Combination Of Process Parameters Research Articles

Optimizing mechanical properties of virgin and recycled PLA components using Anova and neural networks

The increasing demand for polymers in additive manufacturing (AM) has led to a significant increase in plastic waste, with over 300 million metric tons used in recent years. This research article explores the use of Poly Lactic Acid (PLA) as a biodegradable thermoplastic recycled material for 3D printed components, comparing its properties with virgin PLA and discussing solutions for variation and mechanical features improvement. Fused Deposition Modeling (FDM) is a widely used additive manufacturing process that allows for the creation of three-dimensional objects by depositing molten material layer by layer. This study investigates the impact of infill density, layer thickness, and raster angle for recycled 3D printing material, focusing on their dimensions and their influence on processing efficiency. This research paper aims to investigate the mechanical effects of recycled 3d printed components which are printed by using FDM with the combination of different process parameters compared with virgin PLA. From results optimal process parameters are found to enhancing quality and performance of recycled 3D printed components. Later results are compared by Analysis of Variance (ANOVA) as a statistical tool and also with ANN technique, which minimizes error deviation.

Study on the Forming Process and Properties of AlSi60 Alloy by Selective Laser Melting

Hypereutectic Al-Si alloys, which have a silicon content ranging from 12% to 70%, are a new generation of casing materials for chip packaging. They have broad applications in aerospace, weaponry, and civilian communications. Selective Laser Melting (SLM) offers significant advantages in achieving near-net shaping of complex casings. This paper presents a study on the formation defects, microstructure, and room temperature tensile properties of AlSi60 alloy prepared by SLM. The results indicate that the primary forming defects in the SLM AlSi60 alloy are balling, lack of fusion, and porosity. These defects are mainly influenced by the volumetric energy density. Samples of good quality can be produced within the range of 150 J/mm3 to 250 J/mm3. However, the same volumetric energy density can result in differences in sample quality due to various combinations of process parameters. Therefore, it has been determined that a well-formed AlSi60 alloy can be obtained within a laser power range of 300 W–350 W, scanning speed of 400 mm/s–800 mm/s, and hatch spacing of 0.09 mm–0.13 mm, with a density close to 98%. The microstructure of the SLM AlSi60 alloy consists of primary Si phases with irregular shapes and sharp edges measuring 5–10 μm, eutectic Si particles of 0.5 μm, and α-Al phases, with eutectic Si dispersed within the α-Al. The SLM AlSi60 alloy exhibits fine and evenly distributed primary Si phases with an average hardness of 203 HV. No significant anisotropy in hardness values was observed in the X and Y directions. The tensile strength of the alloy reached an average of 219 MPa, with an average elongation of 2.99%. During the tensile process, cracks initiated by the primary Si phases rapidly expanded, exhibiting minor ductile fracture characteristics in the Al phases. Due to the high volume fraction of Si phases, the tensile test was dominated by brittle fracture. The tensile curve only exhibited the elastic stage.

EXPERIMENTAL INVESTIGATIONS ON THE ELECTROCHEMICAL MACHINING OF D3 DIE STEEL MATERIAL AND MULTI-OBJECTIVE PARAMETER OPTIMIZATION

The removal of metal in the form of sludge can be termed an ECM process, having primary salient features consisting of the tool, workpiece, power supply and electrolyte solution. ECM is a potential nontraditional machining process that includes too many factors contributing to process performance. Therefore, obtaining optimal factor combinations for higher efficiency is complex. In this research work, machining parameters are voltage, powder concentration and electrolyte concentration, based on which material removal rate (MRR) and surface roughness (SR) are derived. Regression analysis, signal-to-noise ratios and analysis of variance (ANOVA) are performed to determine the optimal levels and to examine the effects of machining parameters on MRR and SR. The results derived from the experiments noticed that the MRR decreases when the voltage level is increased and surface roughness decreases for the applied voltage. In case of an increase in powder concentration, MRR decreases from range 4 to 6 and further reduces to 6–8. In case of SR, it additionally decreases on the maximum level of powder concentration. In electrolyte concentration, the MRR decreases on the increment of electrolyte and increases after a short period of time; simultaneously SR is increased from range value 10–20 and decreased with a maximum level 20–30 of NaCl solution. A comparison of MOORA-PCA and TOPSIS-PCA demonstrates the superiority of TOPSIS over MOORA technique. Statistical results (95% confidence level) indicate that the voltage, powder concentration, and electrolyte concentration affect the surface roughness by 69.50%, 8.67% and 14.59% in the electrochemical machining of d3 die steel. Therefore, the optimum combination of process parameters corresponding to voltage: 45[Formula: see text]V, powder concentration: 4[Formula: see text]g/L, and electrolyte concentration: 10 NaCl/L, respectively, were found to yield the desired result. The prediction accuracy of the TOPSIS-PCA hybrid approach model is found better than MOORA-PCA technique.

