Investigation of surface roughness in the milling of Al7075 and open-cell SiC foam composite and optimization of machining parameters

• Published in: Neural computing & applications Vol. 28; no. 2; pp. 313 - 327
• Main Authors: Karabulut, Şener, Karakoç, Halil
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2017; Springer Nature B.V
• Subjects: Aluminum base alloys; Aluminum matrix composites; Artificial Intelligence; Artificial neural networks; Carbide tools; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Feed rate; Graphs; Image Processing and Computer Vision; Machinability; Mathematical models; Metal matrix composites; Milling (machining); Optimization; Original Article; Orthogonal arrays; Prediction models; Probability and Statistics in Computer Science; Process parameters; Rapid prototyping; Regression analysis; Signal to noise ratio; Silicon carbide; Surface properties; Surface roughness; Variance analysis; Taguchi’s method; Surface roughness; Artificial neural network; Open-cell SiC foam MMC; Al7075
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-015-2058-x

In the present study, aluminum alloy 7075 (Al7075)-based open-cell silicon carbide (SiC) foam composite was fabricated and the machinability of both Al7075 and the open-cell SiC foam Al metal matrix composite was investigated during milling using an uncoated carbide tool. The machining trials were conducted using the Taguchi L 27 full-factorial orthogonal array, and the milling parameters were optimized for surface roughness. Analysis of variance was employed to determine the effect of the cutting variables on surface roughness. The experimental results were evaluated by signal-to-noise ratio, 3D surface graphs, artificial neural networks (ANNs) and main effect graphs. The analysis results show that the feed rate was the most significant milling parameter affecting surface roughness of both Al7075 and the open-cell SiC foam composite. Prediction models have been developed for the surface roughness through regression analysis and ANNs. Confirmation experiments were performed to identify the performance of mathematical models, and the surface roughness was predicted with a mean squared error equal to 1.6 and 0.24 % in the milling of Al7075 and open-cell SiC foam composite, respectively. The test result showed that the three-dimensional open-pore SiC foam network reinforcement was restricted the movement of the soft matrix and provided an acceptable surface quality in the milling of MMCs.