Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Rekayasa Mesin Journal of Mechanical Engineering and Applied Technology
Imaduddien Ariefa
Unknown Affiliation
Automotive Engineering Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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

Found 2 Documents
Search

 1 
OPTIMASI DIAMETER LAS TITIK MENGGUNAKAN METODE TAGUCHI PADA PROSES RESISTANCE SPOT WELDING ROBOTIK DI MESIN OTC DAIHEN Roni, Komar; Imaduddien Ariefa; Rieky Handoko; Anton Harseno; Muhammad Nanang Adi Saputra; Sahal Ahmad Albab
Journal of Mechanical Engineering and Applied Technology Vol. 3 No. 2 (2025): VOLUME 3 ISSUE 2 YEAR 2025 (JULY 2025)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jmeat.v3i2.6735

Abstract

Robotic welding technology has become a key component in modern manufacturing, particularly in automotive industries, due to its ability to deliver consistent weld quality and improve efficiency. However, weld quality remains highly dependent on process parameters such as current (Ampere), voltage, electrode movement speed, and heat input. In the context of resistance spot welding (RSW), optimizing these parameters is essential to achieve ideal weld diameters and ensure joint integrity. Previous research has primarily focused on tensile strength, overlooking diameter as a crucial quality metric in precision manufacturing. This study addresses that gap by investigating the influence of four welding parameters—current, voltage, speed, and heat input—on weld diameter using the Taguchi method. An experimental design with L8 orthogonal array was implemented to reduce the number of trials while maintaining robust analysis. The quality characteristic "Smaller is Better" was used to align with industrial diameter standards of 15–16 mm. Signal-to-noise (SN) ratios and ANOVA were applied to identify the most influential factors. The optimal parameter combination was found to be current at 180 A, voltage at 18 V, speed at 80 cm/min, and heat input at 0.2 KJ/mm. Confirmation experiments yielded an average weld diameter of 15.8125 mm, validating the Taguchi prediction and demonstrating the method’s effectiveness in minimizing diameter variation. These findings confirm that all four parameters significantly affect weld diameter and that Taguchi-based optimization can effectively enhance weld quality and manufacturing efficiency
Performance Enhancement of 2D CNN-Based Visual Inspection Using Data Augmentation for Defect Classification in Metal Casting Products Imaduddien Ariefa; Hutomo Jiwo Satrio; Della Kumalaningrum; Rieky Handoko; Anton Harseno; Fariz Wisda Nugraha
Jurnal Rekayasa Mesin Vol. 20 No. 3 (2025): Volume 20, Nomor 3, Desember 2025
Publisher : Mechanical Engineering Department - Semarang State Polytechnic

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jrm.v20i3.7170

Abstract

Deep learning-based automated visual inspection has become increasingly important for reducing the subjectivity and mistakes that come with manual inspection.  However, when the image dataset is small, Convolutional Neural Networks (CNN) often do not perform optimally because the model overfits and fails to generalize effectivelyl.  This study investigates the effect of data augmentation on enhancing the performance of an AlexNet-based CNN model for classifying defect and non-defect casting images.  There were 13266 grayscale images in total, and they were divided into two groups: defect and non-defect.  To increase data variability, several augmentation techniques were used, such as rotation, flipping, zooming, and brightness adjustment.  We evaluated two different training scenarios: training a model without adding anything and training a model with adding something.  We used accuracy, precision, recall, F1-score, validation loss, and confusion matrix analysis to evaluate model perfomance.  The findings demonstrate that data augmentation significantly improves model performance.  The validation loss decreased from 0.019747 to 0.014853, and the accuracy, precision, recall, and F1-score all showed slight improvements.  The enhanced model also achieved higher true positive and true negative values, signifying improved recognition proficiency.  Tests on previously unseen samples yielded 100% correct predictions, indicating enhanced generalization.  To sum up, data augmentation is an effective strategy for mitigating small datset limitations and improving the reliability of CNN-based visual inspection systems in industrial environments.
Co-Authors Anton Harseno Della Kumalaningrum Fariz Wisda Nugraha Hutomo Jiwo Satrio Komar Roni Muhammad Nanang Adi Saputra Rieky Handoko Sahal Ahmad Albab