Minimum Surface Roughness Research Articles

Prediction and optimization kerf width in laser beam machining of titanium alloy using genetic algorithm tuned adaptive neuro-fuzzy inference system

In the power diode laser beam machining (DLBM) process, the kerf width (KW) and surface roughness (SR) are important factors in evaluating the cutting quality of the machined specimens. Apart from determining the influence of process parameters on these factors, it is also very important to adopt multi-response optimization approaches for them, in order to achieve better processing of specimens, especially for hard-to-cut materials. In this investigation, adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm tuned ANFIS (GA-ANFIS) were used to predict the KW on a titanium alloy workpiece during DLBM. Five machining process factors, namely power diode, standoff distance, feed rate, duty cycle, and frequency, were used for the development of the model due to their correlation with KW. As in some cases, traditional soft computing methods cannot achieve high accuracy; in this investigation, an endeavor was made to introduce the GA-assisted ANFIS technique to predict kerf width while machining grooves in a titanium alloy workpiece using the DLBM process based on experimental results of a total of 50 combinations of the process parameters. It was observed that FIS was tuned well using the ANN in the ANFIS model with an R2 value of 0.99 for the training data but only 0.94 value for the testing dataset. The predicting performance of the GA-ANFIS model was better with less value for error parameters (MSE, RMSE, MAE) and a higher R2 value of 0.98 across different folds. Comparison with other state-of-the-art models further indicated the superiority of the GA-ANFIS predictive model, as its performance was superior in terms of all metrics. Finally, the optimal process parameters for minimum KW and SR, from gray relational–based (GRB) multi-response optimization (MRO) approach, were found as 20 W (level 2) for laser power, 22 mm (level 5) for standoff distance, 300 mm/min (level 5) for feed rate, 85% (level 5) for duty cycle, and 18 kHz (level 3) for frequency.

Effective optimization strategies for abrasive water jet machining of glass-carbon fiber reinforced composites: A comparative study of evolutionary optimization techniques

This study applied evolutionary optimization techniques and neural networks to predict optimum machining parameters of Abrasive Water Jet Machining (AWJM) for machining Glass-Carbon Fiber Reinforced Composite (GCFRC) materials. Several researchers have employed different optimization techniques; however, evolving computational capabilities further open avenues to optimize such parameters. Five evolutionary techniques, namely Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Nonlinear Least Square Error (LSE), were applied to minimize surface roughness (Ra) and maximize kerf width (Kw) and material removal rate (MRR). The output performance parameters (Ra, Kw, and MRR) were formulated as a linear mathematical function of machining parameters: tool feed rate (TFR), cutting speed rate (CSR), and stand-off distance (SOD). Though this study solves linear optimization problems, the proposed technique will be a strong tool for solving complex associations of machining and performance parameters in the future. A dataset of machining parameters and subsequent performance parameters was adopted from the available literature. The results indicated that the LSE method outperformed other techniques, yielding the lowest Root Mean Square Error (RMSE) in predicting Ra, Kw, and MRR, thus ensuring high machining accuracy. LSE technique reported relatively least RMSE values of 0.37 µm, 0.149Mm, and 237.23 mm3/min for Ra, Kw, and MRR, respectively. SA and PSO displayed identical and competitive RMSE values, slightly higher than LSE (up to 20 % higher). ANN and GA techniques were not effective relative to other considered techniques. LSE, SA, and PSO provide superior performance in optimizing AWJM parameters. The significant contribution of this research is the proposed optimization technique, offering a clear direction for solving complex associations between the performances and machining parameters of AWJM. This work also provides a foundation for future research to optimize such associations for other machining setups.

