Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS

Abstract

Soft computing techniques, with their self-learning capabilities, fuzzy principles, and evolutionary computational philosophy, are being increasingly utilized in modeling complex machining processes. This study develops artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models to predict cutting force, surface roughness, and tool life during Inconel 718 turning with a hybrid nanofluid under minimum quantity lubrication. The hybrid nanofluid was created by combining 50�50% multi-walled carbon nanotubes and aluminum oxide nanoparticles with vegetable-based palm oil. ANFIS and ANN models were constructed with data from well-designed machining trials. The ANFIS model predicted machining performance using fuzzy logic, whereas the ANN model employed a feedforward neural network design. The results showed that both models were able to accurately predict the machining performance. However, ANFIS outperforms ANN in terms of accuracy, with prediction errors of 4.47% and 10.97% for surface roughness, and 6.05% and 9.86% for tool life, respectively. However, the accuracy of cutting force prediction was slightly higher with the ANN. This shows that ANFIS could be a better option for forecasting the machining performance while turning Inconel 718. However, this study suggests further investigation into ANFIS modeling, with a focus on membership function parameter optimization through hybrid optimization techniques.

Keywords

• ann
• anfis
• inconel 718
• subtractive manufacturing
• tool wear
• fracture
• modeling