In statistical process control, change point estimation is an essential requirement for diagnosing the source of a deviation when a process is out of control. In this study, an ANN- based method is proposed to estimate the change point in the multivariate normal process which is subjected to covariance variation. Since in a physical system parameter is correlated, correlation is kept constant to obtain realistic simulated data. Employing statistical simulation, different out of control scenarios are simulated and statistics are calculated for each scenario. This study is to predict the change point in the control chart using the simulated set and corresponding statistical sets, an ANN is adopted. The resulting model achieved a high accuracy of 90% in training and 80% testing with a reasonable level of confidence in the prediction. Also, results show that Bayesian reaches a higher accuracy than Levenberg in ANN training.
This paper aims to examine the Total Productive Maintenance (TPM) as the significant technique to improve the maintenance management of production equipment. Attempt was made to discuss the available literature related to existing techniques in maintenance management, particularly on breakdown maintenance (BM), preventive maintenance (PM), predictive maintenance (PdM) or condition-based maintenance (CBM), reliability-centered maintenance (RCM), computerized maintenance management system (CMMS) and TPM. The outcomes from these reviews would serve as useful guidelines for the future research in maintenance management. This review justifies TPM as a comprehensive technique to improve the flaw in maintenance management. Notably, TPM encompasses of all elements applied by other maintenance methodology, from tools and techniques to involvement of all operational hierarchical in the organization as what is very much required in manufacturing organization is to integrate different functional areas in a coherent manner.
This paper describes an overview of Fuzzy Logic (FL) application for solving machining problems. The developed fuzzy prediction model is an essential operational guideline for machinist in decision making and adjusting process parameters. This paper also discussed the previous literature that applied the FL in modeling machining process.
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