Optimization Of Process Variables Research Articles

Bio2Py: An API for integrating Python with BioWin for enhanced data acquisition in wastewater treatment simulations

Commercial simulators for modeling wastewater treatment processes (WWTPs), such as BioWin, play a crucial role for optimizing the design, operation, and control of treatment facilities. These simulators offer fast and user-friendly interfaces and can be employed to enhance the efficiency of wastewater treatment plants. However, a notable limitation of BioWin and similar platforms is the lack of direct integration with Python®, a programming language renowned for its versatility and extensive data analysis and machine learning libraries. Bridging this gap constitutes an opportunity to significantly improve the simulation process by leveraging Python's capabilities. The Python package Bio2Py (BioWin to Python) is presented as a compelling solution, reconciling BioWin's specialized functionality and Python's automation and data processing strengths. By utilizing Python's PyAutoGUI package, Bio2Py automates various aspects of the simulation process, such as loading influent data, running simulations, and saving simulation results without manual intervention. The integration of BioWin with Python not only speeds up the workflow by automating repetitive tasks but also opens new possibilities, such as parameter calibration for specific wastewaters and using BioWin as a data generator for machine learning applications. This brief communication presents the Bio2Py API, illustrating its use and applicability through a simple implementation example. Bio2Py's ability to automate and optimize various aspects of wastewater treatment process simulation, including data acquisition, and its potential use for process variable optimization and parameter tuning, positions it as a valuable tool for engineers and researchers who work in this field.

A physical simulation-machine learning model for optimal process schemes in laser-based directed energy deposition process

The major challenge faced is the definition of optimal process variables for rich quality of fabricated parts in laser-based directed energy deposition (DED-LB) processes. However, predicting the optimal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DED-LB. In view of this, a physical simulation (PS)-machine learning (ML) model using a relatively small accurate data set of 31 simulation data for optimal process schemes is proposed in DED-LB process in this study. Firstly, a powder-scale high precision phenomenon model incorporating the mass transfer, phase transformations and heat transfer and using Lagrangian particle model to add mass is developed to depict the DED-LB process. Then, a Gaussian process regression (GPR) agent model is established to rapidly and accurately predict the geometry and dilution rate of deposition tracks under different manufacturing parameters based on the high precision simulation results. Finally, a PS-ML model for process parameter optimization is developed using the particle swarm algorithm (PSO), and the optimized parameters are experimentally validated. The results show that the developed powder-scale model and GPR model results are consistent with the Inconel 718 alloy experimental results. The proposed PS-ML model can increase the accuracy of the ML models even with a small simulation data set. None of PS-ML model optimization results has a relative error of more than 3% to the experimental results, and the dilution rate is reduced by up to 61.66% compared to the experimental design parameters without optimization. The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters.

Determination of selective laser melting process parameters of tungsten carbide powders coated with nickel via electroless plating for improved interface properties

Tungsten carbide powders are commonly used at various industrial applications due to their high hardness. However, these powders may exhibit low binding and processing challenges without a binder. Therefore, various binding methods such as electroless Ni coating are used to hold WC powders together, increase their processability and improve the mechanical properties of the final product. Electroless Ni plating plays an important role in the use of WC powders, especially in processes such as selective laser melting. The structural, tribological, and mechanical properties of parts manufactured by SLM method are significantly influenced by the parameters employed in the process. Hence, optimizing process variables is essential to unlock the full potential of this advanced manufacturing technique. The main objective of this study was to identify the optimum process parameters for the SLM method of WC-Ni composite powders, which were prepared by coating WC powder materials with electroless Ni. Experimental investigations focused on analyzing the laser power, scanning speed, and hatch spacing parameters. The effects of these parameters on the mechanical and tribological properties of WC-Ni composite material were investigated using microhardness tester, tribometer device, optical microscope, 3D optical profilometer and scanning electron microscope. Eventually, it was determined that the manufacturing parameters employed in the SLM method had a significant impact on relative density, microstructure, surface roughness, hardness, and tribological properties. Samples exhibited a relative density of nearly 99%, and the highest observed hardness value was 1962 HV. This article supports a specialized SLM manufacturing process developed specifically to enhance durability and performance across a broad spectrum of industrial applications through interface improvement and the optimization of manufacturing parameters.

QbD Based Formulation Development and Optimisation of Ozenoxacin Topical Nano-Emulgel and Efficacy Evaluation Using Impetigo Mice Model.

