International Journal of Industrial Optimization (IJIO)
Vol. 3 No. 2 (2022)

Analysis of sag mill machine performance using overall equipment effectiveness and failure model and effects analysis method

Sinta Luluk Fuadiya (Univeritas Pembangunan Nasional Veteran Jawa Timur)
Endang Pudji Widjajati (Universitas Pembangunan Nasional Veteran Jawa Timur)



Article Info

Publish Date
30 Dec 2022

Abstract

The mining company uses a variety of grinding machines to process minerals, whereas the most common type of machine is the Semi-Autogenous Grinding SAG Mill machine. This machine is employed for the mining process of hard rock as raw material into gold, copper, and silver. However, the SAG Mill machines are often broken, even suddenly not working, with an average loss time of 97.30 hours which impacts a decrease in efficiency and production quality of up to 40%. It can cause losses that do not reach the production target. This research aims to measure the effectiveness of the SAG Mill machine and determine the failure using the OEE and FMEA methods. The results showed that the SAG Mill machine is still under standardized based on the Japan Institute of Plant Maintenance (JIPM), which is 85%. The FMEA method and RPN value apply to analyze downtime losses, and idling is the loss that highly affects the effectiveness of SAG Mill machines. Recommendations for the company are to increase the number of equipment that aims to prolong the machine's age and accelerate production. This research contributes to another solution to help maintenance managers by measuring the effectiveness and determining the failure of the SAG Mill machine

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Journal Info

Abbrev

ijio

Publisher

Subject

Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

Description

The Journal invites original articles and not simultaneously submitted to another journal or conference. The whole spectrums of Industrial Engineering are welcome but are not limited to Metaheuristics, Simulation, Design of Experiment, Data Mining, and Production System. 1. Metaheuristics: ...