Experimental Analysis of Surface Roughness Optimization of EN19 Alloy Steel Milling by the Cuckoo Search Algorithm

In the present paper, end milling has been performed on EN19 alloy steel by selecting cutting speed, feed rate, & depth of cut as input parameters and surface roughness (SR) as a response. EN19 alloy steel milling is widely used in various sectors, such as the automotive, defence, construction, aerospace, and nuclear sectors. A parametric study of EN19 alloy steel is needed for better machining. The central composite design was used for designing the experiments & modeling the surface roughness as a response. A cuckoo search algorithm was applied to minimize the surface roughness. It was found that feed rate is the most important factor affecting surface roughness (SR). The Cuckoo Search Algorithm also reveals that a minimum SR 1.8576 micrometer has been achieved at a higher speed of 765 RPM, a lower feed rate of 55.9516 mm/min., & a lower depth of cut of 0.4846 mm. The experiment concludes that it is so that the optimum SR is exhibited at both lower feed rates & high speeds. This, in turn, indicates that our implementation of CCD-based SR, followed by the real cuckoo search algorithm optimization, provides similar results and a good model to the practical results we would expect.

Finite element analysis of wire EDM process parameters on performance characteristics of Al–SiC hybrid composite with micro and nano reinforcements

Aluminum Hybrid Composites (AHC) microchannels find extensive applications in aerospace, refrigeration and air conditioning (RAC), microelectronics, automotive, biomedical, and marine industries due to their improved mechanical properties. The combination of lower density, high hardness, elevated thermal and electrical conductivity, superior elasticity, and heightened oxidizing ability at low temperatures makes machining aluminum hybrid composites challenging through orthogonal methods in an open environment. Therefore, it is imperative to investigate the impact of various machining conditions to determine optimal parameter values for microchannel fabrication in aluminum hybrid composite materials. This study aims to identify the ideal machining parameters for Al–SiC micro-SiC nano hybrid composites using wire electrical discharge machining. Four distinct process parameters (pulse on time, gap voltage, wire tension, and wire feed rate) and two response parameters (material removal rate (MRR) and surface roughness (SR)) were considered for process optimization. Additionally, finite element modeling was conducted for the aforementioned process, and the results were compared with experimental outcomes. Single-variable optimization yielded maximum MRR at 125 tons (μs), 25 SV (volt), 12 WT (kgf), and 2 WFR (m/min), and minimum SR at 125 tons (μs), 10 SV (volt), 12 WT (kgf), and 4 WFR (m/min). Single-variable optimization for simulations resulted in maximum MRR at 125 tons (μs), 40 SV (volt), 4 WT (kgf), and 6 WFR (m/min), and minimum SR at 110 tons (μs), 10 SV (volt), 12 WT (kgf), and 2 WFR (m/min). Precise modeling aids in minimizing unnecessary machining, enabling exploration of a broader range of experimental results, thereby promoting environmentally friendly and sustainable machining practices.

Investigating the impact of laser polishing parameters on surface roughness, mass ablation rate, and optical transmittance of 3D‐printed PLA substrates

AbstractLaser polishing technique is one of the most effective post‐processing methods for 3D‐printed Polylactic acid (PLA) substrates and has gained significant interest of scientific research community in past few years. This paper aims to investigate the effect of fill density, laser power, and scanning speed on the polishing responses of 3D‐printed PLA substrates. These include surface roughness, mass ablation rate, and optical transmittance of PLA substrates printed using the fused deposition modeling technique. The optical transmittance was measured using Fourier‐transform infrared spectroscopy technique and surfaces were characterized using scanning electron microscope, and optical microscope. Minimum surface roughness of 1.04 μm was achieved at 5% laser power and 55% fill density constituting a decrease of 61.8% from unpolished PLA (2.735 μm). The lowest mass ablation rate (0.015 μg/pulse) was observed for the specimens with 40% infill density. Higher scanning speed enhanced the molecular bonding in PLA‐55 specimen indicated by increased peak transmittance ratios of CH bonds at stretching (1.021) and bending modes (1.045).