To formulate and optimize Ozenoxacin nano-emulsion using Quality by Design (QbD) concept by means of Box-Behnken Design (BBD) and converting it to a gel to form Ozenoxacin nano-emulgel followed by physico-chemical, in-vitro, ex-vivo and in-vivo evaluation. This study demonstrates the application of QbD methodology for the development and optimization of an effective topical nanoemulgel formulation for the treatment of Impetigo focusing on the selection of appropriate excipients, optimization of formulation and process variables, and characterization of critical quality attributes. BBD was used to study the effect of "% of oil, % of Smix and homogenization speed" on critical quality attributes "globule size and % entrapment efficiency" for the optimisation of Ozenoxacin Nano-emulsion. Ozenoxacin loaded nano-emulgel was characterized for "description, identification, pH, specific gravity, amplitude sweep, viscosity, assay, organic impurities, antimicrobial effectiveness testing, in-vitro release testing, ex-vivo permeation testing, skin retention and in-vivo anti-bacterial activity". In-vitro release and ex-vivo permeation, skin retention and in-vivo anti-bacterial activity were found to be significantly (p < 0.01) higher for the nano-emulgel formulation compared to the innovator formulation (OZANEX™). Antimicrobial effectiveness testing was performed and found that even at 70% label claim of benzoic acid is effective to inhibit microbial growth in the drug product. The systematic application of QbD principles facilitated the successful development and optimization of a Ozenoxacin Nano-Emulsion. Optimised Ozenoxacin Nano-Emulgel can be considered as an effective alternative and found to be stable at least for 6months at 40°C / 75% RH and 30°C / 75% RH.

Ultrasonication-Assisted Aqueous Extraction of Waste Orange Peel Polyphenols: Optimization of Process Variables and Effect on Extract Composition

Ultrasonication-Assisted Aqueous Extraction of Waste Orange Peel Polyphenols: Optimization of Process Variables and Effect on Extract Composition

Multi-stage batch adsorption of acephate onto cauliflower like Fe3O4-MMT: Characterization and statistical optimization using response surface methodology

Globally acephate (an organophosphate pesticide) contaminates water bodies, and detriment to the biota is cancer-causing and neurotoxic which needs to be safely removed. This study presents the synthesis and characterization of magnetic montmorillonite (Fe3O4-MMT) as an adsorbent for the adsorption of acephate. The features and characteristics of the nanocomposite were traced by XRD, SEM-EDX, gas sorption analysis, FTIR, and XRF. RSM techniques were used to identify the optimal process variables that result in the highest removal. The numerical optimization of optimum variables corresponds to an initial acephate concentration of 2 mg/L, pH 6 and material adsorbent dose of 0.5 g/L. The uptake of acephate achieved 83.18 % under optimum environs. Dual factors i.e., concentration and dosage remarked as vital parameters that affected the response from ANOVA. Results revealed that equilibrium adsorption data were best fitted with Langmuir and kinetic data were well described by pseudo-first order kinetic model. Thermodynamic parameters such as enthalpy, entropy and Gibb’s energy were evaluated and the effect of temperature on acephate adsorption was studied. Greater acephate adsorption onto on Fe3O4 serves as an excellent material for pesticide mitigation.

Experimental analysis and optimization of machining parameters for Nitinol alloy: A Taguchi and multi-attribute decision-making approach

Abstract The automotive and aerospace sectors have a strong demand for Nitinol alloy machined parts; therefore, optimizing machining parameters is essential to achieving better process performance results in terms of cost and product quality. In general, the process variables that influence machining include feed (f), depth of cut (t), and spindle speed (S). Material removal rate (MRR), tool wear (TW), and surface roughness (Ra) are pertinent output performance indicators. Analysis of variance has been performed to assess the effect of process variables on the aforesaid output performance. It has been found that feed has a significant effect on MRR and surface roughness with a contribution of 50.65 and 33.62%, respectively, whereas spindle speed has a major contribution on TW with a contribution of 51.9%. This study assesses how well the Nitinol 56 machining process works overall. In this work, the Taguchi method has been used to determine the effect of aforesaid process variables on the output performance indices. To satisfy previously stated conflicting performance indices, a variety of multi-attribute decision-making approaches were used, such as utility, TOPSIS, and grey, to determine the optimal process variables. The optimal process variable combination has been achieved as f = 0.133 mm·rev−1, d = 0.06, and S = 835 RPM. This combination has been achieved using all methods.

