The Untiring Eye: Automated Visual Inspection System for the Electronics Industry

Hendi Herlambang; Tri Ngudi Wiyatno; Zulfa Fitri Ikatrinasari; Uly Amrina; Kosasih Kosasih

Authors

Hendi Herlambang Universitas Pelita Bangsa
Tri Ngudi Wiyatno
Zulfa Fitri Ikatrinasari
Uly Amrina
Kosasih Kosasih

Keywords:

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

Abstract

To address the escalating complexity of modern quality standards, this study introduces an automated vision inspection system for terminals in electronic components. The system employs a conveyor driven by a stepper motor, a photo-sensor for distance measurement, positioning guides, a vision sensor for quality detection, a cylinder for product ejection, and a PLC for control. Through computer logic, the system accurately differentiates between normal and defective products, segregating the latter into a reject box. Operating at a rate of 60 units per minute, the system demonstrated exceptional performance, achieving 100% accuracy in both vision inspection and cylinder ejection, and a 97.5% success rate in product positioning. The results of this study indicate the great potential of automated inspection systems in improving the efficiency and accuracy of electronic component production processes.

References

H. Herlambang, H. Hardi Purba, Z. F. Ikatrinasari, and K. Kosasih, “Terminals and Connector Inspection Innovation using Machine Vision,” WALUYO JATMIKO PROCEEDING, pp. 581–590, Nov. 2023, doi: 10.33005/wj.v16i1.27.

H. Herlambang, Z. F. Ikatrinasari, and K. Kosasih, “Single-Digit Time : Toward a Quick Change-Over Process With the Single-Digit Time : Toward a Quick Change-Over Process With the Smed Method Using the Vision,” Operational Research in Engineering Sciences: Theory and Applications, no. February, 2022, doi: DOI: https://doi.org/10.31181/oresta190222076h.

H. Herlambang, H. H. Purba, and C. Jaqin, “Development of Machine Vision to Increase the Level of Automation in Indonesia Electronic Component Industry,” Journal Européen des Systèmes Automatisés, vol. 54, no. 2, pp. 253–262, 2021, doi: https://doi.org/10.18280/jesa.540207.

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, pp. 1–12, 2017, doi: 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, no. May 2020, p. 100109, 2021, doi: 10.1016/j.jafr.2021.100109.

N. N. S. Abdul Rahman, N. M. Saad, A. R. Abdullah, M. R. M. Hassan, M. S. S. M. Basir, and N. S. M. Noor, “Automated real-time vision quality inspection monitoring system,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 11, no. 2, pp. 775–783, 2018, doi: 10.11591/ijeecs.v11.i2.pp775-783.

B. Huang et al., “Research and implementation of machine vision technologies for empty bottle inspection systems,” Engineering Science and Technology, an International Journal, vol. 21, no. 1, pp. 159–169, 2018, doi: 10.1016/j.jestch.2018.01.004.

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,” Measurement and Control (United Kingdom), vol. 52, no. 7–8, pp. 804–813, 2019, doi: 10.1177/0020294019847706.

H. Herlambang, “Improving Process Capability of The Electronics Component Company Through SMED,” 2020. [Online]. Available: http://publikasi.mercubuana.ac.id/index.php/ijiem

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: 10.1016/j.ifacol.2019.09.182.

R. Ciobanu, D. Rizescu, and C. Rizescu, “Automatic Sorting Machine Based on Vision Inspection,” International Journal of Modeling and Optimization, vol. 7, no. 5, pp. 286–290, 2017, doi: 10.7763/IJMO.2017.V7.599.

T. Czimmermann et al., “Visual-based defect detection and classification approaches for industrial applications—A SURVEY,” Sensors (Switzerland), vol. 20, no. 5, pp. 1–25, 2020, doi: 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,” Journal Europeen des Systemes Automatises, vol. 54, no. 5, pp. 721–731, Oct. 2021, doi: 10.18280/JESA.540507.

T. H. Febriana, H. Dewita, C. Hermawan, and H. Herlambang, “Perbaikan Ketahanan Lifetime Bladder untuk Peningkatan Curing Efficiency pada Proses Industri Tire Manufacture,” IJIEM (Indonesian Journal of Industrial Engineering & Management), vol. 1, pp. 33–44, 2020, doi: http://dx.doi.org/10.22441/ijiem.v1i1.9258.

Z. F. Ikatrinasari and Kosasih, “control Improving quality process through value stream mapping,” International Journal of Engineering and Technology(UAE), vol. 7, no. 2, pp. 219–225, 2018, doi: 10.14419/ijet.v7i2.29.13321.