Name: HUGO DOS ANJOS SANTOS
Publication date: 13/12/2024
Examining board:
Name |
Role |
|---|---|
| JULIO CESAR SAMPAIO DUTRA | Coorientador |
| LUIZ ALBERTO DA SILVA ABREU | Examinador Externo |
| MARCELO CAMARGO SEVERO DE MACEDO | Examinador Interno |
| WELLINGTON BETENCURTE DA SILVA | Presidente |
Summary: The prediction of temperature distribution during the turning process is critical for optimizing machining operations and extending tool life. This study investigates the application of LSTM neural networks to model the temperature field in turning operations using high-speed steel tools. The research integrates numerical simulations performed with ANSYS® software and experimental data, enabling a comprehensive analysis of heat transfer mechanisms. The results reveal that the LSTM neural network is highly effective, achieving low root mean square error (RMSE) values and processing data more efficiently compared to traditional numerical methods. This study proposes a metamodel that preserves prediction accuracy while significantly reducing computational costs relative to conventional simulations. This innovative approach has the potential to enhance thermal monitoring in industrial processes, optimizing production and improving machining quality.
Keywords: Metamodeling; LSTM Neural Networks; Turning Process; Temperature Prediction.
