TY - GEN
T1 - Automatic Tungsten Inert Gas (TIG) welding using machine vision and neural network on material SS304
AU - Baskoro, Ario Sunar
AU - Tandian, Randy
AU - Haikal,
AU - Edyanto, Andreas
AU - Saragih, Agung Shamsuddin
N1 - Funding Information:
The author would like to express his sincere gratitude for the financial support of Directorate Research and Public Service, Universitas Indonesia through the contract number: 1753/UN2.R12/PPM.00.00/2016 with title of "Pengembangan Mesin Tungsten Inert Gas Welding Otomatis Berbasis Machine Vision dan Neural Network".
Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/6
Y1 - 2017/3/6
N2 - Welding is a process of joining two or more substances that are based on the principles of diffusion processes, resulting in unification on the materials to be joined. The strength of the weld joint is determined by several parameters, including the weld bead width and the penetration. The width of the weld bead especially the upper part can be determined by looking directly through the CCD (Charge-Coupled Device) camera. But it is difficult to observe the back bead width directly since in practice it is impossible to install the CCD camera at the bottom of specimen. In this paper, Tungsten Inert Gas (TIG) Welding with the welding speed is controlled by the microcontroller for the purpose of adjusting the back bead width has observed. The back bead width is estimated based on data of weld bead width obtained from machine vision, welding speed, and currents that used in this experimental. It's used to obtain a series of data which would have conducted as initial experiments to train and build the neural network system. Results showed that the back bead width is 3 mm on the current 55 A, 60 A, and 65 A have an average error of each current of 0.11 mm, 0.09 mm, and 0.12 mm.
AB - Welding is a process of joining two or more substances that are based on the principles of diffusion processes, resulting in unification on the materials to be joined. The strength of the weld joint is determined by several parameters, including the weld bead width and the penetration. The width of the weld bead especially the upper part can be determined by looking directly through the CCD (Charge-Coupled Device) camera. But it is difficult to observe the back bead width directly since in practice it is impossible to install the CCD camera at the bottom of specimen. In this paper, Tungsten Inert Gas (TIG) Welding with the welding speed is controlled by the microcontroller for the purpose of adjusting the back bead width has observed. The back bead width is estimated based on data of weld bead width obtained from machine vision, welding speed, and currents that used in this experimental. It's used to obtain a series of data which would have conducted as initial experiments to train and build the neural network system. Results showed that the back bead width is 3 mm on the current 55 A, 60 A, and 65 A have an average error of each current of 0.11 mm, 0.09 mm, and 0.12 mm.
KW - TIG welding
KW - machine vision
KW - microcontroller
KW - molten pool
KW - neural network
KW - welding
UR - http://www.scopus.com/inward/record.url?scp=85014102054&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS.2016.7872739
DO - 10.1109/ICACSIS.2016.7872739
M3 - Conference contribution
AN - SCOPUS:85014102054
T3 - 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
SP - 427
EP - 432
BT - 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
Y2 - 15 October 2016 through 16 October 2016
ER -