The best artificial intelligence method for predicting the machining process can vary depending on several factors, including the complexity of the relationship between the input variables and the output, the size and quality of the available data, and the computational resources available. However, some methods that are commonly considered to be among the best for this task include:

Also, there are also several hybrid methods that have been developed for machining process prediction.

1. Artificial Neural Network-Support Vector Regression (ANN-SVR).

2. Decision Tree-Neural Network (DT-NN).

3. Random Forest-Artificial Neural Network (RF-ANN).

4. Reinforcement Learning-Support Vector Regression (RL-SVR).

5. Bayesian Optimization-Gradient Boosting (BO-GB).

Mohammad Ghasemian Fard

The newest artificial intelligence method for predicting the machining process is currently Deep Learning, specifically Artificial Neural Networks (ANNs). These models have shown promising results in recent studies for modeling and predicting various aspects of the machining process.

The chance of publishing a paper in this area in a good journal depends on many factors such as the quality of the research, the novelty of the approach, and the relevance to the field. However, since Deep Learning is a highly active and competitive research area, it may be challenging to get published in a high-impact journal without a significant contribution. To increase the chances of getting published, it is important to thoroughly review the existing literature and to present a well-designed experimental study with a clear and concise discussion of the results.