Evaluating the Machinability Characteristics of Aluminium Metal Matrix Composites for Automotive Applications

Machining stands as a fundamental activity within the majority of manufacturing enterprises, demanding significant time, materials and effort. The advancement of any manufacturing and automotive sector hinges on the establishment of a well-regulated machining environment. Consequently, to mitigate material wastage and guarantee timely production, the optimization of the machining process becomes imperative. This optimization can be realized by fine-tuning specific parameters like speed, feed and depth of cut. Attaining a controlled machining process involves either conducting a pre-simulated study or predicting values through a computational approach. The predictive analysis of these parameters ensures enhanced machinability within a specified timeframe, minimizing resource wastage. In this study, turning operations were conducted on A356 aluminium alloy, Al-SiC composite material and mild steel using a medium-duty lathe equipped with a High-Speed Steel (HSS) tool. The cutting parameters included varying cutting speeds of 140, 310 and 500 rpm, feed rates of 0.33, 0.66 and 1 mm/rev with a consistent cutting depth of 0.5mm. The assessment of mechanical properties aims to investigate the impact of reinforcement particles within the matrix material. Regression analysis is being employed to predict output values based on corresponding input values.

Authors

Publication Details

Journal
International Journal of Vehicle Structures and Systems
Published
2024-12-31
DOI
https://doi.org/10.4273/ijvss.16.6.07
Primary Topic
Advanced Machining and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating the Machinability Characteristics of Aluminium Metal Matrix Composites for Automotive Applications

Padmayya Naik, B. E. Naveena, V. Hariram, Jenoris Muthiya Solomon et al.
International Journal of Vehicle Structures and Systems
Advanced Machining and Optimization Techniques
article

Evaluating the Machinability Characteristics of Aluminium Metal Matrix Composites for Automotive Applications

Padmayya Naik, B. E. Naveena, V. Hariram, Jenoris Muthiya Solomon, J. Godwin John, B.T. Manjunath
article en

Abstract

Machining stands as a fundamental activity within the majority of manufacturing enterprises, demanding significant time, materials and effort. The advancement of any manufacturing and automotive sector hinges on the establishment of a well-regulated machining environment. Consequently, to mitigate material wastage and guarantee timely production, the optimization of the machining process becomes imperative. This optimization can be realized by fine-tuning specific parameters like speed, feed and depth of cut. Attaining a controlled machining process involves either conducting a pre-simulated study or predicting values through a computational approach. The predictive analysis of these parameters ensures enhanced machinability within a specified timeframe, minimizing resource wastage. In this study, turning operations were conducted on A356 aluminium alloy, Al-SiC composite material and mild steel using a medium-duty lathe equipped with a High-Speed Steel (HSS) tool. The cutting parameters included varying cutting speeds of 140, 310 and 500 rpm, feed rates of 0.33, 0.66 and 1 mm/rev with a consistent cutting depth of 0.5mm. The assessment of mechanical properties aims to investigate the impact of reinforcement particles within the matrix material. Regression analysis is being employed to predict output values based on corresponding input values.

International Journal of Vehicle Structures and SystemsVol. 16(6)
Openalex Percentile: Top 73%
Advanced Machining and Optimization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.