Experimental investigations and machining of uncoated carbide insert CNMG 120408 TTR on Al-SiC&lt;SUB align="right"&gt;p metal matrix nanocomposite

Swain, Pradyut Kumar; Mohapatra, Kasinath Das; Swain, Pratyush Kumar

doi:10.1504/ijnm.2021.126667

Cited by 1 publication

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“…Principal component analysis (PCA) was employed to optimize flank wear (VBc) and surface roughness (Ra). Analysis of variance identified cutting speed and depth of cut as the most influential factors for flank wear, and depth of cut and feed for surface roughness [41]. This study investigates the machinability of Cu matrix reinforced with B and ceramic CrC powders during dry turning.…”

Section: Introductionmentioning

confidence: 99%

Investigation on the machinability of Biochar reinforced AA5083 Metal Matrix composites fabricated by stir casting technique

Praveen,

Raja,

Jannet

et al. 2023

Eng. Res. Express

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Over the past two decades, there has been a significant rise in the demand for Aluminum Metal Matrix Composites (AAMCs) due to their notable advantages such as lightweight properties, increased strength, and enhanced hardness. In this study, three distinct AAMC samples based on Al5083 alloy, each varying in reinforcement levels at 4%, 8%, and 12% by weight were used. These composites were produced through a stir-casting process. The biochar used in our research was obtained through a pyrolysis process, adding an environmentally friendly and sustainable aspect to the composite materials. In the machining aspect of the study, A systematic L27 Taguchi Design of Experiment approach was used to investigate the influence of input parameters, namely cutting speed, feed rate, depth of cut, and weight percentage of reinforcement material, on the machining process. Further regression models were developed to gain insights into the relationships between these parameters and the resulting surface roughness. Turning operations at a cutting speed of 1050 rpm, a feed rate of 60 mm, and a depth of cut of 0.75 mm were idle for superior surface roughness and to assess the impact of the reinforcement material on the machined surface.

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Section: Introductionmentioning

confidence: 99%