Optimization of the Machinability of the Al-SiC Metal Matrix Composite Using the Dynamic Material Model

Mohan, B.; Venugopal, S.; Rajadurai, A.; Mannan, S.L.

doi:10.1007/s11661-008-9591-5

Cited by 8 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The occurrence of multiple slopes in the variations of skewness and kurtosis with time can possibly be attributed to the occurrence of different wear mechanisms in the composite. Scanning electron microscopy (SEM) results were reported in an earlier paper (Mohan et al, 2008). This showed that the cutting tool experiences abrasive wear at the flank face, as well as chip breakage and scooping of the SiC particles.…”

Section: Statistical Analysis Of Ae Signalsmentioning

confidence: 82%

Statistical analysis of acoustic emission signals generated during turning of a metal matrix composite

Mukhopadhyay¹,

Jayakumar²,

Raj³

et al. 2012

J. Braz. Soc. Mech. Sci. & Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Statistical Analysis Of Ae Signalsmentioning

confidence: 82%

Statistical analysis of acoustic emission signals generated during turning of a metal matrix composite

Mukhopadhyay¹,

Jayakumar²,

Raj³

et al. 2012

J. Braz. Soc. Mech. Sci. & Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Table 1 lists various recommended turning parameters for industry consideration based on optimization studies from Refs. [119][120][121][122][123][124].…”

Section: Surface Integrity and Machining Efficiencymentioning

confidence: 99%

A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites

Chen

2020

Adv. Manuf.

103

View full text Add to dashboard Cite

Among the various types of metal matrix composites, SiC particle-reinforced aluminum matrix composites (SiC p /Al) are finding increasing applications in many industrial fields such as aerospace, automotive, and electronics. However, SiC p /Al composites are considered as difficult-to-cut materials due to the hard ceramic reinforcement, which causes severe machinability degradation by increasing cutting tool wear, cutting force, etc. To improve the machinability of SiC p /Al composites, many techniques including conventional and nonconventional machining processes have been employed. The purpose of this study is to evaluate the machining performance of SiC p /Al composites using conventional machining, i.e., turning, milling, drilling, and grinding, and using nonconventional machining, namely electrical discharge machining (EDM), powder mixed EDM, wire EDM, electrochemical machining, and newly developed high-efficiency machining technologies, e.g., blasting erosion arc machining. This research not only presents an overview of the machining aspects of SiC p /Al composites using various processing technologies but also establishes optimization parameters as reference of industry applications.

show abstract

“…Under force, the stress of the matrix is unequal to that of SiC particles. According to Mohan et al [29] in the cutting process of composites, when the matrix deforms plastically, the SiC particles may only deform elastically to break, and also the boundary of the matrix and the SiC particles may break between the tool and reinforcing particles, which causes severe abrasive wear on the tool flank. This is because when the bond is broken, the SiC particles will gain higher kinetic energy due to turning of workpiece during machining.…”

Section: Effect Of Sic Particlesmentioning

confidence: 99%

Multiresponse Optimization of Al Alloy-SiC Composite Machining Parameters for Minimum Tool Wear and Maximum Metal Removal Rate

Bhushan

2013

Journal of Manufacturing Science and Engineering

View full text Add to dashboard Cite

Optimization in turning means determination of the optimal set of the machining parameters to satisfy the objectives within the operational constraints. These objectives may be the minimum tool wear, the maximum metal removal rate (MRR), or any weighted combination of both. The main machining parameters which are considered as variables of the optimization are the cutting speed, feed rate, depth of cut, and nose radius. The optimum set of these four input parameters is determined for a particular job-tool combination of 7075AI alloy-15 wt. % SiC (20^0 fim) composite and tungsten carbide tool during a single-pass turning which minimizes the tool wear and maximizes the metal removal rate. The regression models, developed for the minimum tool wear and the maximum MRR were used for finding the multiresponse optimization solutions. To obtain a trade-off betM'een the tool wear and MRR the, a method for simultaneous optimization of the multiple responses based on an overall desirability function was used. The research deals with the optimization of multiple surface roughness parameters along with MRR in search of an optimal parametric combination (favorable process environment) capable of producing desired surface quality of the turned product in a relatively lesser time (enhancement in productivity). The multi-objective optimization resulted in a cutting speed of 210 m/min, a feed of 0.16 mm/rev, a depth of cut of 0.42 mm, and a nose radius of 0.40 mm. These machining conditions are expected to respond with the minimum tool wear and maximum the MRR, which correspond to a satisfactory overall desirability.

show abstract

Optimization of the Machinability of the Al-SiC Metal Matrix Composite Using the Dynamic Material Model

Cited by 8 publications

References 22 publications

Statistical analysis of acoustic emission signals generated during turning of a metal matrix composite

Statistical analysis of acoustic emission signals generated during turning of a metal matrix composite

A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites

Multiresponse Optimization of Al Alloy-SiC Composite Machining Parameters for Minimum Tool Wear and Maximum Metal Removal Rate

Contact Info

Product

Resources

About