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

PurposeThe textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.Design/methodology/approachThis research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.FindingsThe results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.Originality/valueThis paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

Optimization of process parameters for laser cladding Stellite6 cobalt-based alloy

As a new remanufacturing technology, laser cladding can repair damaged parts in large valves in nuclear power plants. The objective is to enhance the surface morphology quality of parts post-remodeling, the paper optimizes the key process parameters in the laser cladding process. Numerical simulation was used to analyze the influence of each process parameter ( laser power, scanning speed, powder feeding speed) on the morphology of the cladding layer. The laser power and powder feeding rate were positively correlated with the width of the cladding layer, and the scanning speed was negatively correlated with the width of the cladding layer. The orthogonal experiments are designed based on the Taguchi method, and the Stellite6 single cobalt-based alloy cladding layer was prepared on the WCB low-carbon steel substrate. Analysis of variance and RSM was used to analyze the experimental results. The influence order of process parameters on the morphology of the cladding layer was determined. The regression prediction model was established to analyze the relationship between the process parameters and the morphology quality (width and height) of the cladding layer. The errors of the model are 5% and 3.5%, respectively. The optimal combination of process parameters is determined as follows: P = 1700 W, Vs = 8 mm/s, Vf = 26 g/min. The cladding layer prepared by the optimal process parameters has fine structure and good bonding. The accuracy of the predictions was validated using this model, which offered valuable theoretical guidance for predicting and controlling the geometric characteristics of the laser cladding of Stellite6 alloy.

Advancing scaffold porosity through a machine learning framework in extrusion based 3D bioprinting

Three Dimensional (3D) bioprinting holds great promise for tissue and organ regeneration due to its inherent capability to deposit biocompatible materials containing live cells in precise locations. Extrusion-based 3D bioprinting (EBP) method stands out for its ability to achieve a higher cell release rate, ensuring both external and internal scaffold structures. The systematic adjustment of key process parameters of EBP, including nozzle diameter, printing speed, print distance, extrusion pressure, material fraction, and viscosity allows for precise control over filament dimensions, ultimately shaping the desired scaffold porosity as per user specifications. However, managing these factors with all possible interactions simultaneously to achieve the desired filament width can be intricate and resource intensive. This study presents a novel framework designed to construct a predictive model for the filament width of 3D bioprinted scaffolds for various process parameters. A total of 157 experiments have been conducted under various combinations of process parameters and biomaterial’s weight fraction for this study purpose. A regression-based machine learning approach is employed to develop the predictive model utilizing Adj. R2, Mallow’s Cp, and Bayesian Information Criterion (BIC). Following model development, rigorous experimental validations are conducted to assess the accuracy and reliability of the model. Based on the cross-validation of randomly split test data, Adj. R2 model emerges as the highest performing machine learning model (Mean Squared Error, MSE = 0.0816) compared to Mallow’s Cp and BIC (MSE = 0.0841 and 0.0877, respectively) models. The comparative analysis results between the experimental and model’s data demonstrate that our predictive model achieves an accuracy of approximately 85% in filament width prediction. This framework presents a significant advancement in the precise control and optimization of 3D bioprinted scaffold fabrication, offering valuable insights for the advancement of tissue engineering and regenerative medicine applications.

