Name
Title | ||
|---|---|---|
Neural Network Inverse Model for Quality Monitoring - Application to a High Quality Lackering Process. |
Abstract | ||
|---|---|---|
The quality requirement is an important issue for modern companies. Many tools and philosophies have been proposed to monitor quality, including the seven basic tools or the experimental design. However, high quality requirement may lead companies to work near their technological limit capabilities. In this case, classical approaches to monitor quality may be insufficient. That is why on line quality monitoring based on the neural network prediction model has been proposed. Within this philosophy, the dataset is used in order to determine the optimal setting considering the operating point and the product routing. An inverse model approach is proposed here in order to determine directly the optimal setting in order to avoid defects production. A comparison between the use of a classical multi-inputs multi-outputs NN model and a sequence of different multi-inputs single-output NN models is performed. The proposed approach is tested on a real application case. |
Year | Venue | Field |
|---|---|---|
2017 | IJCCI | Inverse,Monitor quality,Operating point,Computer science,Seven Basic Tools of Quality,Artificial neural network,Reliability engineering |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Thomas | 1 | 78 | 12.59 |
| M. C. Suhner | 2 | 1 | 1.02 |
| Emmanuel Zimmermann | 3 | 2 | 0.71 |
| Hind El Haouzi | 4 | 27 | 3.86 |
| André Thomas | 5 | 111 | 21.26 |
| Mélanie Noyel | 6 | 7 | 2.78 |