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

This document is a list of references to research papers and articles that focus on machining processes, specifically the machining of superalloy Inconel 718. The papers cover a range of topics including tool wear reduction, surface integrity evaluation, optimization techniques, and prediction models using artificial intelligence and machine learning. The research explores different lubrication techniques, nanofluid additives, and cutting parameters to improve the machinability of Inconel 718. The papers utilize various methodologies such as neural networks, fuzzy logic, response surface methodology, and genetic algorithms to optimize machining parameters and predict tool wear, surface roughness, and material removal rate. [Extracted from the article]