journal article·2019-08-21Open access

Optimization of turning parameters for AlSi10Mg/SCBA/SiC hybrid metal matrix composite using response surface methodology

S Shankar, A Balaji, A Pramanik

Materials Research Express· Volume 6· Issue 10

Abstract

For mass production, casting industries mainly require hybrid metal matrix composites with a high degree of surface finish. In this work, an attempt is made to design a low-cost hybrid material and also aims to investigate the optimum turning conditions to minimize the surface roughness (Ra), thereby producing high-quality hybrid metal matrix composites. AlSi 10 Mg alloy was fabricated with constant 9 wt% of treated sugarcane bagasse ash (SCBA) and 3, 6 and 9 wt% of silicon carbide (SiC) reinforcements and the mechanical properties were examined to predict the better composition in the fabricated materials. Experiments were conducted in the better composition material to identify the optimized turning parameters such as spindle speed, feed rate and depth of cut with bio lubricants as cutting fluid using response surface methodology (RSM) method and then the results were analyzed using ANOVA. The hardness of the developed hybrid material was increased from 102.7 BHN to 129.8 BHN, and the tensile strength was increased from 138.9 MPa to 161.7 MPa compared with the base AlSi 10 Mg alloy. Spindle speed of 275 rpm, a feed rate of 0.2 mm rev −1 and depth of cut of 0.75 mm were identified as the optimized input machining parameters. The regression equation for the turning parameters was derived through the study made by RSM. Overall the results concluded that the incorporation of reinforcements with the aluminium alloy exhibit superior mechanical properties and high quality of surface finish with the optimized turning conditions.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Aluminum Alloys Composites Properties · Aluminum Alloy Microstructure Properties · Advanced Welding Techniques Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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