Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Jurnal Rekayasa Mesin Journal of Mechanical Engineering and Applied Technology Multidisciplinary Innovations and Research in Applied Engineering
Fariz Wisda Nugraha
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
ANALISIS KINERJA MESIN LASER CUTTING TRULASER 1030 PADA PRODUKSI KENDARAAN KHUSUS MENGGUNAKAN METODE OVERALL EQUIPMENT EFFECTIVENESS (OEE) Fariz Wisda Nugraha; Sendie Yuliarto Margen; Rifky Ismail; Muhammad Farel; Yusuf Subagyo; Hartanto Prawibowo; Rizal Mustofa
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.v3i2.6761

Abstract

The manufacturing industry is continuously challenged to improve production efficiency and quality. Production machinery plays a crucial role in supporting smooth manufacturing processes, especially in the fabrication of special vehicle components. One such machine is the TruLaser 1030, a 2D laser cutting machine manufactured by TRUMPF, which is used for processing body and accessory parts in special vehicles. This study aims to evaluate the performance of the TruLaser 1030 machine using the Overall Equipment Effectiveness (OEE) method, as well as to identify damaged machine components and maintenance requirements. The Total Productive Maintenance (TPM) approach is applied to enhance equipment effectiveness, prevent breakdowns, and ensure product quality and workplace safety. The OEE methodology involves three main parameters: Availability, Performance Rate, and Quality Rate. The results show the machine’s availability at 94%, performance rate at %, and quality rate at 97.1%. These findings suggest that the TruLaser 1030 machine operates at a relatively optimal level; however, regular evaluations of work systems and maintenance programs are necessary to maintain stable performance and continuously improve productivity.
Design, Construction, and Testing of an Electric Wheelchair Operated by Arduino Uno R3 Microcontroller Subagyo, Yusuf; Sendie Yuliarto Margen; Baharudin Priwintoko; Fariz Wisda Nugraha; Hartanto Prawibowo
Multidisciplinary Innovations and Research in Applied Engineering Vol. 2 No. 2 (2025)
Publisher : Akademi Inovasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70935/ha1z3r27

Abstract

The research aims to design and develop an electric wheelchair based on the Arduino Uno microcontroller as a mobility solution for individuals with disabilities. A conventional wheelchair was modified by integrating an electric drive system controlled by an analog joystick, which is connected to the Arduino Uno and DC motors via a BTS 760 motor driver. The wheelchair design complies with ISO 7176-5 standards and is adapted to the anthropometric dimensions of Indonesian users. Test results indicate that the control system functions effectively, allowing responsive control of wheelchair movements forward, backward, left, and right according to joystick operation. However, several challenges were encountered during the chain adjustment and gear welding processes, requiring further development to achieve optimal performance. This study demonstrates that utilizing the Arduino Uno as the central control unit enables the production of an electric wheelchair at a more affordable cost.
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 Baharudin Priwintoko Della Kumalaningrum Hartanto Prawibowo Hutomo Jiwo Satrio Imaduddien Ariefa Muhammad Farel Rieky Handoko Rifky Ismail Sendie Yuliarto Margen Yusuf Subagyo Yusuf Subagyo