Pengamatan Cacat Material Aluminium 6061 Proses Pemotongan Laser Menggunakan ESP32CAM

Rizki Aulia Nanda; Karyadi Karyadi; Tukino Tukino; Ade Suhara; Muhammad Nuzan Rizki; Muhammad Faiz Ramadhan; Khafid Khaulsar Akmal

doi:10.30630/jtm.17.2.1609

Rizki Aulia Nanda Prodi Teknik Mesin, Fakultas Teknik, Universitas Buana Perjuangan Karawang
Karyadi Karyadi Prodi Teknik Mesin, Fakultas Teknik, Universitas Buana Perjuangan Karawang
Tukino Tukino Prodi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Buana Perjuangan Karawang
Ade Suhara Prodi Teknik Industri, Fakultas Teknik, Universitas Buana Perjuangan Karawang
Muhammad Nuzan Rizki Prodi Teknik Mesin, Fakultas Teknik, Universitas Malikussaleh
Muhammad Faiz Ramadhan Prodi Teknik Mesin, Fakultas Teknik, Universitas Buana Perjuangan Karawang
Khafid Khaulsar Akmal Prodi Teknik Mesin, Fakultas Teknik, Universitas Buana Perjuangan Karawang

DOI: https://doi.org/10.30630/jtm.17.2.1609

Keywords: ESP32CAM, laser cutting , openCV, color segmentation

Abstract

Using high-pressure and high-temperature laser light emission techniques, laser cutting works to cut materials in such a way that components at the laser cutting point produce cutting results. However, the current problem is that laser cutting often produces material defects including protruding parts, burnt surfaces, and pores on the laser-cut parts. Given this problem, the purpose of this study is to develop a camera capable of identifying material defects caused by laser cutting . The research method is the preparation of 6061 aluminum material, the preparation of the ESP32CAM camera, and the preparation of laser cutting parameter settings. In order to run the ESP32CAM program, C and OpenCV programming languages are needed to identify items with material defects, color images, histograms, and FPS are needed. The results of the study showed that detecting defects at the highest FPS reading of 15.57 and a histogram value of 250 at the x coordinate and 950000 at the y coordinate. Eight defects in the material were found using Open CV detection on the ESP32CAM camera sample 3. From this technique it can be concluded that ESP32CAM is capable of identifying material defects caused by laser cutting.

References

S. Marimuthu, J. Dunleavey, Y. Liu, M. Antar, and B. Smith., 2019. Laser cutting of aluminium-alumina metal matrix composite. Opt. Laser Technol., 117 (4), pp.251–259.

H. Cha et al., 2017. An Efficient, ‘Burn in’ Free Organic Solar Cell Employing a Nonfullerene Electron Acceptor. Adv. Mate, 29, (33), pp.1–8.

Y. Zong et al., 2021. An intelligent and automated 3D surface defect detection system for quantitative 3D estimation and feature classification of material surface defects. Opt. Lasers Eng, 144, (11), pp.106633.

P. Promoppatum and S. C. Yao., 2019. Analytical evaluation of defect generation for selective laser melting of metals. Int. J. Adv. Manuf. Technol, 103 (1–4), pp.1185–1198.

R. Usamentiaga and D. F. Garcia., Multi-camera calibration for accurate geometric measurements in industrial environments. Meas. J. Int. Meas. Confed, 134 (3), pp.345–358.

Y. V. Chiryshev, A. V. Kruglov, A. S. Atamanova, and S. G. Zavada., 2017. Detection and dimension of moving objects using single camera applied to the round timber measurement. Proc. 2017 Fed. Conf. Comput. Sci. Inf. Syst. FedCSIS, 11, pp.49–56.

D. Bacioiu, G. Melton, M. Papaelias, and R. Shaw., 2019. Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural network. J. Manuf. Process, 45 (7), pp.603–613.

A. M. Grillet, A. C. B. Bogaerds, G. W. M. Peters, F. P. T. Baaijens, and M. Bulters., 2002. Numerical analysis of flow mark surface defects in injection molding flow. J. Rheol. (N. Y. N. Y),46 (3), pp.651–669.

A. Setiawan and A. Irma Purnamasari., 2019. Pengembangan Passive Infrared Sensor (PIR) HC-SR501 dengan Microcontrollers ESP32-CAM Berbasiskan Internet of Things (IoT) dan Smart Home sebagai Deteksi Gerak untuk Keamanan Perumahan. Prosisiding Semin. Nas. SISFOTEK (Sistem Inf. dan Teknol. Informasi), 3 (1), pp.148–154.

M. Jaimez, C. Kerl, J. Gonzalez-Jimenez, and D. Cremers., 2017. Fast odometry and scene flow from RGB-D cameras based on geometric clustering. Proc. - IEEE Int. Conf. Robot. Autom, pp. 3992–3999.

X. Zhang, J. Saniie, and A. Heifetz., 2020. Detection of Defects in Additively Manufactured Stainless Steel 316L with Compact Infrared Camera and Machine Learning Algorithms. Jom, 72 (12), pp.4244–4253.

S. Vinod, P. Shakor, F. Sartipi, and M. Karakouzian., 2023. Object Detection Using ESP32 Cameras for Quality Control of Steel Components in Manufacturing Structures. Arab. J. Sci. Eng, 48 (10), pp.12741–12758.