Predictions on surface finish in electrical discharge machining based upon neural network models

The objective of the chapter is to present the application of Artificial Neural Network (ANN) modelling of the Electrical Discharge Machining (EDM) process. It establishes the best ANN model by comparing the prediction from different models under the effect of process parameters. In EDM, the motivation is frequently to get better Material Removal Rate (MRR) with fulfilling better surface quality of machined components. The vital requirements are as small a radial overcut with minimal tool wear rate. The quality of a machined surface is very important to fulfilling the growing demands of higher component performance, durability, and reliability. To improve the reliability of the machine component, it is necessary to have in depth knowledge of the effect of parameters on the aforesaid responses of the components. An extensive chain of experiments has been conducted over a wide range of input parameters, using the full factorial design. More than 150 experiments have been conducted on AISI D2 work piece materials using copper electrodes to get the data for training and testing. The additional experiments were obtained to validate the model predictions. The performance of three neural network models is discussed in the evaluation of the generalization ability of the trained neural network. It was observed that the artificial neural network models could predict the process performance with reasonable accuracy, under varying machining conditions.

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Method of complex copper-zinc ore typification using neural network models

MINING INFORMATIONAL AND ANALYTICAL BULLETIN ◽

10.25018/0236-1493-2020-5-0-140-147 ◽

2020 ◽

Vol 5 ◽

pp. 140-147 ◽

Cited By ~ 1

Author(s):

T.N. Aleksandrova ◽

◽

E.K. Ushakov ◽

A.V. Orlova ◽

◽

...

Keyword(s):

Neural Network ◽

Network Models ◽

Neural Network Models ◽

Copper Zinc ◽

Complex Copper

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Digital twin of equipment as a basis for the consumer in digital production

Automation. Modern Techologies ◽

10.36652/0869-4931-2020-74-9-394-402 ◽

2020 ◽

Keyword(s):

Neural Network ◽

Tool Wear ◽

Chip Formation ◽

Network Models ◽

Machining Accuracy ◽

Neural Network Models ◽

Digital Twin ◽

The Neural Network ◽

Digital Production ◽

Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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Universal approximation with error bounds for dynamic artificial neural network models: A tutorial and some new results

2011 IEEE International Symposium on Computer-Aided Control System Design (CACSD) ◽

10.1109/cacsd.2011.6044542 ◽

2011 ◽

Cited By ~ 4

Author(s):

Kwang Ki Kevin Kim ◽

Ernesto Rios Patron ◽

Richard D. Braatz

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Error Bounds ◽

Network Models ◽

Universal Approximation ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

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Bridging the Analytical and Artificial Neural Network Models for Keyhole Formation with Experimental Verification in Laser-melting Deposition: A Novel Approach

Results in Physics ◽

10.1016/j.rinp.2021.104440 ◽

2021 ◽

pp. 104440

Author(s):

Muhammad Arif Mahmood ◽

Andrei C. Popescu ◽

Mihai Oane ◽

Asma Channa ◽

Sabin Mihai ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Experimental Verification ◽

Network Models ◽

Laser Melting ◽

Neural Network Models ◽

Novel Approach ◽

Laser Melting Deposition ◽

Artificial Neural ◽

Artificial Neural Network Models

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Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽

10.3390/en14144242 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4242

Author(s):

Fausto Valencia ◽

Hugo Arcos ◽

Franklin Quilumba

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Finite Element ◽

Power Transformer ◽

Network Models ◽

Activation Function ◽

Neural Network Models ◽

Transformer Winding ◽

Artificial Neural ◽

Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

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