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All Journal Jurnal Energi Dan Manufaktur Jurnal Optimasi Sistem Industri INOVTEK POLBENG INSIST (International Series on Interdisciplinary Research) Teknomekanik Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks EDUBIOLOCK Mechanical Engineering for Society and Industry Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) METAL : Jurnal Sistem Mekanik dan Termal Jurnal Polimesin
Agus Sutanto
Jurusan Teknik Mesin, Fakultas Teknik Universitas Andalas Limau Manis, Padang, Sumatera Barat. 25163
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Lean six sigma methodology for waste reduction in ship production Eko Priyanda; Agus Sutanto
Teknomekanik Vol. 6 No. 1 (2023): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v6i1.24172

Abstract

This study was conducted to reduce the amount of waste in ship production and assembly processes at PT ES. Several wastes during the ship production process result in the actual ship completion time being longer than the planning time set by the process planner. Therefore, accurate analysis is required to reduce waste. In this way, contributing factors can be identified, and more effective solutions can be obtained to reduce waste. This is done by implementing the Lean Six Sigma method (DMAIC processes) and several tools and methods, such as Pareto and fishbone diagrams and the FMEA method. The results show that the most critical potential root cause affecting production delays comes from the potential causes with the highest Risk Priority Number (RPN) value. The causes are welders who do not understand the WPS (RPN 432), unstable welding transformers (RPN 432), and unproductive loader movements (RPN 384). The recapitulation of welding defects produced in the production process at a sigma level of 2.48. Recommendations for the three potential critical wastes were made and planned for implementation. The estimated average RPN impairment for the three critical root causes was 32.3%. This condition will impact the total ship production time, which is 6% shorter (equivalent to 14 days) than the previous production time with a new sigma level of 2.55.
Smart vibration sensing and predictive analytics for intelligent textile manufacturing: An IoT-edge and machine learning method Kurnia, Deni; Sutanto, Agus; Fakhrurroja, Hanif; Son, Lovely
Mechanical Engineering for Society and Industry Vol. 5 No. 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.13588

Abstract

This study aimed to propose an IoT-Edge method for detecting vibration abnormalities in Textile Manufacturing, specifically on Draw Texturing Yarn (DTY) machines using an ADXL345 sensor and a Machine Learning Algorithm. The proposed system incorporated wireless sensor nodes, the MQTT protocol, Fast Fourier Transform (FFT) analysis, and a tuned Random Forest (RF) classifier to enable real-time monitoring as well as predictive maintenance. During the analysis, vibration data were collected from 13 spindles, with features extracted in both time as well as frequency domains to distinguish between normal and abnormal machine conditions. The RF model, optimized through hyperparameter tuning, achieved an accuracy of 97%, significantly outperforming the Support Vector Machine (SVM) baseline, which reached 71%. Major results showed the effectiveness of energy and centroid features in fault detection, with the Z-axis vibration proving to be a good indicator of yarn defects. The system presented low latency (average 20.37 ms) in data transmission using the MQTT protocol, ensuring practical deployability. This study offered a scalable and cost-effective solution for industrial vibration monitoring, bridging gaps in real-time processing and seamless IoT incorporation to support predictive maintenance in textile manufacturing.
Co-Authors Adriansyah Adriansyah Akmal Indra Akmal Indra Alfian Alfian Anak Agung Oka Anisau Sholihah Anita Susilawati Berry Yuliandra Deni Kurnia Eko Priyanda Fakhrurroja, Hanif Farchan - Vernando Hendris Prasojo Ismet Hari Mulyadi Lovely Son, Lovely Mahardika, Muladi Muhammad Ardi Muhammad Dani Gitatisna Nilda Tri Putri Nota Effiandi Priyanda, Eko Rika Ampuh Hadiguna Sha'ri Mohd Yusof Tessa Alqifti Wibowo, Nanang Roni Willy Pratama