Optimal speckle size and signal processing for displacement measurements using a 4f optical setup

Fully developed laser speckle patterns are, due to their high contrast and statistical nature, well suited to measure strain and displacement via an appropriately designed measurement system. Laser speckle patterns are formed when a sufficiently coherent light source, such as a HeNe-laser, illuminates an optically rough surface. Therefore, methods based on laser speckle patterns can be applied to any surface scatterer with a minimum mean surface roughness of about a quarter of the laser’s wavelength. This includes also materials such as thin natural and technical fibres as well as foils, for which the presented measurement system, including the digital signal processing, was designed. In order to achieve the best possible resolution of a speckle-based measurement system, combined with a sufficiently small measurement uncertainty, all available design parameters must be optimised. One of these parameters is the speckle size, which is dependant on the properties of the imaging optics. In this paper a subjective laser speckle-based measurement system based on a so-called 4f-optical setup is presented. This setup allows the speckle size to be controlled in both axial and lateral dimensions separately, which is achieved with the help of an aperture in the Fourier plane of the optics. It is shown that the optimal speckle size for the presented measurement system, not only depends on the physical setup, but also on the signal processing applied. The signal processing routine estimates displacements of the speckle pattern, leading to an estimate for the strain. Additionally, it is demonstrated that the optimal speckle size can be lower than the commonly reported optimum between two and five pixel pitches, necessary to circumvent aliasing in the image data. While this is shown for a measurement setup using 4f-optics, the results are of general importance to speckle-based strain or displacement measurement systems and should thus be taken into account.

Cl2 corrosion resistance of SiC coatings with different morphologies

The objective of this study was to investigate the corrosion resistance of SiC coatings with various morphologies on graphite in a high-temperature Cl2 environment. The researchers conducted a thorough assessment of the coating's corrosion resistance by comparing micro-morphology, changes in surface roughness, and mass loss rates before and after corrosion. The findings reveal that the SiC layer can undergo a chemical reaction with Cl2 at high temperatures, resulting in the formation of layered or granular carbon on the grain surface. Corrosion occurs primarily at the grain boundaries. Among the tested samples, the S30 sample, characterized by the smallest grain size with average grain size 7.15μm, exhibited the highest corrosion resistance. It showed a low mass loss rate of only 0.72 % and a minimal surface roughness change rate of 11.3 % before and after corrosion, whereas the S40 exhibited the highest mass rate loss of 1.2 % and S20 had the highest roughness variation rate 54.3 %.

Optimization of Process Parameters of Wire Electric Discharge Machining Process for Machining AISI A2 Tool Steel

Optimization of Process Parameters of Wire Electric Discharge Machining Process for Machining AISI A2 Tool Steel

Application of Taguchi Method, ANOVA Analysis, and TOPSIS Technique in Optimization of Process Parameters for Surface Roughness and Material Removal Rate in Electrochemical Machining of Al-SiC MMCs

Objectives: To evaluate the significance of advanced machining techniques, such as EDM, ECM, and USM, in increasing productivity and overcoming challenges associated with outdated Al-SiC MMC machining. To assess the surface roughness, tool wear, and machining cost implications of employing advanced machining methods for Al-SiC MMCs. Methods The parameters studied were voltage (V), feed rate (F), and electrolyte concentration (C) in electrochemical machining (ECM) of Al/15%SiC composites. To optimise process parameters, the Taguchi method for Design of Experiments (DOE) with an L27 orthogonal array was used. Signal responsiveness is optimised using the Taguchi approach. The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is used to find optimal machining settings. Findings: The outcome of this research is that the parameters affecting surface roughness and material removal rate are voltage, electrolyte concentration and feed rate. The minimum Surface Roughness achieved by selecting the best combination level is A2, B3, C3 (smaller is better) i.e., voltage 20 V, feed rate (f) 0.4 mm/min., electrolyte concentration (c) 30 g/lit. The maximum Material Removal Rate achieved by selecting the best combination level is A3, B3, C3 (larger-is-better) i.e., voltage 25V, feed rate (f) 0.4 mm/min., electrolyte concentration (c) 30 g/lit. Novelty : In this work, TOPSIS technique paired with Taguchi method is used which is rarely studied by other researchers. TOPSIS technique provides the best optimal solution as compared to other techniques. Keywords: Al-SiC MMCs, ECM, MRR, Ra, Taguchi method, TOPSIS

