Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis

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A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites (2020, background): “Muthukrishnan and Davim [92] also supported that the feed rate has the highest statistical and physical influences on the SR, whereas Manna and Bhattacharayya [48] considered that the cutting speed, feed rate, and depth of cut had equal influences on the Ra and Rt values.”
Study on tool wear and surface roughness in machining of particulate aluminum metal matrix composite-response surface methodology approach (2010, methodology): “Muthukrishnan et al. [ 18 ] developed two modeling techniques used to predict the surface roughness namely ANOVA and ANN.”
Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method (2015, background): “Where Ri is the obtained value of surface roughness and cutting force for the ith test in that experiment, and n is the number of tests in the trials, a high signal to noise ratio is always preferred [3,26,27]”
Optimization of surface roughness, cutting force and tool wear of nitrogen alloyed duplex stainless steel in a dry turning process using Taguchi method (2014, methodology): “Muthukrishnan and Davim [14] optimized the machining parameters of Al/SiC metal matrix composites using ANOVA and ANN analysis and reported that feed rate has high physical influence on the surface roughness.”