Multi objective optimization of process variables using grey relational analysis for drilling of hybrid composites (LM6/B4C/fly ash)

Aluminium alloys continue to be an effective material for the aviation and automotive sectors in spite of the development of composites and other lightweight materials because of their sophisticated manufacturing techniques, strong durability against fatigue fracture propagation, and excellent impact endurance. As numerous holes are needed for creating rivets, drilling is the most difficult of all the machining operations. The primary problems with drilling these aluminium alloys are represented by a low hole quality, which increases the risk of airframe construction defects and lowers reliability. As a result, parts get discarded throughout the process of assembling, which affects the overall cost of production. To satisfy the needs of machined parts, the selection of Feed rate (F), Spindle Speed (S), Drill Material (D) and percentage of reinforcements (R) as well as drilling equipment, is necessary. The objective of this research work is to study the impact of cutting parameters on surface roughness (SR) and thrust force (TF) during drilling Hybrid MMCs, which were prepared by stir casting. The reinforcements used for fabrication were fly ash and boron carbide. A computer numerical control VMC with a cutting tool dynamometer for measuring TF was used for the experiments. The investigations used the L18 orthogonal array as their base of operation. The multi response optimization considering TF and SR was carried out using grey relational analysis (GRA) and the optimal level of cutting parameters like ‘F’, ‘S’, ‘D’ and ‘R’ were identified to obtain low TF and SR. The optimal machining parameters which give low TF and low SR are at 6 % reinforcement, coated carbide drill, S 3000 rpm and F 50 mm/min. Feed Rate (67.20 %) has the most significance on GRG trailed by S (15.88 %), Drill Material (6.39 %) and reinforcement percentage (3.65 %). Confirmation experiments demonstrated that grey relational analysis precisely optimized the process variables in drilling of hybrid MMCs.

TAGUCHI-GREY OPTIMIZATION OF SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE ON ELECTRO-DISCHARGE MACHINING OF NOVEL AA7075-TiO2 METAL MATRIX COMPOSITE USING WASTE CORNCOB BIOSILICA DIELECTRICS

In this study, a novel AA7075-TiO2 metal matrix composite was machined utilizing a biosilica mixed EDM technique and the surface roughness and material removal rate are optimized. The biosilica particles are produced from waste maize cobs and then silane-treated. The optimization of process variables wasperformed using Taguchi grey relational approach with a process variable of peak current, gap voltage and pulse-on time. Results revealed that the gap voltage is the most important process variable, since it has a larger max-min difference of 0.25. In order to create a high MRR of 11.6[Formula: see text]mm3/min and a surface roughness of 2.25 m, the maximum GRG of 0.79 for Trial 1 (A2B1C3) represents the most ideal process variable group. The best results appear to be obtained with a peak current of 10 A, a gap voltage of 20[Formula: see text]V, and a pulse-on time of 140[Formula: see text][Formula: see text]s. The new GRG, however, is around 2.51% better than the anticipated optimized process variables of A2B1C3 with an old GRG of 0.79, according to the confirmation research. The new MRR of 11.89[Formula: see text]mm3/min and the surface roughness of 2.30[Formula: see text]s with a GRG of 0.81 are based on the optimized new process variables (A1B1C3).

Integrated optimization of common rail direct injection diesel engine input parameters with linseed biodiesel: A hybrid approach using grey relational analysis and genetic algorithm techniques

In the pursuit of improved productivity and efficiency, the search for alternative fuels that reduce dependence on fossil fuels is of utmost importance. This research study aims to investigate the influence of process parameters, namely blend percentage, fuel injection pressure, exhaust gas recirculation (EGR) rate, and engine load, on various engine performance parameters such as torque, brake thermal efficiency (BTE), brake mean effective pressure (BMEP), hydrocarbon emissions (HC), and mechanical efficiency. The experiments are conducted following the Taguchi orthogonal array (L25) design of experiments. Additionally, grey relational analysis, a multi-objective optimization technique, is applied to identify the optimal levels of the process variables that optimize all the response parameters. The optimal values obtained through grey relational analysis are determined as 0% blend, 600 bar fuel injection pressure, 12% EGR rate, and 12 kg engine load. Moreover, the Genetic Algorithm-based Multi-Objective Genetic Algorithm (MOGA) is employed for multi-objective optimization of the engine input variables, yielding optimal levels of 19.45% blend, 594.35 bar fuel injection pressure, 14.12% EGR rate, and 12 kg engine load. Furthermore, multivariable regression models are developed to predict the response variables within the experimental domain. These models are validated through a confirmation test. The findings of this study provide insights into the optimization of process variables for enhanced engine performance, with the developed models serving as valuable tools for future predictions and optimization.