ENHANCED SURFACE QUALITY AND STRENGTH OF FDMed SPECIMENS USING BBD AND BIO-INSPIRED ALGORITHMS

This research investigated and optimized the parameters of the FDM process by employing bio-inspired algorithms for determining the optimal parameter settings in terms of surface quality and mechanical performance. Four important process parameters including layer thickness (0.11–0.33[Formula: see text]mm), part orientation (0–90∘), raster width (0.2–0.56[Formula: see text]mm), and the raster angle (0–60∘) at three variation levels were selected for fabricating the specimens (ABS material P430) using the statistical Box–Behnken design. ANOVA analysis and multiple regression analysis were used to fit the experimental data to a second-order polynomial equation. Through, the RSM analysis, the layer thickness is the key important factor that accounts for all of the responses. The fracture behavior of specimens was examined using a scanning electron microscope (SEM). From the SEM analysis, a substantial amount of plastic deformation on the fracture surface indicative of craze cracking is visible from a 0∘ orientation, indicating a totally ductile fracture mechanism. Then, three swarm intelligence algorithms such as Tasmanian Devil Optimization (TDO), Remora Optimization Algorithm (ROA), Tuna Swarm Optimization (TSO) were implemented to optimize the input parameters that would lead to minimum surface roughness and maximum tensile strength. Experimental data and predicted values varied between 1.64% and 1.84%, as shown by verification experiments.

Evaluation and Multi-Objective Optimisation of Cutting Parameters in Turning of AISI 1020 Mild Steel using Formulated Cutting Fluid

Input parameter like the cutting fluid is one of the requirements for minimal surface roughness, cutting temperature, tool wear and optimal material removal rate coupled with improved machinability and productivity. The evaluation of the optimal factors of surface roughness, material removal rate, cutting temperature and tool wear in the turning of AISI Mild Steel with the use of eco-friendly fluids. Concerns has been raise globally about the non - biodegradability and non-recyclability of the conventional fluids in the research space. This prompted the research interest in replacing the mineral oil based fluids with eco-friendly cutting fluid such as castor seed oil based cutting fluid (CBCF). The locally sourced castor seed oil was investigated for its physiochemical properties as well as its fatty acid composition (FAC). The cutting fluid was formulated using ratio 1:9 of oil with additives to distilled water and then characterized. In turning of AISI 1020 Mild Steel, the evaluation of surface roughness, material removal rate, cutting temperature and tool wear under the CBCF compared to the mineral oil based cutting fluid (MBCF) were carried out using Taguchi experimental design and Grey Relational Analysis (GRA) for multi-response optimization. The formulated cutting fluid showed pH value of 8.47, viscosity of 0.830 mm2/s, good resistance to corrosion, good stability and milkfish in colour. From the GRA, the multi-response optimal factor combination under the CBCF is (1250 rev/min) spindle speed, (0.6 mm/rev) feed rate and (1.0 mm) depth of cut, all at level 3 while under the MBCF, it also shows (1250 rev/min) spindle speed, (0.6 mm/rev) feed rate and (1.0 mm) depth of cut all at level 3. The parameters from Taguchi and GRA results are in agreement with results from other vegetable oil based fluids and this study also contributes and improves the science of machining.

Optimization of process parameters in plasma arc cutting of commercial-grade aluminium plate

