Investigating the EDM parameter effects on aluminium based metal matrix composite for high MRR

In current manufacturing science and engineering application, material selection is an important criterion. Hybrid metal matrix composite (MMCs) has emerged as an important growing demand for manufacturing field like aerospace, automotive and electronic engineering due to its excellent mechanical, physical and thermal characteristics in comparison with convectional materials. The distinctive features of electrodischarge machining (EDM) and high exhaustiveness lead to improve the machining characteristics of MMCs. In this current research work, EDM technique has been applied in order to machine hybrid Al-7075 composite to obtain the high product quality and improve the performance level. The impetus of this research paper is to investigate the optimum parameters to reduce the machining time with high material removal rate (MRR), low surface roughness (SR), low tool wear rate (TWR) and frequency by varying the input parameters such as current, voltage and pulse-on time(Ton), pulse-off time (Toff) and arc gap. Several experimental runs are conducted by using L16 orthogonal array (OA), and prime parameters were predicted by ANOVA technique with effective parametric contributions. From the result, it is found that Grey–Taguchi method gave better optimization result with error percentage as compared to simple Taguchi method and also found current and voltage, Ton gave higher contribution in the time of machining in comparison with other parameters.KeywordsEDMMMCsMRRSRTWRTaguchiANOVAGrey–Taguchi

The paper investigates the effect of different machining parameters such as Current (A), Pulse ON time (Ton) and Pulse OFF time (Toff) of the Die-sink EDM process on NIMONIC 80A which influence the three major machining characteristics: Material Removal Rate (MRR), Tool Wear Rate (TWR) and Radial overcut (ROC). The experiment is executed and analysed as per Box Behnken Design (BBD) of Response Surface Methodology (RSM). After conducting the experiments, Analysis of variance (ANOVA) and optimization using desirability function were performed. Regression equation was developed to show a relationship of the influences of machining parameters on characteristics. This research results show that Current and Pulse ON time are the main important parameters for MRR and the Current is a major parameter for TWR and ROC. The optimize parameters where highest value of MRR and lowest value of TWR and ROC is Current 3 A, Pulse ON time 900 µs and Pulse OFF time 51 µs with the desirability value 0.988.

In this paper, a comparative experimental analysis of die-sinking electric discharge machining (EDM) to two most exhaustively used aluminum metal matrix composites (AMMCs) has been performed using Copper and Tungsten as tool electrodes. AMMCs containing silicon carbide (SiC) and alumina (Al2O[Formula: see text] as reinforcement (10[Formula: see text]wt%) were fabricated by stir casting method. The Box–Behnken Design (BBD) approach of response surface methodology was used to develop experimental models for material removal rate (MRR) and surface roughness (SR). Effect of input parameters such as current ([Formula: see text]–12[Formula: see text]A), gap voltage ([Formula: see text][Formula: see text]V), pulse-on time ([Formula: see text]s), and duty factor ([Formula: see text]–6) on the output responses has been investigated with response surface plots. Effectiveness of design of experiment (DoE) and evolutionary algorithm-based multi-objective optimization (MOO) technique have been compared to find the best feasible optimal solution. ANOVA analysis reveals that for alumina reinforced AMMC interaction between [Formula: see text] has significant effect on both MRR and SR using Cu electrode. But for tungsten, electrode interaction between [Formula: see text], [Formula: see text], and [Formula: see text] have major role on MRR whereas SR is mostly influenced by interaction between [Formula: see text] and [Formula: see text]. The parametric analysis reveals that an increase of current from 4[Formula: see text]A to 12[Formula: see text]A at a higher pulse-ontime increases the MRR more significantly, and higher MRR occurs in cases of alumina-reinforced AMMC. Increase of pulse-ontime at low current (4[Formula: see text]A) reduces the MRR in AMMC/Al2O3. Good surface finish can be obtained by combining high voltage (60[Formula: see text]V) with either small current (4[Formula: see text]A) or small duty factor (4) for both AMMCs. Both DoE and metaheuristic-based MOO technique reveals that copper electrode should be preferred for die-sinking EDM of AMMC/SiC. Metaheuristic approach should be preferred for optimization of die-sinking EDM of AMMCs using different electrodes because it requires low current for effective machining of different AMMCs.

