Artificial Neural Networks as a Means for Making Process Control Charts User Friendly

The paper discusses usability of various pattern recognition methods, especially based on artificial neural networks for decision making support in process control chart analysis. Their effectiveness for detecting process instability is compared with the effectiveness of a human operator and of a widely accessed commercial statistical software. The results are verified on the basis of data obtained from real production processes.

[1]  Beata Starzyńska,et al.  Excellence toolbox: Decision support system for quality tools and techniques selection and application , 2013 .

[2]  Izabela Rojek.  Neural Networks as Prediction Models for Water Intake in Water Supply System , 2008, ICAISC.

[3]  A. Erhan Mergen,et al.  Using SPC in Conjunction with APC , 2011 .

[4]  S. A. Lesany,et al.  Recognition and classification of single and concurrent unnatural patterns in control charts via neural networks and fitted line of samples , 2014 .

[5]  Claus Weihs,et al.  Monitoring a Drilling Process Using Residual Control Charts , 2008 .

[6]  zhiqiang cheng.  Mean Shifts Diagnosis and Identification in Bivariate Process Using LS-SVM Based Pattern Recognition Model , 2013 .

[7]  Somkiat Tangjitsitcharoen,et al.  Integration of in-process monitoring and statistical process control of surface roughness on CNC turning process , 2013, Int. J. Comput. Integr. Manuf..

[8]  Lifeng Xi,et al.  Using an MQE chart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes , 2008 .

[9]  Izabela Rojek.  Technological process planning by the use of neural networks , 2017, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[10]  Chi-Jie Lu,et al.  Integrated Application of SPC/EPC/ICA and neural networks , 2008 .

[11]  Dennis K. J. Lin,et al.  Monitoring the Slopes of Linear Profiles , 2009 .

[12]  I. Rojek,et al.  Comparison of different types of neuronal nets for failures location within water-supply networks , 2014 .

[13]  Anna Burduk.  Artificial Neural Networks as Tools for Controlling Production Systems and Ensuring Their Stability , 2013, CISIM.

[14]  Eamonn Murphy,et al.  Process Visualization in Medical Device Manufacture: an Adaptation of Short Run SPC Techniques , 2013 .

[15]  Shing I. Chang,et al.  Statistical Process Control for Monitoring Nonlinear Profiles: A Six Sigma Project on Curing Process , 2012 .

[16]  A. Hamrol,et al.  Nowa metoda analizy kart kontrolnych procesu , 2006 .

[17]  Adam Hamrol,et al.  Assessment of ductile iron casting process with the use of the DRSA method , 2016 .

[18]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .