texture analysis techniques’, ELCVIA Electron. Lett. Comput. Vis. image Anal., vol. 7, no. 3, pp. 1–22, 2008, [Online]. Available: https://www.raco.cat/index.php/ELCVIA/article/view/150223.

R. Shamass and K. A. Cashell, ‘Bending Moment Capacity of Stainless Steel-Concrete Composite Beams’, in Proceedings 12th international conference on Advances in Steel-Concrete Composite Structures - ASCCS 2018, Jun. 2018, pp. 145–151, doi: https://doi.org/10.4995/ASCCS2018.2018.6969.

E. A. Alhassan, D. A. Olasehinde, A. Musonda, and O. M. Odeniyi, ‘Tensile and flexural behaviour of steel materials used in the construction of crop processing machines’, IOP Conf. Ser. Earth Environ. Sci., vol. 445, no. 1, p. 12044, 2020, doi: https://doi.org/10.1088/1755-1315/445/1/012044.

N. Mubarok, H. A. Notonegoro, and K. A. Z. Thosin, ‘Comparative Mechanical Improvement of Stainless Steel 304 Through Three Methods’, IOP Conf. Ser. Mater. Sci. Eng., vol. 367, no. 1, p. 12023, 2018, doi: https://doi.org/10.1088/1757-899X/367/1/012023.

F. Marques, W. M. da Silva, J. M. Pardal, S. S. M. Tavares, and C. Scandian, ‘Influence of heat treatments on the micro-abrasion wear resistance of a superduplex stainless steel’, Wear, vol. 271, no. 9, pp. 1288–1294, 2011, doi: https://doi.org/10.1016/j.wear.2010.12.087.

D. L. Galata et al., ‘Applications of machine vision in pharmaceutical technology: A review’, Eur. J. Pharm. Sci., vol. 159, p. 105717, 2021, doi: https://doi.org/10.1016/j.ejps.2021.105717.

T. Botterill, S. Mills, R. Green, and T. Lotz, ‘Optimising Light Source Positions to Minimise Illumination Variation for 3D Vision’, in 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission, 2012, pp. 222–229, doi: https://doi.org/10.1109/3DIMPVT.2012.13.

H. Herlambang, Z. F. Ikatrinasari, and K. Kosasih, ‘Single-Digit time: Toward a Quick Change-over Process with The SMED method using The Vision System’, Oper. Res. Eng. Sci. Theory Appl., vol. 5, no. 1, pp. 56–68, Apr. 2022, doi: https://doi.org/10.31181/oresta190222076h.

H. Herlambang, ‘Improving Process Capability of The Electronics Component Company Through SMED’, IJIEM - Indones. J. Ind. Eng. Manag., vol. 1, no. 3, pp. 167–175, Oct. 2020, doi: https://doi.org/10.22441/ijiem.v1i3.10236.

T. H. Febriana, H. Herlambang, H. Hernadewita, H. Hasbullah, and A. Halim, ‘Problem-solving step analysis for increasing tire static balance levels: a case study’, J. Sist. dan Manaj. Ind., vol. 5, no. 1, pp. 15–24, Jun. 2021, doi: https://doi.org/10.30656/jsmi.v5i1.3292.

Downloads

Published

2023-12-02

How to Cite

[1]
Z. F. Ikatrinasari, H. . Herlambang, and K. Kosasih, “Design and implementation of intelligent electronic component inspection based on PLC and vision system”, j. sist. manaj. ind., vol. 7, no. 2, pp. 155–168, Dec. 2023.

Issue

Vol. 7 No. 2 (2023): December

Section

Research Article

License

All articles in Jurnal Sistem dan Manajemen Industri can be disseminated provided they include the identity of the article and the source of the article Jurnal Sistem dan Manajemen Industri. The publisher is not responsible for the contents of the article. The content of the article is the sole responsibility of the author

Creative Commons License

Jurnal Sistem dan Manajemen Industri is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC

Most read articles by the same author(s)