Electro Discharge Drilling of Hybrid MMC

An overview of the effect of stirrer design on the mechanical properties of Aluminium Alloy Matrix Composites fabricated by stir casting

Nowadays, composites are used in different parts of industries and it is one of the most important subjects. The most widely used reinforcements in metal matrix composites are Al2O3 and SiC fibers and particles which may be used in cutting-edge functional and structural applications of aerospace, defense, and automobile industries. Depending on the type of powder used, composite materials are difficult to machine by conventional cutting tools and methods. The most appropriate way for machining of these composites is electro discharge. For the reason of improving the surface quality, tool wear rate and material removal rate and reducing the cracks on the surface, Al2O3 powder was used. In this study, the effect of input parameters of EDM such as voltage, pulse current, pulse on-time and pulse off-time on output parameters like material removal rate, tool wear rate and surface roughness in both conditions of the rotary tool with powder mixed dielectric EDM and the stationary tool excluding powder mixed dielectric were investigated. The critical parameters were identified by variance analysis, while the optimum machining parameter settings were achieved via Taguchi method. Results show that using of powder mixed dielectric and rotary tool reduce the tool wear rate, surface roughness and the cracks on the surface significantly. It is found also that using of powder mixed dielectric and rotary tool improve the material removal rate due to improved flushing action and sparking efficiency. The analysis of variance showed that the pulse current and pulse on-time affected highly the MRR, TWR, surface roughness and surface cracks.

Aluminum metal matrix composites are potential materials for aerospace and automobile industrial applications due to their enhanced mechanical and tribological properties. Aluminum reinforced with silicon carbide particles has been developed with enhanced mechanical and tribological behavior, but it lacks wettability between matrix and reinforcement causing weak bonding, which reduces the degree of enhancement. The objectives of this study were to fabricate aluminum-based metal matrix composites with enhanced wettability at varying stirring speeds (350, 450, 550 rpm), stirring time (5, 10, 15 min), weight percentage of SiC (0, 5, 10 wt.%), and weight percentage of MoS2 (0, 2, 4 wt.%). Nine samples were fabricated using stir casting based on Taguchi L9 orthogonal array. Hardness, tensile strength, and wear rate of the developed composite were investigated and analyzed as a single response characteristic using Taguchi’s signal-to-noise ratio and as a multi-response characteristic using hybrid Taguchi–grey relational analysis (HTGRA). The results revealed that the addition of SiC in the composite produced better hardness, tensile strength, and wear rate. The addition of MoS2 in the composite showed better hardness and tensile strength only up to 2 wt.% of MoS2, and in the case of wear rate, the addition of MoS2 in the composite up to 4% showed better wear resistance. Al–SiC–MoS2 hybrid composite shows better enhancement in hardness, tensile strength, and wear resistance than the Al–SiC composite.

This study presents a comprehensive investigation of electrical discharge machining (EDM) applied to Al-SiC metal matrix composites (MMCs), focusing on the comparative performance of copper and graphite tool electrodes. Material removal rate (MRR) and tool wear rate (TWR) were considered as the primary response variables, while current, pulse-on time (TON), and pulse-off time (TOFF) served as the controlling parameters. A Box–Behnken experimental design was employed to construct the design matrix, and analysis of variance (ANOVA) was used to evaluate the statistical significance of the process variables. Results indicate that both current and TON exert a strong influence on MRR and TWR, whereas increasing TOFF consistently reduces TWR for both electrodes. Overall, copper electrodes demonstrated superior machinability, offering up to 18% higher MRR and 25% lower TWR compared to graphite. These findings establish copper as a more efficient tool material for EDM of Al–SiC MMCs and provide practical guidelines for optimizing EDM parameters to enhance productivity and reduce tool wear in composite machining applications.

This paper presents the results of an experimental investigation on machining of micro-hole during electrochemical micromachining (ECMM) of stir-cast hybrid aluminum/(alumina + silicon carbide + carbon particulates) metal matrix composite. An ECMM set-up was developed and utilized for carrying out the experimental investigation. The supply current (Ip), supply voltage (v), pulse-on time (Ton), pulse-off time (Toff), electrolyte concentration (EC), and electrolyte flow rate (FR) were selected as process parameters. The effect of process parameters on material removal rate (MRR), electrode wear rate (EWR), surface roughness height (SR), taper cut (TC), over cut (OC), and micro-spark-affected zone (MSAZ) were analyzed through various graphs. The surface characteristics of the micro-drilled holes were analyzed using energy dispersive X-ray spectroscopy and scanning electron microscope techniques. Experimental results revealed that the MRR, EWR, TC, OC, and MSAZ increase with an increase in supply current, voltage, and pulse-on time. The optimal parametric combination for high MRR with low TC, OC, and MSAZ was found at 1.5 A supply current, 13 V supply voltage, 10 µs pulse-on time, 10 µs pulse-off time, 15 g/L electrolyte concentration, and 0.2 L/min electrolyte flow rate.