Abstract Plasma arc cutting (PAC) has emerged as a versatile and efficient method for the precision cutting of various materials, including commercial-grade aluminium plates. The optimization of process parameters is crucial for achieving high-quality cuts, minimizing material wastage, and enhancing overall productivity. This study aims to systematically investigate and optimize the key process parameters in PAC of commercial-grade aluminium plates. The experimental design involves the manipulation of parameters such as arc current, gas pressure, and workpiece thickness. A Design of Experiments approach, specifically Taguchi’s orthogonal array, is employed to efficiently explore the parameter space and identify the optimal combination of settings. The response variables considered for optimization include minimum surface roughness, minimum burr height, and maximum material removal rate (MRR). Analysis of variance is performed to get the percentage influence of each process parameter on the performance characteristic. The results obtained from the optimization process are expected to provide valuable insights into enhancing the efficiency and precision of PAC for commercial-grade aluminium plates. Arc current is found to be the most significant parameter in altering the surface roughness. The thickness of the material is the most significant parameter in altering burr height. None of the parameters is found to be significant in altering the MRR from Analysis of Variance analysis. From signal-to-noise ratio analysis and average performance graph, the optimum combination of processes in altering the bur height and MRR are found as arc current at 50 amp, the gas pressure at 5.4 bar, and the thickness of the workpiece at 6 mm.

Influence of Awj Parameters on Material Removal Rate, Surface Roughness, and Kerf Angle during Machining of Prosopis Juliflora Bark Fibres/Carbon Reinforced Hybrid Polymeric Composites

This study investigates the impact of changing the settings of the Abrasive Water Jet (AWJ) on the material removal rate (MRR), surface roughness, and kerf angle during the machining process of carbon-reinforced hybrid polymeric composites and Prosopis juliflora bark fibres. The hybrid nature of these materials presents unique machining opportunities as well as difficulties. As a result, optimizing the AWJ settings is essential for productive and efficient machining. The findings demonstrate the intricate connections between the AWJ parameters and the machining characteristics of the hybrid composite. To optimize MRR while minimizing surface roughness and controlling the kerf angle, the optimal parameter combinations are identified. The findings provide valuable insights for the manufacturing industry, particularly concerning sustainable materials and hybrid composites.

Experimental investigation during nano powder added µ-ED milling on nickel-titanium shape memory alloy

Nickel-titanium (NiTi) shape memory alloy (SMA) is one of the smart materials which has a vast application in the aerospace and biomedical industry due to its shape memory effect (SME), strong corrosion and wear resistance. Thus, the present study investigates the influence of process variables like gap voltage (V), powder concentration (PC) and pulse on time (ton) towards the material removal rate (MRR), surface roughness (SR) and micro-hardness (MH) amid graphene nano powder added micro-electrical discharge milling (µ-ED milling) of NiTi SMA. ANOVA analysis has been carried out to determine the % distribution of individual machining parameters towards all responses. The addition of graphene nano particles to the dielectric oil considerably enhanced the MRR, MH, and decreased the SR of the milled micro-channel. Taguchi’s grey relational analysis (GRA) and grey-desirability approach have been applied for multi-response optimisation to find the maximum MRR, MH, and minimum SR. It is discovered that the grey-desirability method improves all the responses. The Field emission scanning electron microscopy (FESEM) micro-graphs confirm defect-free mirror-like surface finish at the optimal grey-desirability setting. The X-ray diffraction (XRD) analysis discovered the formation of oxides and hardened phases in the machined surface. The application of graphene enhanced the recast layer thickness and reduced the indentation depth at subsurface region. The nano indentation test validates the rise in nano hardness and elastic modulus at the grey desirability optimal setting compared to the initial machining condition.

Additive manufacturing of yttria-stabilized zirconia using digital light processing: Green density and surface roughness analysis

The amalgamation of digital light processing (DLP) technology and pressureless sintering has emerged as a promising method for producing delicate, complex, and custom-made yttria-stabilized zirconia (YSZ) components. The procedure consists of fabrication of green part using DLP, debinding to remove the binders and sintering to densify the parts. Explorations in the parameter selection in each step are still far from maturity for producing defect-free densified parts. This study addresses the effect of DLP print parameters on the density of green parts and surface irregularities. DLP printing parameters, such as layer thickness, orientation, and light intensity, were selected, and a central composite design methodology was adopted for the design of experiments to investigate the impact of individual parameter as well as their interactions. The density of the green part decreased with an increase in layer thickness from 10 μm to 70 μm, whereas an increase in density was observed when the part orientation changed from 0 ° to 90°, and the intensity changed from 10 to 50 mW/cm2. The variations in layer thickness and part orientation substantially impact the surface roughness, but variations in intensity have comparatively less influence on the surface roughness. The in-layer and inter-layer voids of the samples fabricated with varying parameters were analyzed using scanning electron microscopy. The minimum surface roughness and maximum green part density responses were obtained using regression equations and a multi-objective optimization strategy. Subsequently, the samples were debinded and sintered to produce high-density zirconia components. To establish the importance of parameter optimization, a comparison of the specimen density and surface irregularity in the green and sintered states was performed for specimens fabricated with optimized parameters and a randomly chosen set of parameters. The novel study provides the parametric relation to fabricate as desired green and sintered density parts along with tuned surface roughness.

