Name
Abstract | ||
|---|---|---|
This study presents a novel soft computing procedure based on the application of artificial neural networks, genetic algorithms and identification systems, which makes it possible to optimise the implementation conditions in the manufacturing process of high precision parts, including finishing precision, while saving both time and financial costs and/or energy. This novel intelligent procedure is based on the following phases. Firstly, a neural model extracts the internal structure and the relevant features of the data set representing the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques. This constitutes the model for the fitness function of the production process, using relevant features of the data set. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The proposed novel approach was tested under real dental milling processes using a high-precision machining centre with five axes, requiring high finishing precision of measures in micrometres with a large number of process factors to analyse. The results of the experiment, which validate the performance of the proposed approach, are presented in this study. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neucom.2012.04.033 | Neurocomputing |
Keywords | Field | DocType |
novel soft computing procedure,novel intelligent procedure,relevant feature,manufacturing process,genetic algorithm,finishing precision,high precision part,soft computing technique,high finishing precision,neural model,artificial neural network,dental milling process,soft computing,unsupervised learning | Nonparametric statistics,Machining,Fitness function,Scheduling (production processes),Unsupervised learning,Artificial intelligence,Soft computing,Artificial neural network,Mathematics,Machine learning,Genetic algorithm | Journal |
Volume | ISSN | Citations |
109, | 0925-2312 | 8 |
PageRank | References | Authors |
0.53 | 40 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vicente Vera | 1 | 27 | 5.45 |
| Emilio Corchado | 2 | 2626 | 210.70 |
| Raquel Redondo | 3 | 24 | 4.00 |
| Javier Sedano | 4 | 282 | 43.50 |
| Alvaro Enrique Garcia | 5 | 19 | 3.92 |