Optimization of machining parameters for surface roughness during abrasive water jet machining of aluminium/magnesium hybrid metal matrix composites

Aluminium alloys are popular in many advanced applications and are considered to be an essential class of engineering materials mainly due to their desirable combination of low density, high strength, and cost-effectiveness compared to competing materials. Aluminium metal matrix composites and hybrid metal matrix composites will further enhance these properties by incorporating hardened particles as reinforcements. Hybrid metal matrix composites are second-generation composites that exhibit superior mechanical properties due to the synergistic combination of different reinforcement combinations. Various combinations of reinforcing particulates are being explored. The fabrication of the hybrid composites is done using a stir casting route, which will significantly influence the mechanical properties, microstructure and reinforcement distribution. Aluminium HMMCs offer lightweight, high strength, good wear resistance, and other properties that allow them to be extensively used in the structural, aerospace, marine, and automotive industries. Hybridization of metal alloys provides flexibility in designing components and quality control during manufacturing. Thus, this review paper is focused on the fabrication and characterization of hybrid Aluminium metal matrix composites, advancements in their mechanical properties, recent developments and applications.

In the present work, dry sliding wear behaviour of hybrid aluminum metal matrix composites is carried out. A mixture of silicon carbide and boron carbide is used in equal fraction as reinforcement with base material AA6082-T6 to prepare AA6082-T6/SiC/B4C hybrid metal matrix composites using stir casting technique. The weight percentage of silicon carbide and boron carbide mixture taken to prepare hybrid composites is 5, 10, 15 and 20. The wear behaviour of Al-SiC-B4C composites is investigated using a pin-on-disc apparatus at room temperature, and optimization of process parameters is done using response surface methodology. The weight percentage of reinforcement, sliding speed, load and sliding distance are selected as process parameters with five levels of each. Analysis of variance shows that wear increases with increase of load or sliding distance and decreases with an increase in reinforcement or sliding speed. The experimental results revealed that the wear of Al-SiC-B4C hybrid composites has been influenced most by the sliding distance and least by weight percentage of reinforcement. The interaction between load–sliding speed is the only significant two-factor interaction in the present model which increases wear rate in fabricated hybrid composites. Further, the experimental results obtained are verified by conducting confirmation tests, and the errors found are within 3 to 7%.

Wire-electric discharge machining (W-EDM) is an advanced technology used for machining hard-to-cut materials with high hardness. Therefore, it is critical to characterize and model the Wire-ED machining performance for new materials having remarkable mechanical properties with respect to the multiple control parameters involved in the process. In this light, the present study investigates the multi-parametric optimization of current, pulse-on time (Ton), and pulse-off time (Toff) on material removal rate (MRR), kerf width (KW), surface roughness (R a) for Wire-EDM of artificially aged Al6061/B4C composite using response surface method (RSM) and grey relational analysis (GRA). The results of the investigation revealed that, Toff has the most significant impact on the multi-parametric response, with a percentage-wise contribution of 38% from the analysis of variance. The optimization results established that a multi-parametric combination of current - 6 A, Ton - 42.5253 ms, and Toff - 10 ms achieved the optimum response of MRR - 1.7036 mg/min, KW - 0.1727 mm, and R a - 5.6525 mm. The results obtained herewith have practical relevance to Wire-EDM industry for manufacturing applications.

Optimization of wear behaviour of AA6061 – SiC – B4C – Fly ash metal matrix composite using response surface methodology

Incoloy 925, a Nikel-based superalloy, exhibits low machinability with conventional machining techniques due to its inhomogeneous properties. Therefore, there is a need to establish a non-conventional method to efficiently machine this alloy. This work is a novel attempt to present the electric discharge machining (EDM) of the superalloy Incoloy 925 and subsequent multi-response optimization. The model for the analysis was designed using the Box Behnken Design (BBD) technique, and the Response Surface Methodology (RSM) was used for the optimization of the results. The machining was performed using a cylindrical copper tool of 11 mm diameter. The effect of pulse-on time (Ton), current, and pulse-off time (Toff) on the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) was investigated. The results from variance analysis confirmed the significance of all the three input factors. The investigation revealed that the maximum MRR (99.2154 mm3/min) was obtained at a pulse-on time of 90 μs, pulse-off time of 5 μs, and current of 30 A. The minimum TWR (0.8866 mm3/min) were achieved at Ton = 60 μs, Toff = 8 μs and current = 10 A. The microscopic images of the machined surfaces revealed very few micro-voids and globules and no cracks, resulting in a fine surface finish of 1.2436 μm, achieved through optimal discharge energy transfer and copper electrodes. The optimal values MRR, TWR, and SR according to composite desirability function are 99.1524 mm3/min, 1.0915 mm3/min, and 1.3925 μm. The experimental results were accurately predicted using RSM and an artificial neural network (ANN), with the ANN showing a predicted correlation coefficient (R) close to 1 indicating high accuracy of the model.