Optimization of mango peel pectin extraction (Mangifera indica L.): For the production of jam and jelly

Pectin extracted from mango (Mangifera indicia L.) peel is commonly utilized as a gel-forming agent in food and pharmaceutical applications. In Ethiopia, there is a substantial amount of mango fruit production and the peel removed from mango fruits are discarded as waste. However, there was limited scientific data on the development of pectin from mango peel and its utilization in jam and jelly making. Therefore, the study aimed to develop pectin from mango peel and utilization optimized pectin in jam & Jelly developments. For expermental work, the mango kent variety was collected from the Bako Agricultural Research Center. Seventeen experimental runs were generated after the minimum and maximum temperatures (60–100 °C), time (60–120 min) and pH (2–3) were adjusted by Design Expert® software version 13.0.5.0 using the Response Surface Methodology. All physicochemical and sensory properties were determined using standard procedures and analytical graded chemicals. The results showed that the highest (64.63 %) extraction yield of mango peel pectin was recorded at 100 °C, 60 min and pH.2. Optimum process variables for pectin yield (65.20 %) extracted from dried mango peel were recorded at temperature 94.64 °C, time 118.67 min, and pH 2.01,The optimized mango pectin yield has 497.5 equivalent weight, 6.15 methoxy, 70.3 anhydrouronic acid, and 49.69° of esterification content. Mango jam and jelly were developed using optimized pectin yield of 3.64 and 3.42, 4.30 and 4.46 for pH and titrable acidity, respectively. The results of the sensory panelists showed that mango jam and jelly developed with pectin significantly (p < 0.05) liked the aroma and general acceptability compared to the control sample. The findings highlighted that the pectin optimized from dried mango peel has a good potential to develop jams and jellies with better physicochemical properties and sensory acceptability.

Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from Phyllantus emblica

This study investigates the feasibility of extracting bioactive antioxidants from Phyllantus emblica leaves using a combination of ethanol-water mixture (0-100%) and heat-assisted extraction technology (HAE-T). Operating temperature (30-50°C), solid-to-liquid ratio (1:20-1:60 g/mL), and extraction time (45-180 min) were varied to determine their effects on extract total phenolic content (TPC), yield (EY), and antioxidant activity (AA). The Box-Behnken experimental design (BBD) within response surface methodology (RSM) was employed, with multi-objective process optimization using the desirability function algorithm to find the optimal process variables for maximizing TPC, EY, and AA simultaneously. The extraction process was modeled using BBD-RSM and multi-gene genetic programming (MGGP) algorithm, with model reliability assessed via Monte Carlo simulation. HPLC characterization identified betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid as bioactive constituents in the extract. The study found that a 50% ethanol solution yielded the best extraction efficiency. The optimal process parameters for maximum EY (21.6565%), TPC (67.116 mg GAE/g), and AA (3.68583 µM AAE/g) were determined as OT of 41.61°C, S:L of 1:60 g/mL, and ET of 180 min. Both BBD-RSM and MGGP-based models satisfactorily predicted the observed process responses, with BBD-RSM models showing slightly better performance. Reliability analysis indicated high certainty in the predictions, with BBD-RSM models achieving 99.985% certainty for TPC, 97.569% for EY, and 98.661% for AA values.

Kinetics, equilibrium and thermodynamics investigations of polypyrrole and polyaniline composites with Oryza sativa biomass for the removal of Nitenpyram insecticide

Abstract In this research, composite materials were prepared by combining polypyrrole (PPY) and polyaniline (PAN) with Oryza sativa (OS). These biocomposites were utilized for the adsorption of Nitenpyram (insecticide), from aqueous solutions. The study involved the optimization of various process variables including contact time, pH, adsorbent dose and initial Nitenpyram concentration. The maximum sorption efficiency of OS for Nitenpyram was observed at pH 2, biosorbent dose of 0.05 g, an initial Nitenpyram concentration of 125 mg/L and contact time of 30 min. To analyze the Nitenpyram adsorption process, the adsorption data for OS and its composites (OS-PPY and OS-PAN) were subjected to equilibrium isotherms and kinetics models (pseudo-first and pseudo-second-orders). The Langmuir isotherm and pseudo-second-order explained the Nitenpyram adsorption data. Furthermore, the feasibility of the adsorption was evaluated by employing thermodynamic studies. The outcomes of the study demonstrated the promising efficiency of the synthesized biocomposites for Nitenpyram removal, which was 37.03, 43.0 and 35.71 (mg/g) in the case of OS, OS-PPY and OS-PAN composites. These findings suggest that the composites have promising potential for the removal of pesticides from wastewater, contributing to the development of effective strategies for environmental remediation.

