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Forward and reverse mapping for milling process using artificial neural networks

The data set presented is related to the milling process of AA6061-4.5%Cu-5%SiCp composite. The data primarily concentrates on predicting values of some machining responses, such as cutting force, surface finish and power utilization utilizing using forward back propagation neural network based appr...

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Detalles Bibliográficos
Autores principales: Malghan, Rashmi L., M C, Karthik Rao, Shettigar, Arun Kumar, Rao, Shrikantha S., D’Souza, R.J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5694959/
https://www.ncbi.nlm.nih.gov/pubmed/29188231
http://dx.doi.org/10.1016/j.dib.2017.10.069
Descripción
Sumario:The data set presented is related to the milling process of AA6061-4.5%Cu-5%SiCp composite. The data primarily concentrates on predicting values of some machining responses, such as cutting force, surface finish and power utilization utilizing using forward back propagation neural network based approach, i.e. ANN based on three process parameters, such as spindle speed, feed rate and depth of cut.The comparing reverse model is likewise created to prescribe the ideal settings of processing parameters for accomplishing the desired responses as indicated by the necessities of the end clients. These modelling approaches are very proficient to foresee the benefits of machining responses and also process parameter settings in light of the experimental technique.