Ceramic material coatings: emerging future applications

Chate, Ganesh R.; Kulkarni, Raviraj M.; Nikhil, R.; Chate, Vaibhav R.; Patel, Manjunath; Sollapur, Shrishail; Shettar, Manjunath

doi:10.1016/b978-0-323-99624-2.00007-3

Cited by 19 publications

(1 citation statement)

References 73 publications

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“…Investigates the effects of cutting speed, particle size, and particle volume percentage on surface roughness while turning 2024Al alloy composites supplemented with Al2O3 particles [13][14][15]. The grey-Taguchi approach is used to optimise milling settings for A6061P-T651 aluminium alloy, which results in better flank wear and decreased surface roughness [16][17]. Determine the best cutting parameters for Inconel 718 by using statistical experimental design, artificial neural networks, and genetic optimisation approaches [18][19][20].…”

Section: Literature Reviewmentioning

confidence: 99%

An ANN Approach to Determine the Surface Roughness in End Milling Cutter

Sollapur,

Raut,

Baviskar

et al. 2024

Int. J. Res. Publ. Rev.

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Surface finish is an important indication in the manufacturing process, particularly during milling processes. The goal of this project is to predict surface roughness using artificial neural networks. The neural network model efficiently determines the ideal cutting parameter values for various milling settings, resulting in reduced surface roughness. The current study is an experimental inquiry into end milling of M.S material using a carbide tool, examining the effect of various cutting settings on surface roughness. Using an artificial neural network (ANN), the study creates a link between surface roughness and cutting input parameters such spindle speed, feed, and depth of cut. The findings of this study have practical implications for industrial application, providing a way to minimise the time and expenses involved with surface roughness prediction.

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