Abstract
In the recent trends, production plants in the automobile industries all over the world are facing a lot of challenges to achieve better productivity and customer satisfaction due to increasing the passenger’s necessity and demand for transportation. In this direction, the belt, tyre, and tube manufacturing plants act as vital roles in the day-to-day life of the automobile industries. Tyre production plant comprises five major units, namely, raw material selection, preparation, tyre components, finishing, and inspection. The main purpose of this research is to implement the new method to predict the most critical subsystems in the tyre manufacturing system of the rubber industry. As mathematically, any one maintenance parameter among reliability, availability, maintainability, and dependability (RAMD) parameters is evaluated to identify the critical subsystems and their effect on the effectiveness of the tyre production system. In this research, the effect of variation in maintenance indices, RAMD, is measured to identify the critical subsystem of the tyre production system based on the mathematical modeling Markov birth-death approach (MBDA), and the equations of the subsystems are derived by using the Chapman–Kolmogorov method. Besides, it also calculates the performance of certain maintenance parameters concerning time such as mean time between failures (MTBF), mean time to repair (MTTR), and dependability ratio for each subsystem of the tyre production system. Finally, RAMD analysis of the tyre production systems has been executed for predicting the most critical subsystem by changing the rates of failure and repair of individual subsystems with the utilization of MATLAB software. RAMD analysis reveals that the subsystem bias cutting is most critical with the minimum availability of 0.8387, dependability 5.19, dependability ratio 0.8701, and maximum MTTR 38.46 hours of the subsystem. In this implementation of the proposed method, a real-time case study of the industrial repairable system of tyre manufacturing system has been taken for evaluating RAMD indices of the production plant of rubber industry cited in the southern region of Tamil Nadu, India.
Highlights
In the recent trends, in the last few decades, logistics and transportation have rapidly increased throughout the world due to the customers or passenger’s necessity, and the population explosion
The prediction of the critical subsystems, machines, and their components is an essential activity for a better maintenance management system in the industry. e effectiveness and performance of the production system can be obtained by identifying the critical subsystems, machines, and components earlier. e important maintenance parameters, RAMD, of the manufacturing system are evaluated with different selections and combinations of repair, failure rates of the individual manufacturing machines, and their components in the shop
The availability of the subsystem will increase from 0.59 to 1.36% with an increased failure rate of the subsystem, and the mean time to repair (MTTR) will decrease by 4.87% approximately. e graphical representation of the availability changes in the subsystem Bias cutting (BC) concerning the rate of failure and repair is shown in Figure 8. e Av variation of the subsystem BC is entirely different from all other subsystems due to the sudden up and down values of Av changes
Summary
In the last few decades, logistics and transportation have rapidly increased throughout the world due to the customers or passenger’s necessity, and the population explosion. Is section presents the critical overview of the various published research articles based on the RAMD analysis of the different manufacturing systems, applications, challenges, and opportunities of mathematical modeling in the industries. Performance evaluation of the milk production industry is described, and the reliability and availability of the milk production systems are analyzed through the application of the MINITAB software package with the different mathematical analysis techniques of RAM engineering. RAM analysis of the reciprocating compressor in the oil and gas refinery industry has been described, and the operational performance of the compressor has been identified based on the genetic failure database with the application of the mathematical method tools and engineering techniques such as MTBF and availability of the equipment in the oil and gas industry. Reliability-centred maintenance (RCM) and redundancy allocation problem (RAP) have been discussed, and the maintenance scheduling of the repairable subsystems is analyzed through the application of three different metaheuristic algorithms such as nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), and multiobjective firefly algorithm (MOFA) for implementing the novel integrated model to optimize the RCM and RAP in the industrial applications [5]
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