Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Rekayasa Mesin
Ramdhani Yusli Arbain Sugoro
Unknown Affiliation
Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PREDICTION OF LOCATOR PIN ERRORS IN FACING PROCESS USING NEURAL NETWORK AND SUPPORT VECTOR REGRESSION Ramdhani Yusli Arbain Sugoro; Agung Wibowo; Kurniawan Aji Muhammad
Jurnal Rekayasa Mesin Vol. 15 No. 2 (2024)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v15i2.1661

Abstract

Facing is a common process in machining. Facing process error could be the result of a three-pin error in 3-2-1 principle. Measuring pin error is difficult to carry out periodically. This study discusses how to identify pin errors based on the objects deviation after facing using neural network and supporting vector regression. The facing process is carried out on two surfaces. The two processes separately modelled. First case is pin error value prediction based on object deviation at fifteen measuring points. Second case is pin error value prediction based on the deviation of the object at sixteen measuring points and the surface error represented by the pin error in the first case. The performance neural network model for case 1 and case 2 based on R2 score reaches 0.960 and 0.986. The support vector regression model for case 1 and case 2 reaches 0.9485 and 0.9921 of R2 score.
PREDICTION OF LOCATOR PIN ERRORS IN FACING PROCESS USING NEURAL NETWORK AND SUPPORT VECTOR REGRESSION Ramdhani Yusli Arbain Sugoro; Agung Wibowo; Kurniawan Aji Muhammad
Jurnal Rekayasa Mesin Vol. 15 No. 2 (2024)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v15i2.1661

Abstract

Facing is a common process in machining. Facing process error could be the result of a three-pin error in 3-2-1 principle. Measuring pin error is difficult to carry out periodically. This study discusses how to identify pin errors based on the objects deviation after facing using neural network and supporting vector regression. The facing process is carried out on two surfaces. The two processes separately modelled. First case is pin error value prediction based on object deviation at fifteen measuring points. Second case is pin error value prediction based on the deviation of the object at sixteen measuring points and the surface error represented by the pin error in the first case. The performance neural network model for case 1 and case 2 based on R2 score reaches 0.960 and 0.986. The support vector regression model for case 1 and case 2 reaches 0.9485 and 0.9921 of R2 score.
Co-Authors Agung Wibowo Kurniawan Aji Muhammad