Application of Artificial Neuron Network in roughness prediction: A case study in turning of stainless steel
DOI:
https://doi.org/10.37628/ijcam.v8i1.1423Keywords:
Neuron Network, Artificial Intelligence, Linear regression, Prediction of surface roughness.Abstract
- Artificial neural networks offer a practical and efficient way to choose the best machining parameters for the turning process in order to reduce surface roughness, the resulting cutting forces, and maximize tool life. Surface roughness is a significant aspect in the evaluation of cutting performance and plays a significant role in the manufacturing process. The goal of this project is to create a model based on an Artificial Neural Network that can replicate hard turning of EN19 steel with only a small amount of cutting fluid. In terms of cutting parameters, this model is meant to forecast the surface roughness. Following training with a set of training data for a specified number of cycles, various network topologies are assessed using input/output data sets specifically designated for this purpose. The root means the square error is determined for the selected architectures. Utilizing linear regression, the regression equation is established. And by applying ANN to this equation, we can forecast the surface roughness of test data.
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