Microstructure and Mechanical Properties of Cermet Composite Coating on TC4 Surface

In order to improve the surface quality and performance of TC4, the TC4-HfC-TaC metal-ceramic composite cladding coating was prepared on the surface of TC4 by using different process parameters by laser cladding technology. Through digital testing equipment, the macro morphology, molten pool size, dilution rate, Rockwell hardness (HRC), geometry, microstructure and other comprehensive properties of the coating are studied and analyzed. The results show that the performance characterization of TC4-HfC-TaC cladding layer is closely related to the melting state of refractory carbide ceramic phase HfC and TaC powder, when the laser beam energy density is not enough to completely melt the ceramic phase powder, with the increase of energy density, the coating surface tends to be flat, the molten pool size and dilution rate increase, the hardness gradually increases, the metallographic structure is refined uniformly, and the surface quality is improved. Excessive energy density of the laser beam can easily lead to excessive dilution rate of the cladding layer, reduced coating hardness, coarse metallographic structure, and reduced surface quality. Finally, the best combination of process parameters was screened out: laser power 3000 W, scanning speed 300 mm/min, spot size 2.5 × 11.5 mm, rectangular spot lap rate 40%, preset powder thickness 1.5 mm, at which time the coating hardness increased to 63.22 HRC. It is about 2.1 times the hardness of TC4 substrate, with the best performance, and the metallographic structure is densely arranged fine cell-like crystals, with outstanding microscopic characteristics, which realizes the purpose of preparing high-quality TC4-HfC-TaC cermet composite cladding coating on the surface of TC4 titanium alloy.

Process Parameter Optimization for CO2 Laser Polishing of Fused Silica Using the Taguchi Method.

Fused silica was polished to a high quality by a CO2 laser beam with a rapid scanning rate. The rapid scanning rate produced a line laser heat source, resulting in a "polishing line" during the polishing process. The Taguchi method was used to evaluate the comprehensive influence of polishing process parameters on the polishing qualities. Four factors, namely the length of laser reciprocating scanning (A), laser beam scanning speed (B), feed speed (C), and defocusing amount (D), were investigated in this study. The optimal process parameter combination (A1B1C1D1) was obtained. The surface roughness of fused silica was reduced from Ra = 0.157 μm to 0.005 μm. Through analysis of variance (ANOVA), it was found that laser beam scanning speed (B) had a significant influence on the polishing quality. The interaction of the two factors plays a decisive role in the determination of the best process parameters, and the influence of other multi-factor interaction can be ignored; the interaction between A × B is the largest, with a contribution of 42.69%.

Improvement of the Dimensional Accuracy of a Ti-6Al-4V Ripple Disc During Electric Hot Incremental Sheet Forming

The edge warpage of a Ti-6Al-4V ripple disc is a major forming defect during electric hot incremental forming, which can lead to a significant dimensional error. In this paper, a novel manufacturing method, namely the combination of electric hot incremental forming and electrically assisted sizing, has been proposed to improve the forming defect. The effect of process parameters on forming fracture was analysed in detail, and then an optimal combination of process parameters was obtained to ensure the successful forming of a Ti-6Al-4V ripple disc. On this basis, a sizing device and a sizing current were separately designed and analysed to eliminate the warpage defect of Ti-6Al-4V ripple discs. According to the experimental result, Ti-6Al-4V ripple discs can be satisfactorily fabricated through the method proposed.

Efficient low-carbon manufacturing for CFRP composite machining based on deep networks

ABSTRACT The drilling quality of carbon fibre reinforced polymer (CFRP) components is a key factor affecting the service life of the components, while energy saving and emission reduction in industrial production are crucial. In this study, drilling experiments were conducted on T300 plywood using a 55° coated tungsten steel drill bit, and CNN-LSTM neural network models were used to construct mapping relationships between process parameters (spindle speed, feed rate, and fibre lay-up sequence) and delamination factor and machine energy consumption. A new method of predicting the delamination factor by process parameters is proposed, and explored the optimal process parameter combinations that reduce the energy consumption of machine tools and minimise the delamination factor at the same time. The research results show that within the parameter settings, a spindle speed of 7000 r/min, a feed rate of 40 mm/min, and a lay-up sequence of [0°, 0°, −45°, 90°]6s ensure both low power consumption in the drilling process and the highest possible hole quality. This paper clearly demonstrates the feasibility of achieving low-power, high-quality drilling of CFRP through parameter optimisation, providing guidance to the manufacturing industry to improve the quality of CFRP hole-making while easing the pressure on carbon emissions.

