Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy
| Autor: | Elmas Aşkar Ayyıldız, Fuat Kara, Aykut Eser, Mustafa Ayyildiz |
|---|---|
| Přispěvatelé: | [Belirlenecek] |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Materials science Article Subject Artificial neural network Mean squared error Computer Science::Neural and Evolutionary Computation General Engineering 02 engineering and technology 021001 nanoscience & nanotechnology Rprop Stability (probability) Backpropagation 020901 industrial engineering & automation Conjugate gradient method TA401-492 Surface roughness General Materials Science Response surface methodology 0210 nano-technology Materials of engineering and construction. Mechanics of materials Algorithm |
| Zdroj: | Advances in Materials Science and Engineering, Vol 2021 (2021) |
| Popis: | This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg-Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R-2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method. WOS:000621837500001 2-s2.0-85101526619 |
| Databáze: | OpenAIRE |
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