Study on machinability of in situ Al–4.5%Cu–TiC metal matrix composite-surface finish, cutting force prediction using ANN. (January 2016)

• Record Type: Journal Article
• Authors: Das, Biswajit; Roy, Susmita; Rai, R.N.; Saha, S.C.
• Is Part Of: CIRP journal of manufacturing science and technology. Volume 12(2016:Jan.)
• Page Start: 67
• Page End: 78
• Publication Date: 2016-01
• Subjects: MMCs -- Chip formation -- Surface roughness -- In situ technique -- ANN; ANN artificial neural network -- MSE mean square error -- TiC titanium carbide -- MMC metal matrix composite -- SiC silicon carbide -- Ra centre line average roughness -- Rz average maximum height of the profile -- Rt maximum peak to valley height of the profile -- Fc cutting force in the direction of tool travel -- Ft thrust force -- CNC computer numerical control -- BUE built up edge -- CBN cubic boron nitride -- PCD polycrystalline diamond -- BPNN back propagation type neural network; Manufacturing processes -- Periodicals; 670.5
• DOI: 10.1016/j.cirpj.2015.10.002
• Languages: English
• ISSNs: 1755-5817

Abstract

Abstract: This paper discusses the experimental exploration on chip formation, surface roughness and cutting force measurement throughout the CNC milling operation of Al–4.5%Cu/TiC MMCs produced by the in situ practice and compared the results with those for Al–4.5%Cu/SiC MMCs produced by ex situ technique and with Al–4.5Cu master alloy. The cutting forces were measured during the milling operation with the aid of a dynamometer, surface roughness was measured by using a 3D profilometer, the chips formed were also characterised and compared from the viewpoint of machinability. The present study also explores the potential of artificial neural network to predict the cutting force and surface roughness generated during machining in CNC milling machine.