Assessment of Reliability Factors in Glass Manufacturing plant Using Boolean Algebra and Neural network

In this study a glass manufacturing plant has been considered for its reliability and its cost evaluation by employing algebra of logics and neural networking. Calculations of numerous reliability parameters are presented in this research to analyse the performance of an Industrial glass manufacturing Plant. The development of a seventeen-component model showing the plant’s functioning operation. For short-term and long-term reliability, the model is then created and solved utilising two techniques. In the absence of a repair facility, the Boolean Function Technique is used to analyse algebraic logics and expressions for reliability parameters. Overall system reliability is evaluated in case of weibull and exponential distribution. Additionally, numerical examples were used to calculate MTTF or Mean Time to Failure, which is a key reliability measure. When a repair facility for the failed components was available, the ANN approach was used. To reduce the number of states, the components were grouped into three pieces and depicted as a block diagram. Then, to show the working conditions of these states, a state transition diagram was created. Both approaches were used to calculate numerical examples. For both strategies, the change in profit per unit time was also discussed with a focus to calculate the cost of the manufacturing model using MATLAB which is useful.