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RESEARCH PAPER
Predicting the overall equipment efficiency of core drill rigs in mining using ANN and improving it using MCDM
 
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1
AVS College of Technology, Department: Department of Mechanical Engineering, Chinnagoundapuram, Salem, Tamil Nadu, India
 
2
Department of Mechanical Engineering, Knowledge Institute of Technology, NH544, Kakapalayam, Salem, India
 
3
Department of Electrical Engineering, Annaporana Engineering College, Salem, India
 
 
Submission date: 2023-05-01
 
 
Final revision date: 2023-05-29
 
 
Acceptance date: 2023-07-13
 
 
Online publication date: 2023-07-22
 
 
Publication date: 2023-07-22
 
 
Corresponding author
Kirubakaran Balakrishnan   

AVS College of Technology,Department: Department of Mechanical Engineering, Chinnagoundapuram, Salem, Tamil Nadu, India
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(3):169581
 
HIGHLIGHTS
  • Combined Box Jenkins and artificial neural network model was used to improve Overall Equipment Efficiency of Core Drill rigs.
  • Combined model achieved better prediction of overall equipment effectiveness, compared to auto regressive moving average and non linear auto regressive neural network model.
  • Response surface methodology was found to be effective in optimizing and improving theoverall equipment efficiency.
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ABSTRACT
In this manuscript, an attempt has been made to predict and improve the overall equipment effectiveness of core drill rigs. A combined Box–Jenkins and artificial neural network model was used to develop a three parameter model (drill pushing pressure, drill penetration rate & average pillar drill pit cycle time) for predicting effectiveness. the overall equipment efficiency of core drill rigs. The values of mean average percentage error, root mean square error, normalized root mean square error, men bias error, normalized mean biased error and coefficient of determination values were found to be 9.462%, 17.378%, 0.194, 0.96%, 0.0014 and 0.923. Empirical relationships were developed between the input and output parameters and its effectiveness were evaluated using analysis of variance. For attaining 74.9% effectiveness, the optimized values of pushing pressure, penetration rate and average pillar drill pit cycle time were predicted to be 101.7 bar, 0.94 m/min and 272 min, which was validated. Interactions, perturbations and sensitivity analysis were conducted.
ACKNOWLEDGEMENTS
The authors thank the assistance rendered by M/s. Vikram Engineering Industry, Tiruchirappalli, Tamil Nadu, India for their assistance in modeling and optimization studies.
eISSN:2956-3860
ISSN:1507-2711
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