Complexity / 2017 / Article / Tab 4

Research Article

Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization

Table 4

Main parameters of each modeling strategy considered in this study.

AlgorithmsBN ANN HIM + SA

ModelSeven inputs
(Fr, fz, D, ae, HB, Geom, rpm) 
Output: Ra
Seven inputs: (Fr, fz, D, ae, HB, Geom, rpm)
Output: Ra
Seven inputs
(Fr, fz, D, ae, HB, Geom, rpm)
Output: Ra

ClusteringNo (TAN)No (BP)Fuzzy k-NN

StructureThree layers (4-11-4 neurons)Tree-like structureSecond-order polynomial, two neighbors, and 1.92 for fuzzy strength parameter

InferenceGlobalCausal and abductiveGlobal and local

Main parameters to adjustNo tuning parametersMany (MLP)m, k, p, optimal setting by SA

Training algorithmsNo (estimation of conditional probability)BPLSE + IBL

InterpretabilityYesNoPartially

BN: Bayesian network; ANN: artificial neural network; HIM + SA: hybrid incremental model with simulated annealing; TAN: tree augmented Naive-Bayes; BP: backpropagation; MLP: multilayer perceptron; LSE: least square error; IBL: instance based learning.