Eventos Anais de eventos
COBEM 2023
27th International Congress of Mechanical Engineering
OPTIMIZATION OF CUTTING PARAMETERS IN THE FINISH TURNING OF AISI 420 STAINLESS STEEL USING AN ARTIFICIAL NEURAL NETWORK
Submission Author:
Émerson Passari , RS , Brazil
Co-Authors:
Émerson Passari, André João de Souza, Herbert Gomes, Carlos Alfredo Gracioli Aita
Presenter: Émerson Passari
doi://10.26678/ABCM.COBEM2023.COB2023-2106
Abstract
AISI 420 steel is a martensitic stainless steel alloy known for its high corrosion resistance, hardness, and wear resistance, making it suitable for applications requiring protection against aggressive environments. However, due to its properties, this material is considered to have low machinability, resulting in challenges in chip formation and high cutting forces, which adversely affect the quality of the machined surface, particularly in turning processes. Evaluating cutting parameters and their effects is crucial for enhancing machinability and reducing average roughness (Ra). However, there is limited research that comprehensively addresses all input variables and promotes simultaneous optimization. Thus, this study collected various data from the literature and developed a comprehensive algorithm that utilizes an artificial neural network (ANN) to optimize Ra values based on cutting parameters for AISI 420 turning. The collected data were validated and revealed a disparity between the predicted values and the experimental results, which could be attributed to variations in experimental configurations reported in the literature. Nonetheless, the experimentally obtained data yielded remarkably low roughness values (Ra = 0.43 ± 0.01 µm) for the turning process. This proposal demonstrates a viable alternative for achieving low roughness without requiring extensive prior experimental procedures.
Keywords
ANN, AISI 420, Turning, Optimization, Roughness
