Prediction Analysis of Surface Roughness of Aluminum Al6061 in End Milling CNC Machine Using Soft Computing Techniques

Abstract

Computer numerically controlled (CNC) milling has been one of the most commonly used manufacturing processes for the performance of multiple operations, from tiny integrated circuits to heavy-duty mining machine gearboxes. It is a well-known machining process that offers close tolerances and repeated operations. However, the choice of machining parameters to achieve a desired part’s surface roughness (SR) remains a challenge. In the present study, artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) approaches have been used to predict and monitor the surface roughness of aluminum Al6061 machined blocks. Furthermore, both models have been hybridized with genetic algorithm (GA) and particle swarm optimization (PSO) to investigate the potential enhancement in the prediction performance of the hybrid approach. The results show that factors such as the population size, the acceleration values, the choice of membership functions, and the number of neurons and layers significantly influence the prediction performance of the proposed models. Through a parametric analysis, this study demonstrates how the configuration of the models could affect the prediction performance. While exhibiting the impact of models’ hyperparameter combination on the prediction ability, this study provides insight into the development of suitable prediction models and the potential of soft computing techniques to predict the surface roughness of aluminum Al6061 blocks on CNC machines.

Measuring Procedures for Evaluating the Surface Roughness of Machined Parts

“…These are mainly applied in the field of mechanical engineering, mechatronics, robotics, production engineering, machining, biomedical engineering, automotive engineering, tribology, microsystems, precision mechanics, etc. [66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85].…”

Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining

“…In [19], the authors introduced a technique employing the radial basis function (RBF) to tackle the issue of unevenly distributed abrasive particles in belt grinding procedures. Likewise, ANNs have been employed for surface roughness prediction in studies [20][21][22][23][24][25][26][27][28], yet cooling methods [29,30] and dressing parameters [31] have not been treated as distinct variables in the prediction algorithms. Additionally, it is important for the dataset size to be sufficiently large, which has not been consistently observed in the literature.…”

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

“…Indeed, the use of large populations allows for fast convergence of the model, while small et al, (2019), their objective was to predict the behavior of channel connectors embedded in normal, high-strength concrete. In addition, the work of Balonji et al, (2023) examines in depth the prediction of surface quality when milling aluminum (Al6061). In sum, these encouraging results suggest that the PSO-ANN algorithm may be a promising method for predicting output responses in this context.…”