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All Journal Jurnal Polimesin ASEAN Journal for Science and Engineering in Materials
Chang, Steven Henderson
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Mechanical Design and Analysis of Eco-Print Textile Pounding Machine Reflin, Rhainna Rheizkhira; Chang, Steven Henderson; Saptaji, Kushendarsyah; Triawan, Farid
ASEAN Journal for Science and Engineering in Materials Vol 2, No 2 (2023): AJSEM: Volume 2, Issue 2, September 2023
Publisher : Bumi Publikasi Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study presents the design and analysis of an automated eco-print textile pounding machine to reduce labor intensity and preserve artistic aspects. The machine offers an efficient and cost-effective solution for businesses to meet the demand for eco-friendly textiles while maintaining control over the pounding technique. By utilizing a rotating flywheel mechanism, the device achieves approximately an average of 5 pounds with a force of 8.54 N per second. It features unique characteristics, including disassembly capability and replaceable parts for easy maintenance and longevity. The safety analysis indicates favorable safety factor values of 5.68 and 4.70 for static and dynamic loads, respectively, in the most critical part. Based on these results, it can be concluded that this product is safe and has a predicted infinite lifespan. This study serves as a valuable reference for the development of enhanced eco-print textile pounding machines.
Machine fault detection through sound analysis using MFCC and machine learning Chang, Steven Henderson; Purnomo, Ariana Tulus; Bhakti, Muhammad Agni Catur; Mulia, Vania Katherine; Rizky, Agyl Fajar; Fernandez, Nikolas Krisma Hadi; Triawan, Farid
Jurnal Polimesin Vol 23, No 3 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i3.6653

Abstract

This study addresses the need for automated damage and failure detection in industrial machinery through sound analysis and machine learning. Traditional methods rely on human experts to identify faults using microphones, which can be time-consuming, stressful, and prone to errors such as limited perception, subjectivity, and inconsistency. This study leverages machine learning to create a more objective and efficient alternative. Mel-Frequency Cepstral Coefficients (MFCCs) were employed for feature extraction, capturing intricate sound patterns associated with machinery faults. Through rigorous experimentation, 11 MFCC coefficients were identified as optimal. The Support Vector Machine (SVM) emerged as the best-performing classifier compared to LightGBM and XGBoost, achieving a training accuracy of 83.12% and testing accuracy of 82.50%. The dataset was split between 80% for training and 20% for testing. The small gap between training and testing accuracy indicates an ideal model with no signs of over fitting, under fitting, or data leakage. Real-world simulations validated the model’s efficacy under various operational scenarios, demonstrating its readiness for industrial deployment. This study highlights the effectiveness of sound analysis and SVM classification in proactive maintenance, offering a reliable tool to reduce downtime and maintenance costs while enhancing operational efficiency and reliability.
Co-Authors Ariana Tulus Purnomo Fernandez, Nikolas Krisma Hadi Kushendarsyah Saptaji Muhammad Agni Catur Bhakti Mulia, Vania Katherine Reflin, Rhainna Rheizkhira Rizky, Agyl Fajar Triawan, Farid