Monitoring Process Mean and Variability Using Artificial Neural Networks

S.M. Nimbale¹ , V.B. Ghute²

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.3 , pp.153-158, Jun-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i3.153158

Online published on Jun 30, 2019

Copyright © S.M. Nimbale, V.B. Ghute . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In today`s modern market quality of product is most preferable parameter for customers. In order to fulfill the customer requirements manufacturing industries implementing the advanced technology in order to improve and maintain high quality of product. Consequently its real time need that process variation is to be controlled in very advanced approach.The Shewhart chart is usually used to monitor shifts in process mean, whereas, the R and S charts are most widely used in industry to monitor process variability. In this paper, artificial neural network based approach is developed for monitoring the mean and variability of the process. The ARL performance of the proposed approach is evaluated by using simulation and is compared with traditional Shewhart and S charts under normal process distribution. The proposed study indicates that ANN scheme is effective than and S charts for monitoring the process mean and variability.

Key-Words / Index Term :
Artificial neural network; Statistical process control; X chart; S chart; Average run length

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