Program Studi Teknologi Rekayasa Mekatronika 2016
Mahasiswa: Arya Rayfanza Putra, Rangga Aprilia Putra Pratama
Pembimbing: M. W. Resi Bagus Panuntun, M.T.
ABSTRAK/ABSTRACT
Industri pestisida membutuhkan proses pengemasan yang aman dan konsisten, terutama pada bagian tutup botol. Pemeriksaan tutup botol yang dilakukan secara manual masih memiliki risiko kesalahan karena bergantung pada ketelitian operator. Oleh karena itu, dibuat mesin cap inspection untuk membantu proses pemeriksaan tutup botol pestisida secara otomatis. Tujuan dari tugas akhir ini adalah merancang dan membangun sistem inspeksi yang dapat mendeteksi kondisi tutup botol OK dan NG berdasarkan hasil pembacaan kamera.
Metode pengerjaan dilakukan melalui perancangan sistem elektrik, pembuatan program PLC, perancangan tampilan HMI, pengaturan kamera, serta pengujian mesin. Sistem menggunakan kamera Keyence, sensor, PLC Siemens, HMI, conveyor, dan rejector. Hasil pengujian menunjukkan bahwa kamera dapat mengidentifikasi beberapa kondisi NG, yaitu tutup botol miring, ring rusak, ring tidak ada, dan nama Syngenta tidak terbaca. Pada hasil trial, mesin membaca 100 produk dengan 87 good product dan 13 reject product. Berdasarkan hasil tersebut, sistem dapat membantu proses inspeksi dan menampilkan hasil pemeriksaan melalui HMI.
Kata Kunci: HMI, Kamera, PLC, Rejector, Sensor
The pesticide industry requires a safe and consistent packaging process, especially in the bottle cap area. Manual bottle cap inspection still has a risk of error because the result depends on operator accuracy. Therefore, a cap inspection machine was developed to help inspect pesticide bottle caps automatically. The purpose of this final project is to design and build an inspection system that can detect OK and NG bottle cap conditions based on camera readings.
The work method included electrical system design, PLC programming, HMI display design, camera setting, and machine testing. The system uses Keyence cameras, sensors, Siemens PLC, HMI, conveyor, and rejector. The test results show that the camera can identify several NG conditions, including a tilted cap, damaged ring, missing ring, and unreadable Syngenta name. In the trial result, the machine read 100 products, with 87 good products and 13 reject products. Based on these results, the system can support the inspection process and display the inspection result through the HMI.
Keywords: Camera, HMI, PLC, Rejector, Sensor
DAFTAR PUSTAKA
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed. New York: Pearson.
E. R. Davies, Computer and Machine Vision: Theory, Algorithms, Practicalities, 5th ed. London: Academic Press, 2018.
J. Beyerer, F. P. León, and C. Frese, Machine Vision: Automated Visual Inspection, Theory, Practice and Applications. Berlin: Springer, 2016.
A. Hornberg, Handbook of Machine Vision. Weinheim: Wiley-VCH, 2006.
W. Bolton, Programmable Logic Controllers, 6th ed. Oxford: Newnes, 2015.
E. A. Parr, Programmable Controllers: An Engineer’s Guide, 3rd ed. Oxford: Newnes.
M. P. Groover, Automation, Production Systems, and Computer-Integrated Manufacturing, 4th ed. New Jersey: Pearson, 2015.
A. Wijayono, Irwan, and V. G. V. Putra, “Implementation of Digital Image Processing and Computation Technology on Measurement and Testing of Non Woven Fabric Parameters,” arXiv, 2018.
S. J. Shetty, “Vision-based Inspection System Employing Computer Vision and Neural Networks for Detection of Fractures in Manufactured Components,” arXiv, 2019.
M. Ferguson, R. Ak, Y. T. T. Lee, and K. H. Law, “Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning,” arXiv, 2018.
J. Theis, I. Mokhtarian, and H. Darabi, “Process Mining of Programmable Logic Controllers: Input/Output Event Logs,” arXiv, 2019.
Siemens AG, “SIMATIC S7-1200 Programmable Controller System Manual,” Siemens Industry Documentation, 2024.
Keyence Corporation, “IV4 Series Vision Sensor User Manual,” Keyence Corporation, 2024.