Multi-objective Optimization of Injection Molding Process Based on One-Dimensional Convolutional Neural Network and the Non-dominated Sorting Genetic Algorithm II

<div>In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN). Input parameters from the injection molding process, including melt temperature, mold temperature, packing pressure, packing time, injection pressure, injection time, and cooling time, were used while warping deformation and volume shrinkage were considered as outputs. Global optimization was performed using the non-dominated sorting genetic algorithm II (NSGA-II), and the optimal combination of process parameters was evaluated using the criterion importance through intercriteria correlation—technique for order preference by similarity to ideal solution (CRITIC-TOPSIS). Moldflow analysis demonstrated that the obtained indicators outperformed the optimization results from orthogonal experiments, confirming the effectiveness of the injection molding process parameter optimization method based on 1D CNN-NSGA-II. In comparison to the pre-optimization results, product warping deformation decreased by 40.68%, and volume shrinkage reduced by 18.14%, and all of them meet the production requirements.</div>

Modeling of creep-fatigue features of hydrogen storage bed and its parameter optimizing based on finite element method and orthogonal experimental design with artificial neural networks

Regarding the creep-fatigue behavior of hydrogen storage bed (HSB) structures under high-temperature loading and internal pressure loading under repeated hydrogen absorption and desorption process, the heat transfer characteristics and creep-fatigue life during a complete service process in HSB were calculated using Ansys Workbench and Ncode software, and the reliability of the life results calculated by finite element method were validated using ASME code. Based on orthogonal experimental design (OED) theory, an orthogonal experimental table with 4 factors and 5 levels was compiled for the four process parameters: diameter of cooling tube, thickness of cylinder wall, spacing between inner and outer layers, and heat transfer coefficient of condensate. Moreover, an optimization method based on OED coupled with BP ANNs was proposed to find the best process parameters. And the importance of each parameter was analyzed based on range analysis and analysis of variance (ANOVA) to divide the process parameters into rough optimized parameters and precise optimized parameters in this method. In order to identify the optimal process parameter combination of HSB, the maximum life and corresponding process parameter combinations were identified based on OED and the combined method, respectively. The maximum life identified by the combined method proposed in this work is increased by 10.23 % than that identified by OED, which shows the superiority of this combined method.

Optimizing micro-EDM with carbon-coated electrodes: A multi-criteria approach

The pursuit of precision machining in engineering materials has ignited an imperative to advance electrical discharge machining (EDM) techniques. This study is a deep dive into the domain of micro-electrical discharge machining ([Formula: see text]EDM) with a specific focus on the utilization of carbon-coated tool electrodes. The primary objective is to elucidate the influence of carbon-coated electrodes on vital machining parameters, including machining depth (Z coordinate), tool wear rate (TWR) and overcut (OC) in [Formula: see text]EDM operations. Moreover, the investigation extends to scrutinizing surface characteristics generated by [Formula: see text]EDM with carbon-coated tool electrodes. To enhance performance, carbon-coated tungsten carbide tool electrodes are employed for machining titanium alloy (Ti–6Al–4V) workpieces. The optimization process seeks to identify the optimal combination of process parameters using a Taguchi-DEAR-based multi-response approach. Experimental findings reveal that integrating carbon-coated electrodes significantly improves the [Formula: see text]EDM process, leading to enhanced surface performance metrics. Notably, this study identifies specific parameter settings that effectively optimize machining quality indicators. By achieving a voltage of 160[Formula: see text]V, a capacitance of 10,000[Formula: see text]pF and a tool rotation of 600[Formula: see text]RPM, the research contributes valuable insights into the intricate realm of [Formula: see text]EDM, highlighting the potential for enhanced surface performance through strategic parameter manipulation.

Experimental investigation of 3D taper profile machining of SS304 using WEDM

ABSTRACT Wire electrical discharge machining (WEDM) is an outstanding variety of EDM processes in which the tool is a wire electrode. The medical, electronics, tool, and die industries use stainless steel 304 (SS304). However, there are traditionally many complications that manufacturers face, while machining SS304. Also, the creation of 3D shapes using WEDM is challenging. Due to difficulties in normal machining and producing 3D profiles using conventional machining of SS304, this research aims to utilize the application of WEDM for creating complex shapes or products with top as square and bottom as rectangular shape to get a rectangular frustum. A ϕ 0.25 mm brass wire was used, and experiments were conducted as per L9 array. Further, the determination of the best process parameter (Ton, Toff, Voltage, current) combination for achieving the highest MRR, hardness, and minimal surface roughness (Ra) and kerf with analysis of machined surfaces using an SEM microstructure image was tried. The results indicated that MRR and Ra values varied from 7.05 to 47.29 mm3/min and 1.251 to 2.019 µm, respectively. Furthermore, the kerf ranged from 0.294 to 0.475 mm and hardness started at 164 to 233 HV. To optimize WEDM results Grey relational analysis (GRA) was utilized.