A comparative study on milling-induced surface roughness during milling of jute–epoxy and carbon–epoxy composites and optimization for better surface finish through Taguchi and RSM techniques

During the present experimentation, milling machining was performed on two different composites, namely carbon fiber-reinforced polymer composite and jute fiber-reinforced polymer composite, using a computer numerical control vertical machining center. The selected machining parameters were spindle speed (S), feed rate (FR), depth of cut (DOC), and flute number or cutting edge number (FN). The output parameter is the machined surface roughness (Ra). Analysis of variance was used to predict the percentage influence of each parameter on machining quality. The parameter feed rate exhibited a higher influence on the machined surface roughness, followed by spindle speed, flute number, and depth of cut in sequence. Similarly, while milling the carbon fiber composite, the feed rate had the highest influence, followed by the parameter flute number. As for the surface roughness, the feed rate had a greater effect, followed by the spindle speed. Under the same machining conditions, it was observed that the sequence of parameters influencing the jute composite and carbon composite changed in the case of cutting force generation, but the sequence of parameters was the same for both cases in terms of roughness. The outcome of the work confirmed that to achieve a smaller value of roughness in the milling of jute–epoxy composite, the optimum combination should be S = 3000 rpm, FR = 800 mm/min, DOC = 0.25 mm, and FN = 6. Similarly, to achieve the minimum surface roughness value in the milling of carbon–epoxy composite, the optimum combination of parameters should be S = 600 rpm, FR = 100 mm/min, DOC = 0.25, and FN = 6. The average roughness values obtained during the milling of jute–epoxy composite and carbon–epoxy composites are 6.685 and 3.08 μm, respectively. In this present work, it is proved that the optimum combination of parameters to get the minimum surface roughness and the amount of surface roughness produced during milling are highly influenced by the type of reinforced material. The graphs are prepared for the entire range of input parameters to identify the intermediate Ra value at any input value without the use of software.

Multi-objective optimization for balancing surface roughness and material removal rate in milling hardened SKD11 alloy steel with SIO2 nanofluid MQL

In manufacturing practice, manufacturers always strive to achieve both quality and productivity targets simultaneously. In the first part, this study examines the relationship between input factors, including cutting speed, depth of cut, and feed rate, and the output response, which is surface roughness, when milling hardened SKD11 alloy steel under minimum coolant lubrication conditions using SiO2 nanofluid. The input parameters are divided into four levels to determine their influence on surface roughness and to find the optimal conditions for achieving the minimum surface roughness. The experimental design was conducted using an L16 array. A second-order regression model was developed to describe the relationship between the input variables and the output response. In the second part, multi-objective optimization was performed to simultaneously achieve the minimum surface roughness and the maximum material removal rate (MRR). The Response Surface Methodology (RSM) was employed in this study. The results indicated that to achieve the minimum surface roughness, machining should be performed at a cutting speed of 100 m/min, a cutting depth of 0.2 mm, and a feed rate of 0.01 mm/tooth. With these settings, the predicted surface roughness could reach 0.0451 µm. On the other hand, for the multi-objective optimization, to achieve the minimum surface roughness and the maximum MRR simultaneously, machining should be carried out at a cutting speed of 100 m/min, a cutting depth of 0.36 mm, and a feed rate of 0.0168 mm/tooth. With this cutting condition, the predicted surface roughness could reach 0.1069 µm, and the predicted MRR could reach 775.06 mm3/min

