| Hybrid Incremental Modeling Based on Least Squares and Fuzzy $K$-NN for Monitoring Tool Wear in Turning Processes |
| admin |
| Rodolfo Haber |
| Agustín Gajate |
| Raúl del Toro |
|
2012-11-01 |
https://doi.org/10.1109/TII.2012.2205699 |
|
In IEEE Transactions on Industrial Informatics 8, no.4 (November 2012) |
There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model. |
|
true |
|
|
|
|
|
|
|
|
| caption |
focal_point |
preview_only |
|
|
false |
|
|
|
