Authors : M. Angeline Geetha and R. Suresh Premil Kumar
Abstract: The failure prevention is treated as one of the main enablers of achieving continuous quality improvement in Total Quality Management (TQM) projects. One of the risk-free beverages consumed by the humans is tea. In the study, a method of applying a technique known as the ‘Total Failure Mode and Effects Analysis’ (TFMEA) in tea industry is conceptually investigated. The TFMEA is unaccompanied by any complicated calculations and processes and hence it facilitates illiterate labor of the tea industry to participate in the endeavor of attaining the supreme goal of continuous quality enrichment in tea manufacturing. The underlying motivation of the investigation is to envisage a TFMEA in experimental scrutiny and soft computing technique called Feed Forward Back propagation Neural Network (FFBNN) which can effectively assist various training algorithms. The divergent failure mode contains several modules like the control mode, smoke mode, stewing mode and high fired mode in the tea industry so that the quantity of tea is assessed experimentally. The forecast procedure in the FFBNN is employed to predict the quantities of the tea in failure modes and three training algorithms are employed and the minimum error value of the quantity analyzing process is achieved in the Levenberg-Marquardt (LM) algorithm. From the cheering outcomes, the minimum error of all the failure modes in tea industry is 96.33% determined by the FFBNN process.
M. Angeline Geetha and R. Suresh Premil Kumar, 2016. Effective Estimation of Total Failure Mode Effects and Analysis in Tea Industry. Asian Journal of Information Technology, 15: 4030-4039.