Optimization of end milling parameters for rough and finish machining of Al-4032/3%SiC metal matrix composite

Saini, Pardeep; Singh, Premlata

doi:10.1088/2631-8695/ac2e11

Cited by 10 publications

(4 citation statements)

References 47 publications

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“…The steady increase in SiC reinforcement particles on Al4032 improves various mechanical parameters while also causing quick tool wear and high machining costs. The optimization was done using TGRA and ANOVA [86], Taguchi and ANN [87], and RSM [88]. The suitable spindle speed, flow rate, and cut length achieve the lowest tribological characteristics during the turning of the Al7071/SiC composite [89].…”

Section: Aluminium Reinforced With Silicon (Al/si)mentioning

confidence: 99%

Conventional Machining of Metal Matrix Composites towards Sustainable Manufacturing – Present Scenario and Future Prospects

Gutema,

Lemu

2024

Preprint

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This study provides an in-depth review of the available literature on metal composite material machining, focusing on traditional machining operations such as turning, milling, shaping, drilling, and grinding. The motivation for the study is that this composite type has emerged as a vital material in applications requiring functionality, resilience, cost-effective operation, low consumption of energy and lightweight. Despite its superior characteristics, the manufacturing complexity and poor machinability of metal matrix composites have been the primary barriers to its adoption. Nevertheless, there is a reduction of exceptional efficiency, increase in surface roughness, tool wear and so on in metal matrix composites throughout machining. As a result, this review gives a comprehensive investigation of metal matrix composite machining and effect on process parameters and procedures. Furthermore, this study distinguishes metal matrix composites towards sustainable manufacturing and the metals employed by the researchers, as well as the gaps discovered for future recommendations in metal-based composites.

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Section: Aluminium Reinforced With Silicon (Al/si)mentioning

confidence: 99%

Conventional Machining of Metal Matrix Composites towards Sustainable Manufacturing – Present Scenario and Future Prospects

Gutema,

Lemu

2024

Preprint

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“…MLR model is generally considered as approximating or empirical functions that forms a definite relationship among dependent and independent variable with satisfactory estimation to the unknown true function [50]. For predicting the outputs, empirical models are framed for individual output by adopting multiple linear regression (MLR) technique [51] for thrust force, surface roughness and roundness error as represented in equations (2)- (4). By using this developed model, prediction is done and comparison is done withthe experimental values, as shown in figure 10.…”

Section: Development Of Empirical Model Using Multiple-linear Regress...mentioning

confidence: 99%

Parametric analysis on drilling of aluminium alloy hybrid composites reinforced with SIC/WC

et al. 2022

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Aluminium alloy (Al7075) based hybrid metal matrix composites reinforced with Silicon carbide (SiC) and Tungsten carbide (WC), at 5 wt.% each are considered for this investigation, which are developed by stir casting methodology. Material characteristic analysis both at micro and macro (Tensile strength and micro hardness) level were performed. This investigation is further progressed with drilling of the composites using titanium aluminium nitride coated carbide drill (5mm diameter) for varied point angle, feed rate and drill speed. The responses such as thrust force, surface roughness and roundness error were investigated by adopting Response Surface Methodology (RSM). Multiple linear regression (MLR) is developed along with Artificial Neural Networks (ANN) model for predicting the outputs. Scanning Electron Microscope (SEM) image reveals the uniform distribution of ceramic particles in matrix which ascertains enhanced mechanical properties. The parameters such as feed rate and point angle are found to have significant influence during drilling process. The roundness error is found higher with higher point angle which is due to the wider cutting edges in the drill bit. For unconstrained multi-objective optimization, the optimal condition obtained are 128°- point angle, 0.05 mm/rev feed rate and 1000 rpm drill speed. For constrained optimization (roundness error ≤0.05 mm), optimal conditions are 118° point angle, 0.05 mm/rev feed rate and 1000 rpm drill speed.

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“…For instance, Mia et al 18 focused on finding the optimized parameters for simultaneously diminishing surface roughness and cutting forces in machining 4140 steel under MQL by using Grey-based Taguchi and desirability method. Saini et al [19][20][21] adopted the grey relational analysis (GRA) technique to resolve the optimization problem of cutting parameters in end-milling of Al 4032. Their obtained optimization results were useful for industrial practitioners and research community.…”

Section: Introductionmentioning

confidence: 99%

Optimization of sustainable milling of SKD11 steel under minimum quantity lubrication

Nguyen

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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In this study, the sustainably face milling of SKD11 steel under a minimum quantity lubrication condition was investigated. The goal of this work is twofold: (i) observing the impact of main milling parameters, including cutting speed ( Vc), depth of cut ( ap), and feed per tooth ( fz) on surface roughness ( Ra) and specific cutting energy (SCE), and (ii) finding out an optimal combination of cutting parameters for ameliorating the surface quality, productivity, and diminishing energy consumption. For this purpose, the experiment was designed by using the Box–Behnken matrix. A series of 15 experimental runs have been conducted to acquire experimental data. The regression models of Ra and SCE were subsequently developed and evaluated by using the analysis of variance. Finally, the multi-objective optimization issue was resolved by using the technique for order of preference by similarity to ideal solution (TOPSIS) algorithm and the desirability approach (DA). The results show that the parameter fz reveals the highest impact on surface roughness and SCE, followed by ap and Vc. The TOPSIS method allows finding the optimal solution with a reduction of 46% in terms of SCE when compared with that achieved by DA. This is significant in the context of sustainable manufacturing. This research is expected to enrich the knowledge of milling SKD11, and the achieved results can be used for research community and manufacturing industries.

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Optimization of end milling parameters for rough and finish machining of Al-4032/3%SiC metal matrix composite

Abstract: Aluminum-based composites are known for better mechanical properties, superior corrosion performance, and light weight. Gradual increase in the reinforcement (SiC particles) produces RECEIVED

Cited by 10 publications

References 47 publications

Conventional Machining of Metal Matrix Composites towards Sustainable Manufacturing – Present Scenario and Future Prospects

Conventional Machining of Metal Matrix Composites towards Sustainable Manufacturing – Present Scenario and Future Prospects

Parametric analysis on drilling of aluminium alloy hybrid composites reinforced with SIC/WC

Optimization of sustainable milling of SKD11 steel under minimum quantity lubrication

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