Recognition of Quality Control Chart Patterns Based on Back Propagation Neural Network
In the off-line monitoring of the control characteristics of manufactured parts/products has usually been performed by establishing suitable quality control charts. The control charts as maintained would usually show various patterns and the analysis of such patterns would require human expertise so that suitable remedial actions can be taken. In the present paper,a maiden venture has to be taken to recognize various observable quality control chart patterns through the application of back propagation neural network technique. Various patterns like normal,cyclic,increasing/decreasing trend,upward/downward shift etc. have been taken into consideration. Various known patterns have also been generated through the simulation and induction approach. The developed module has been tested and observed to be quite fast,accurate and flexible as it can be able to cope up with varying quality control chart patterns as encountered in real tune manufacturing environment.
Quality Control Charts Neural Network Back Propagation Process Disturbance
Dr.B.K.Bhattacharyya Dr.S.Chakraborty
Mechanical Engineering Department,Bengal Engineering & Science University,Shibpur,Howrah 711103 Department of Production Engineering,Jadavpur University,Kolkata 700032
国际会议
北京
英文
1124-1128
2009-10-21（万方平台首次上网日期，不代表论文的发表时间）