Muhammad Riza

TL;DR: In this article, a Matlab simulink model was developed to simulate the tool life based on the flank wear rate, which was used to estimate the volume loss due to abrasive, adhesive and diffusive wears in turning hard materials with higher cutting speed by using a ceramic cutting tool.

TL;DR: In this paper, the authors investigated tool wear and surface roughness under different rake angles and different cutting speed using Cermets and showed that by increasing negative rake angles the higher wear occurred shorter duration of tool life and poor surface finish.

TL;DR: The development of new models and optimizing these models of machining parameters to minimize the cutting temperature in end milling process by integrating the genetic algorithm (GA) with the statistical approach are presented.

TL;DR: In this paper, the Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature in the CNC end milling process and the results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

TL;DR: In this article, the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach has been presented, and the mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut.