Name
Title | ||
|---|---|---|
Neural-fuzzy statistical process control (NF-SPC) application for manufacturing quality management |
Abstract | ||
|---|---|---|
An alternative approach to the manufacturing process quality control traditional methods, such as X-R charts, is presented in this paper extending the results of S.I Chang and CA. Aw(1). By using both Artificial Neural Networks and Fuzzy Methodology, we have built up a system capable of achieving two fundamental goals regarding process quality control. On one hand, this system immediately detects inadmissible deviation of the mean and the process variability, and on the other hand, it allows to interpret correctly signals that may be misunderstood with the X-R chart. That is the case in certain situations in which however the process is working with the appropriate quality level, measures indicate the contrary. |
Year | Venue | Keywords |
|---|---|---|
2004 | IC-AI '04 & MLMTA'04 , VOL 1 AND 2, PROCEEDINGS | SPC,quality control,neural nets,fuzzy classifier |
Field | DocType | Citations |
Applied engineering,Computer science,Fuzzy logic,Manufacturing quality,Manufacturing engineering,Statistical process control,Manufacturing execution system,Quality assurance | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Parreno | 1 | 66 | 8.66 |
| Paolo Priore | 2 | 147 | 17.02 |
| Nazario Garcia | 3 | 8 | 4.57 |
| José L. Herrero | 4 | 0 | 0.34 |
| María Mitre | 5 | 5 | 1.57 |