Mesoscale Simulation of Laser Powder Bed Fusion with an Increased Layer Thickness for AlSi10Mg Alloy

Low performance is considered one of the main drawbacks of laser powder bed fusion (LPBF) technology. In the present work, the effect of the AlSi10Mg powder layer thickness on the laser melting process was investigated to improve the LPBF building rate. A high-fidelity simulation of the melt pool formation was performed for different thicknesses of the powder bed using the Kintech Simulation Software for Additive Manufacturing (KiSSAM, version cd8e01d) developed by the authors. The powder bed after the recoating operation was obtained by the discrete element method. The laser energy deposition on the powder particles and the substrate was simulated by ray tracing. For the validation of the model, an experimental analysis of single tracks was performed on two types of substrates. The first substrate was manufactured directly with LPBF technology, while the second was cast. The simulation was carried out for various combinations of process parameters, predominantly with a high energy input, which provided a sufficient remelting depth. The calculations revealed the unstable keyhole mode appearance associated with the low absorptivity of the aluminum alloy at a scanning speed of 300 mm/s for all levels of the laser power (325–375 W). The results allowed formulating the criteria for the lack of fusion emerging during LPBF with an increased layer thickness. This work is expected to provide a scientific basis for the analysis of the maximum layer thickness via simulation to increase the performance of the technology.

A multi-objective optimization workflow of ring-rolling process parameters based on production energy and time

Our industrial era is characterized by a significant attention to environmental aspects and production sustainability. Under this point of view, the minimization of the process energy represents a milestone leading to a consistent reduction of pollution. However, to promptly react to the high production rate market request, companies employ time saving manufacturing strategies, often in contrast with this principle. Considering this, an optimization concerning the reduction of both production time and energy, results to be strategical, especially when energy-intensive processes, such as ring rolling, are considered. Ring rolling is a shaping process performed by a dedicated equipment, composed of a driving roll, an idle roll, and a couple of axial rolls, allowing to obtain seamless rings while simultaneously reducing their height and thickness, while enlarging diameter. The kinematics of each component of the equipment can be set independently. This leads to several process conditions influencing quality, energy, and times, and makes it difficult the selection of the most suitable configuration. In this paper, a multi-objective optimization workflow is presented. Starting from validated FEM simulation campaign results, the proposed methodology allowed to individuate the best process parameters combination for limiting energy and time, encouraging its application to a wider range of energy-consuming processes.

Study on Machinability Behaviour and Simultaneous Optimisation of Multiple Responses Using Taguchi‐Based Grey Relational Analysis in End Milling of Aluminum Hybrid Composites

The present work reveals the machinability characteristics of Al7068/Si3N4/BN hybrid composite materials. The composite materials were prepared through the liquid metallurgy technique with various weight fractions (0, 5, 10) of silicon nitride and hexagonal boron nitride particles. The experimental studies were performed on a CNC milling machine by varying cutting speed (600, 1200 and 1800 rpm), feed rate (150, 300 and 450 mm/min), and depth of cut (0.8, 1.2 and 1.6 mm). Taguchi’s plan of experiments (L27 orthogonal array design) and analysis of variance (ANOVA) were employed to investigate the effect of process parameters on surface roughness (Ra), cutting temperature, and material removal rate (MRR). Taguchi‐based grey relational analysis (GRA) was performed to identify the best process parameters combination for optimising the multiple responses simultaneously. ANOVA results revealed that the weight fraction of Si3N4 is the most contributing parameter for multiple response characteristics.

PLA-Based Composite Panels Prepared via Multi-Material Fused Filament Fabrication and Associated Investigation of Process Parameters on Flexural Properties of the Fabricated Composite.

