Application of AI techniques for modeling the performance measures in milling of 7075-T6 hybrid aluminum metal matrix composites

Mohanraj, T.

doi:10.1142/s1793962322500349

Cited by 2 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[6] Furthermore, the initial research concentrated on the effect of milling conditions on tool wears rather than investigating the effect on work material quality. [8][9][10][11] Even in recent years, only a small amount of research has been published on the effect of milling machining parameters and material composition on machining quality of AMC, notably with Al 7075 alloy as the basis material, [12][13][14][15][16][17][18][19] with only three dealing with the traditional milling process of aluminum-titanium diboride (Al/TiB 2 ) composite.…”

Section: Introductionmentioning

confidence: 99%

Multi-Response Optimization of Milling Process Parameters for Aluminium-Titanium Diboride Metal Matrix Composite Machining Using Taguchi- Data Envelopment Analysis Ranking Approach

Kamath¹,

Krishnan²,

Devesh³

et al. 2022

Eng. Sci.

View full text Add to dashboard Cite

Ceramic reinforced aluminum matrix composites are increasingly gaining importance because of their specific, particulate reinforced physical features. Machining such materials is a time-consuming process due to their superior physical properties. Moreover, only a few recorded research documents discuss the optimization of machining parameters in the milling of aluminum-titanium diboride (Al/TiB 2 ) composites. Thus, the current study sought to evaluate the effect of process parameters on the face milling machining process while machining Al/TiB 2 metal matrix composite. Reinforcement weight percentage, cutting speed, feed rate, and depth of cut used in milling were used as input parameters in the Taguchi-DEA ranking methodology to evaluate performance metrics, including material removal rate and material's arithmetic average roughness. According to the results of the experimental investigation, cutting speed and reinforcement weight percentage have a greater influence on determining performance metrics in the milling process due to their role in determining impact strength. Among the selected process factors, the ideal combination of reinforcing weight percent (9%), cutting speed (1500 rpm), feed rate (300 mm/min), and depth of cut (0.15) were discovered and evaluated using a confirmation test with 3.14 and 2.34 percent variation in material removal rate (MRR) and surface roughness (Ra) values, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Response Optimization of Milling Process Parameters for Aluminium-Titanium Diboride Metal Matrix Composite Machining Using Taguchi- Data Envelopment Analysis Ranking Approach

Kamath¹,

Krishnan²,

Devesh³

et al. 2022

Eng. Sci.

View full text Add to dashboard Cite

show abstract

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

2023

View full text Add to dashboard Cite

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.

show abstract

Application of AI techniques for modeling the performance measures in milling of 7075-T6 hybrid aluminum metal matrix composites

Cited by 2 publications

References 41 publications

Multi-Response Optimization of Milling Process Parameters for Aluminium-Titanium Diboride Metal Matrix Composite Machining Using Taguchi- Data Envelopment Analysis Ranking Approach

Multi-Response Optimization of Milling Process Parameters for Aluminium-Titanium Diboride Metal Matrix Composite Machining Using Taguchi- Data Envelopment Analysis Ranking Approach

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

Contact Info

Product

Resources

About