A Comprehensive Study on the Optimization of Drilling Performance in Hybrid Nano-Composites and Neat CFRP Composites Using Statistical and Machine Learning Approaches

Carbon fiber-reinforced polymer (CFRP) composites have gradually replaced metals due to their exceptional strength-to-weight ratio compared to metallic materials. However, the drilling process often reveals various defects, such as surface roughness, influenced by different drilling parameters. This study explores the drilling quality of uni-directional CFRP composites, as well as hybrid Al2O3 alumina and hybrid SiC silicon carbide nano-composites, through experimental exploration using step, core, and twist drills. Response surface methodology (RSM) and statistical tools, including main effect plots, ANOVA, contour plots, and optimization techniques, were used to analyze the surface roughness of the hole. Optimization plots were drawn for optimal conditions, suggesting a spindle speed of 1500 rpm, feed of 0.01 mm/rev, and a 4 mm drill diameter for achieving minimum surface roughness. Furthermore, two machine learning models, artificial neural network (ANN) and random forest (RF), were used for predictive analysis. The findings revealed the robust predictive capabilities of both models, with RF demonstrating superior performance over ANN and RSM. Through visual comparisons and error analyses, more insights were gained into model accuracy and potential avenues for improvement.

Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms

Because of its versatility and ability to work with difficult materials, Electrical Discharge Machining (EDM) has become an essential tool in many different industries. It can produce precise shapes and intricate details. EDM has transformed fabrication processes in a variety of industries, including aerospace and electronics, medical implants and surgical instruments, and the shaping of small components. Its capacity to machine undercuts and deep cavities with little material removal makes it ideal for producing complex geometries that would be challenging or impossible to accomplish with conventional machining techniques. Several attempts have been carried out to solve the optimization problem involved in the EDM process. This paper emphasizes optimizing the EDM process using three metaheuristic algorithms: Glowworm Swarm Optimization (GSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). The study's outcome showed that the GWO algorithm outperformed the GSO and WOA algorithms in solving the EDM optimization problem and achieved the minimum surface roughness value of 1.7593µm.

Impact of process parameters in drilling of glass epoxy composite with clam shell and cenosphere filler: A comparative analysis

Drilling is one of the secondary machining techniques most frequently utilised when Glass Fibre Reinforced Polymer (GFRP) materials are assembled into a structure. The present work examines the drilling GFRPs and analyses hole quality based on the different traditional parameters: feed rate, spindle speed, and drill tool diameter. However, the hybridization of glass fiber- reinforced polymers (GRPs) by incorporating fillers as secondary reinforcement in the present piece of research has introduced new challenges for hole drilling and achieving the desired surface finishes. The delamination at the hole entrance and exit, considering the drill diameter of the HSS twist drill (6, 8, 10 mm), feed rate (0.04, 0.08, 0.12 mm/rev), cutting speed (1000, 1200, 1400 rpm), and filler content (0, 10, 20 wt.%) in GFRP, are the major factors are considered. The fillers used are clamshell powder and cenosphere, which are marine waste and thermal power plant waste, respectively. The present paper aims to study the effect of these fillers on the drilling-induced damages to the holes in terms of delamination factor, surface roughness, and dimensional accuracy of the hole (taper error, circularity error) of GFRPs. The most widely preferred Taguchi experimental design has been used for optimising the process parameters to obtain minimum delamination, surface roughness, and hole error. The study also aims to develop a correlation between feed rate, cutting velocity, drill diameter, and filler content with the delamination factor, surface roughness, taper error, and circularity error in a GFRP material. The observed results show that clamshells filled GFRP shows lower delamination, surface roughness, circularity error, and taper error compared to cenosphere filled GFRP composites under the same process parameters. It indicates the positive impact of the fillers in the drilling of GFRP, where the main objective is to reduce drilling-induced defects.