Perencanaan Penggantian dan Perbaikan Komponen Mesin Die Casting Kapasitas 40 Unit/Jam dengan Metode IRRO
DOI:
https://doi.org/10.61132/manufaktur.v3i4.1291Keywords:
Capacity 40 Units/Hour, Component Repair, Die-Casting, Replacement Planning, Tin TerminalAbstract
The decline in the performance of the die casting machine in 1998 after a long period of producing copper terminals showed dimensional defects and instability in product quality, especially in nozzle clogging, reduced copper flow, crust buildup on the gooseneck, plunger movement obstruction, and hydraulic pressure leaks. The purpose of planning the replacement and repair of die-casting machine components is to obtain replacement and repair costs, replacement and repair schedules for the period 2026, and the ratio of maintenance costs to profits. The replacement and repair planning method includes collecting previous maintenance data, applying the inspection-replace-repair-overhaul (IRRO) method, evaluating component conditions, predicting component service life, predicting labor costs, predicting supporting equipment to be used in maintenance, predicting the time to replace spare parts or reinstall repaired components, estimating replacement and repair costs for the period 2026, and calculating the ratio of replacement and repair costs to profits. The planning results obtained replacement and repair costs for the 2026 period are 75.770.000,- IDR with an estimated die casting machine rental rate of 1,500,000 IDR/hour which has the potential to be rented for 1,200 hours/year, and the ratio of maintenance costs to profits is 10,02 % which implies that the die casting machine with a capacity of 40 units/hour is still suitable for use and has the prospect of generating profits for the next few years.
References
Armanda, D. D., Jufriyanto, M., & Rizqi, A. W. (2023). Perencanaan perawatan mesin dengan metode reliability centered maintenance (RCM) pada PT XYZ. G-Tech: Jurnal Teknologi Terapan, 7(4), 1588–1595. https://doi.org/10.33379/gtech.v7i4.3298
Attalla, M. F. N., & Albana, A. S. (2024). Penjadwalan preventive maintenance pada mesin coding printer (studi kasus PT XYZ). Journal of Industrial and Manufacture Engineering, 8(2), 156–167. https://doi.org/10.31289/jime.v8i2.11614
Benhanifia, A., Cheikh, Z., Oliveira, P. M. B., Valente, A., & Lima, J. (2025). Systematic review of predictive maintenance practices in the manufacturing sector. Intelligent Systems with Applications, 26, 200501. https://doi.org/10.1016/j.iswa.2025.200501
Bidari, A. P., & Suseno. (2025). Evaluasi pengaruh autonomous maintenance terhadap kinerja mesin bubut dalam penerapan total productive maintenance di PT XYZ. Jurnal Teknik Mesin, Industri, Elektro dan Informatika, 4(1), 117–125. https://doi.org/10.55606/jtmei.v4i1.4786
Ezeanyim, O., Ewuzie, N., Aguh, P. S., Nwabueze, C., & Nwamekwe, C. O. (2025). Effective maintenance of industrial 5-stage compressor: A machine learning approach. Gazi University Journal of Science Part A: Engineering and Innovation, 12(1), 96–118. https://doi.org/10.54287/gujsa.1646993
Hadi, S. (2019). Perawatan dan perbaikan mesin industri. Andi Offset.
Hadi, S., Azis, A. A., Viyus, V., Puspitasari, E., Firdaus, A. H., & Setiawan, A. (2021). Planning for maintenance and repair of continuous ship unloader using the IRRO. Logic: Journal of Engineering Design and Technology, 21(1), 52–63. https://doi.org/10.31940/logic.v21i1.2383
Huang, W., Zhang, Y., Yin, D., Zuo, B., & Liu, Z. (2021). Urban bus accident analysis: Based on a Tropos goal risk-accident framework considering learning from incidents process. Reliability Engineering & System Safety, 216, 107918. https://doi.org/10.1016/j.ress.2021.107918
Lajarrige, A., Gontard, N., Gaucel, S., & Peyron, S. (2020). Evaluation of the food contact suitability of aged bio-nanocomposite materials dedicated to food packaging applications. Applied Sciences, 10(3), 877. https://doi.org/10.3390/app10030877
Mauluddin, Y., Rahmawati, D., & Oktavianti, D. (2022). Perencanaan pemeliharaan mesin produksi dengan menggunakan total productive maintenance untuk menjamin kestabilan proses produksi. Jurnal Kalibrasi, 20(2), 86–92. https://doi.org/10.33364/kalibrasi/v.20-2.1148
Mesra, T., Kamil, I., & Hadiguna, R. A. (2023). Perawatan preventif mesin pompa air. Journal of Industrial and Manufacture Engineering, 7(2), 236–246. https://doi.org/10.31289/jime.v7i2.10133
Mustaqim, F., Kosasih, W., & Ahmad, A. (2020). Pemeliharaan mesin hydraulic shear menggunakan pendekatan reliability centered maintenance dan manajemen suku cadang. Jurnal Rekayasa Sistem Industri, 9(3), 153–162. https://doi.org/10.26593/jrsi.v9i3.4023
Prasetio, E. T., & Oktora, A. (2024). Evaluation of the effectiveness of die casting machines using overall equipment effectiveness (OEE). Jurnal Teknologi dan Manajemen, 22(1), 99–106. https://doi.org/10.52330/jtm.v22i1.239
Prasetyo, B., & Subagyo, G. (2019). Analisa umur pakai poros pada putaran kritis dengan menggunakan uji defleksi eksperimental (critical speed apparatus). Jurnal Ilmu-Ilmu Teknik, 9, 53–73.
Primawati, P., Qalbina, F., Mulyanti, M., Yanuar, F., Devianto, D., Lapisa, R., & Rozi, F. (2025). Predictive maintenance of old grinding machines using machine learning techniques. Journal of Applied Engineering and Technological Science, 6(2), 874–888. https://doi.org/10.37385/jaets.v6i2.6417
Rakes, D., Arif, M., Setiawan, A., Nasution, K. P., & Prastyo, Y. (2024). Preventive maintenance on CNC machines using the OEE method to reduce downtime at PT MTAT. Jurnal Impresi Indonesia, 3(7), 481–490. https://doi.org/10.58344/jii.v3i7.5116
Ramitsa, Y. A., & Indrawati, C. D. (2025). Analisis dan usulan peningkatan efektivitas mesin centrifugal dengan pendekatan overall equipment effectiveness (OEE), six big losses, dan total productive maintenance (TPM). Widya Teknik, 24(2), 111–119. https://doi.org/10.33508/wt.v24i2.7406
Sastriawan, A. (2024). Penjadwalan pemeliharaan mesin produksi menggunakan reliability centered maintenance. Jurnal Teknologi, 14(1), 26–35. https://doi.org/10.35134/jitekin.v14i1.113
Suryani, F., Syarifa, S. A., & Azhari, A. (2023). Analisis preventive maintenance komponen mesin pulp dengan metode age replacement. Journal of Industrial and Manufacture Engineering, 7(1), 115–125. https://doi.org/10.31289/jime.v7i1.9498
Yang, Z., Baraldi, P., & Zio, E. (2021). A multi-branch deep neural network model for failure prognostics based on multimodal data. Journal of Manufacturing Systems, 59, 42–50. https://doi.org/10.1016/j.jmsy.2021.01.007
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