Rice husk composite with polyaniline, sodium alginate and polypyrrole: naphthalene adsorption kinetics, equilibrium and thermodynamic studies

Abstract In this investigation, composites consisting of polypyrrole (PPy), polyaniline (PAN), and sodium alginate combined with rice husk (RH) biomass were fabricated, utilizing them as adsorbents for naphthalene. The optimization of key process variables, including pH, pesticide concentration, composite dosage, contact time, and temperature were systematically undertaken to enhance the removal efficiency of naphthalene. Notably, the composites exhibited promising efficacy in adsorbing naphthalene, with native rice husk and PPy/RH (at pH 4), PAN/RH (at pH 5), and Na-alginate/RH (at pH 6) displaying the highest removal rates. Optimized conditions for composite dosage, temperature, and contact time were determined as 0.05 g, 30 °C, and 90 min, respectively, ensuring efficient removal of naphthalene. The adsorption capacities for naphthalene were found to be 22.04, 23.15, 23.89 and 21.67 (mg/g) for RH, PAN/RH, PPY/RH and Na-alginate/RH, respectively. The Langmuir isotherm and pseudo-first-order kinetics models aptly described the adsorption process onto the composite material. The surface morphology and functional groups involved in the biocomposite formation were examined through scanning electron microscope (SEM) and Fourier-transform infrared (FTIR) techniques. These analyses confirmed that PPy, PAN, and Na-alginate composites with RH biomass exhibit high effectiveness in naphthalene removal, showcasing their potential application in the remediation of naphthalene from effluents.

Response Surface Methodology Approach to Optimize the Expression of Thioredoxin-MOG Fusion Protein

Background: The N-terminal domain of the myelin oligodendrocyte glycoprotein (MOG) has been shown to generate experimental autoimmune encephalomyelitis (EAE), an animal model of MS. A considerable amount of MOG must be accessible for EAE induction. Here, for the first time, Response Surface Methodology-Box-Behnken (RSM-BBD) was employed to identify the ideal culture conditions for causing Escherichia coli (E. coli) BL21 to overproduce the Thioredoxin-MOG (Trx-MOG) fusion protein. The RSM method is a powerful, efficient, and reliable alternative to the One-Factor-At-A-Time (OFAT) method in optimizing process variables, allowing for a smaller number of experimental runs, investigating variable interaction, and being cheaper and less time-consuming. Methods: Here, using the 29 experimental assays, the direct and indirect effects of factors including post-induction time, IPTGinducer concentration, pre-induction optical density, and post-induction temperature on the protein expression level content were evaluated. Results: The proposed quadratic model demonstrated a significant effect of the two variables A (time) and C (temperature) on protein synthesis. An inducer concentration of 0.491 mM, the pre-induction optical density (OD600) of 0.8, and a temperature of 23 °C for 23.878 hours were found to be the best growth conditions for high yield Trx-MOG synthesis. The optimum protein concentration was attained (163.96 µg/mL) and was within the range of (200.04 µg/mL), which was the value predicted. Conclusion: The study concluded that RSM optimization effectively increased the production of Trx-MOG in E. coli, which could have the potential for large-scale fermentation.

Hydration behavior and kinetic studies of paddy cultivars: optimization of process variables for improved physical, milling and textural properties

Hydration behavior and kinetic studies of paddy cultivars: optimization of process variables for improved physical, milling and textural properties

Some studies on optimization of process parameters for wire ARC additive manufacturing of Hastelloy C276 using GRA-GWO hybrid techniques