This study prepares composite panels with three Polylactic acid (PLA)-based materials via the multi-material fused filament fabrication method. The influences of four processing parameters on the mechanical properties of 3D-printed samples are investigated employing the Taguchi method. These parameters include the relative volume ratio, material printing order, filling pattern, and filling density. A "larger is better" signal-to-noise analysis is performed to identify the optimal combination of printing parameters that yield maximum bending strength and bending modulus of elasticity. The results reveal that the optimal combination of printing parameters that maximizes the bending strength involves a volume ratio of 1:1:2, a material sequence of PLA/foam-agent-modified eco-friendly PLA (ePLA-LW)/glass fiber-reinforced eco-friendly PLA (ePLA-GF), a Gyroid filling pattern, and a filling density of 80%, and the optimal combination of printing parameters for maximum bending modulus involves a volume ratio of 1:2:1 with a material sequence of PLA/ePLA-LW/ePLA-GF, a Grid filling pattern, and 80% filling density. The Taguchi prediction method is utilized to determine an optimal combination of processing parameters for achieving optimal flexural performances, and predicted outcomes are validated through related experiments. The experimental values of strength and modulus are 43.91 MPa and 1.23 GPa, respectively, both very close to the predicted values of 46.87 MPa and 1.2 GPa for strength and modulus. The Taguchi experiments indicate that the material sequence is the most crucial factor influencing the flexural strength of the composite panels. The experiment result demonstrates that the flexural strength and modulus of the first material sequence are 67.72 MPa and 1.53 GPa, while the flexural strength and modulus of the third material sequence are reduced to 27.09 MPa and 0.72 GPa, respectively, only 42% and 47% of the first material sequence. The above findings provide an important reference for improving the performance of multi-material 3D-printed products.

Optimization of duplex heat treatment settings with the MOORA-PCA technique to customize the mechanical and wear response of Ti6Al4V alloy

The impact of duplex heat treatment settings was examined to optimize the mechanical and wear responses of Ti6Al4V alloys. A multi-objective optimization using ratio analysis (MOORA) coupled with PCA was used to optimize the different outcomes. Heat treatment aims to reduce the wear rate, frictional coefficient, and improve tensile strength and microhardness. Variables like solution treatment temperature, duration, aging temperature, and time are considered for optimization. The optimization results reveal that the optimal circumstances are A3B1C2D3. The appropriate process parameter combination was finally evaluated by a confirmation test, which shows that the MOORA-PCA technique worked well together to find the best heat treatment settings. All of the heat-treated samples demonstrated better mechanical and wear characteristics. However, compared to other heat-treated samples, samples that underwent heat treatment in optimal settings have superior features. Compared to the heat-treated samples at trial 9 and the optimum conditions, the samples at trial 8 settings showed 5.4% and 12.3% lower microhardness, respectively. The alloy subjected to optimal conditions for heat treatment has a tensile strength of 1154.87 MPa, which is 12.95% and 6.09% greater than the specimen heat-treated at trials 8 and 9, respectively. Compared to the as-received alloy, the sample that underwent heat treatment at optimal settings exhibited a wear rate of approximately 40% lower than the as-received alloy, 3% lower than the heat treatment settings used in trial 8, and nearly 26% lower than the heat treatment settings used in trial 9. The bi-model microstructure developed under optimal circumstances is correlated with increased mechanical and wear properties.

Analysis and Optimization of Process Parameters of Powder Mixed Near Dry- EDM by using Grey Relational Optimization

The hybrid Electric Discharge Machine (PM-EDM) is the latest advancement in the removal of hard materials from workpieces while enhancing their performance and characteristics. This research project is focused on optimizing key process parameters, including material removal rate, tool wear, residual stresses, and surface finish. The approach utilized in this study involves employing the standard deviation-based objective weighting method in conjunction with GRA (Grey Relational Analysis) optimization to improve the hybrid PM-EDM process when applied to EN-31 material. Experimental runs were conducted to assess the impact of various input factors such as pulse on time, duty cycle, discharge current, and concentration of metallic powder. The GRA (Grey Relational Analysis)-based Taguchi method was employed for this purpose, and the experimental design followed an L-27 orthogonal array within the framework, facilitated by the use of minitab-20 software. A total of 27 experiments were conducted, encompassing diverse combinations of process parameters. Subsequently, an ANOVA (Analysis of Variance) was performed to scrutinize the impact of various inputs of discharge current, pulse on time, duty cycle, and powder concentration on MRR (Material Removal), TWR (Tool Wear), Ra (surface finish), and Rs (Residual stress).