Wire arc additive manufacturing (WAAM) has become an inevitable manufacturing technology in the present decade. This article discusses optimizing process variables for fabricating Hastelloy C276 alloy via WAAM. The controlling parameters are gas flow rate (GFR), deposition speed, and welding current, while the depth of penetration, top reinforcement, and bead width are the output parameters considered for optimization. The magnitude of interaction effects between control parameters is equal to that of individual impact on the weld bead characteristics evident from analysis of variance results. Grey relational analysis revealed the optimal parameters (trial 1 with I = 120 A, S = 200 mm/min, GFR = 15 L/min) chosen through its robust optimization procedure. S/N ratio analysis revealed the most influencing parameters at the factor level (current level 1, speed level 1). The Grey Wolf optimizer (GWO) algorithm was used as a precision and confirmation experiment. The GWO optimization results further confirmed that the GFR of 15 L/min, speed of 200 mm/min, and current of 120 A were the optimized parameters used in this design space. Further, normal probability plots with minimal error of around 1% reinforce that the parameters chosen in this design space are well within the optimal limits. The surface plots reported the relationship and variation between control and resulting factors.

Optimization of process variables and characterization of mechanically agitated ceramic‐microsphere reinforced aluminium composite

AbstractThis study aims to investigate the effect of stir‐casting process parameters, including stirring speed, stirring time, and weight % of the ceramic microsphere, on the fabrication of an aluminium 6061‐ceramic microsphere composite. Experiments are conducted using a full‐factorial design approach to determine the effect of variables on mechanical properties. A field effect scanning electron microscope is used to analyze the fracture surfaces of the test specimen. Further, the results are subjected to the Anderson‐darling normality test to validate the accuracy of the model, and finally, an analysis of variance is performed to identify the most significant parameters that affect the mechanical properties. The results confirm the validity of the designed experimental model for enhancing the mechanical properties of the aluminium 6061‐ceramic microsphere composite. The optimum mechanical properties are attained for a stirring speed of 600 min−1, stirring time of 15 min, and 5 weight % of reinforcement. Furthermore, the weight percentage of the ceramic microsphere increase, the ductility of the composite decreases while its strength and hardness increase. Finally, the analysis of variance shows that all parameters significantly impact the mechanical properties of the designed composite.

Response Surface Methodology: An Optimization of Process Variables for the Nanoencapsulation of Anthocyanins from Black Rice Bran

Nanoencapsulation technology has been used in food and pharmaceutical applications to increase bioactive chemical functioning and stability against external influences. To develop a cost-effective encapsulating procedure, additional optimization is required. This study employed response surface methodology (RSM) to optimize the encapsulation of anthocyanin-rich extract from black rice bran. The extract was encapsulated through pre-gelation and polyelectrolyte complex formation processes. Box-Behnken design was employed to determine the optimum conditions for the encapsulation process with the following process variables: chitosan concentration, pH, and CaCl2 concentration. Chemical characteristics, surface morphology, and particle size were used to describe the resultant capsules, which were then subjected to phytochemical analysis. The optimal encapsulation conditions for anthocyanin were 6.30 mg/mL chitosan, pH 5.5, and 36 mM CaCl2, with a 51.20 % encapsulation efficiency. The developed anthocyanin-loaded nanocapsule has a high TPC (3.87 mg GAE/g) and potent antioxidant activity (5.69 mg TE/g). SEM images revealed a smooth surface area and spherical particles that clumped together, with an average particle size of 94.70 nm. FTIR analysis corroborates the well-incorporation of anthocyanin into the nanocapsules. The encapsulation process of anthocyanin-rich extract from black rice bran was successfully optimized via RSM.

Optimizing processing parameters in electrical discharge milling of CFRP using metaheuristics

ABSTRACT The micro-channel fabrication in carbon fiber-reinforced polymer (CFRP) composite through electrical discharge machining (EDM) process followed by the comprehensive study of dimensional deviation in the conceived micro-channels are presented in this work. The factors namely, voltage (V), tool rotational speed (TRS), and feed rate (FR) were considered for assessing their impact on channel width deviation (CWD). In addition to the variance analysis,, two potential metaheuristic optimizers, the Butterfly optimization algorithm (BOA) and the Spotted Hyena optimization algorithm (SHO) were used to identify the optimal operating parameters of the aforementioned process. The BOA revealed that the optimal process variables were FR = 5.99 µm/s, V = 150 volt, and TRS = 577.08 rpm for a CWD = 74.96 µm. In contrary, SHO revealed that FR = 5.99 µm/s, V = 150 volt, TRS = 542.96 rpm, and CWD = 74.53 µm were the corresponding optimal values. Overcuts were observed in the fabricated micro-channels and a detailed illustration of the mechanism of overcut formation was provided.