Optimal Setting Of Process Parameters Research Articles

Optimization of Correlated and Conflicting Responses of ECM Process Using Flower Pollination Algorithm

The electrochemical machining (ECM) process has been investigated in this article to achieve the desired process performances by optimizing the machining parameters using the flower pollination algorithm (FPA). Two major process performances namely: material removal rate (MRR) and surface roughness (Ra), which are correlated and conflicting in nature, are optimized with respect to the key process parameters. The regression equations developed by using experimental data are used as objective functions in the flower pollination algorithm. Objectives are set to find the optimal set of process parameters to fulfil a single objective as well as multiple objectives. The performance of the algorithm is checked in terms of accuracy, convergence speed, number of optimized populations, and computational time. The mean values of functional evaluations for MRR and Ra obtained are close to their respective optimal results, which ensures the quality of the convergence. It is further seen that FPA can predict the true overall parametric trends as it does not require keeping any parameter as constant during the analysis.

Modeling and Response Optimization of Traveling Wire Electro-Chemical Spark Machining of Borosilicate Glass Using Hybrid Approach

Traveling Wire Electro-Chemical Spark Machining (TW-ECSM) process is a new innovative thermal erosion-based machining process suitable for cutting electrically nonconductive materials using tool electrode in the form of wire. This article attempts experimental modeling of TW-ECSM process using a hybrid methodology comprising Taguchi methodology (TM) and response surface methodology (RSM). The experiments were carried out on borosilicate glass using L[Formula: see text] orthogonal array (OA) considering the input parameters like applied voltage, pulse on-time, pulse off-time, electrolyte concentration and wire feed velocity along with process performances such as material removal rate (MRR), surface roughness (R[Formula: see text] and kerf width (K[Formula: see text]. The interaction influence of input parameters on process performances was also discussed. Further, multi-objective optimization (MOO) of response performances of TW-ECSM process is executed using a coupled approach of grey relational analysis (GRA) and principal component analysis (PCA). The optimal process parameter setting illustrates the improvement of MRR by 171%, diminution of Ra and K[Formula: see text] by 27% and 8% against the initial parameter settings. Moreover, irregular cutting of kerf width and surface characteristics were also scrutinized using scanning electron microscope (SEM).

Multi-objective optimization of drilling EDM process parameters of LM13 Al alloy–10ZrB2–5TiC hybrid composite using RSM

In this study, a newly engineered stir-cast LM13 aluminium alloy-based hybrid composite (5 wt% of TiC and 10 wt% of ZrB2) is used as workpiece and its machinability investigation is carried out using EDM (electric discharge machining) drilling route with copper electrode. During experimentation, the process variables, namely current (I), pulse on-time (Ton) and pulse off-time (Toff), are varied and the output performance characteristics, viz. material removal rate (MRR), electrode wear rate (EWR) and dimensional accuracy, i.e. overcut (OC), are measured. The series of experiments (20 runs) are formulated through the adoption of response surface methodology (RSM)-based full-factorial central composite design. To recognize the correlation between the process variables and performance characteristics, a mathematical model is developed for each response. Subsequently, adequacy of the proposed models is verified using analysis of variance (ANOVA). Moreover, ANOVA is employed to identify the significantly influencing process parameters over the performance characteristics. From the analysis of results, it is inferred that the current has the most dominant effect on MRR and EWR, whereas OC is highly influenced by Ton. Composite desirability function approach of RSM is utilized to find the optimum set of process parameters in order to satisfy the desired multi-performance characteristics (such as maximizing MRR, minimizing EWR and OC) for obtaining effective holes on hybrid LM13–10ZrB2–5TiC composite. Finally, optimized results are validated with the experimental results to determine the competence of adopted desirability approach.

Optimization and physical performance evaluation of electrospun nanofibrous mats of PLA, PCL and their blends

The optimization of process parameters such as applied voltage, orifice diameter, solvent system, and solvent ratio for electrospinning of neat polymers, polylactic acid (PLA) and poly (є-caprolactone) (PCL), to obtain uniform, randomly oriented nanofibers with minimum diameter variation and beaded structures has been critically discussed. The paper focuses on establishing a sequential optimization technique for arriving at a common set of electrospinning process parameters for individual polymers, such as, applied voltages, orifice diameters, solvent mixtures, solvent ratios, to be used in the fabrication of electrospun nanofibrous mats (ENMs) of blended polymers. In this study, the effect of variation of applied voltages, orifice diameters, solvent mixtures, solvent ratios, PLA/PCL blending ratios, solution concentration of blends and flow rate were reported via morphological analysis of electrospun nanofibers. The set of optimal process parameters obtained for both PLA and PCL were adopted for the fabrication of ENMs based on the PLA/PCL blends. The paper further deliberates on the physical performance of PLA/PCL based ENMs in acidic, basic and neutral release media. Thus, the study establishes a hierarchical processing optimization route for designing blended ENMs by following a set of variable electrospinning process parameters.

Modelling and optimization of mean thickness of backward flow formed tubes using regression analysis, particle swarm optimization and neural network

In this study, the relationship between the input process parameters and the output parameters of the backward metal flow forming process is established using a full factorial design of experiments. Both the linear and interaction terms of the process variables have been analyzed using statistical methods. Composite desirability and particle swarm optimization (PSO) is used to find an optimal set of process parameters for the desired responses. The composite desirability approach was found to provide more accurate results compared to the PSO. Neural network (NN) was used for modelling the flow forming process for the efficient prediction of output parameters. The performance of the NN-based predictive tool is better compared to regression analysis.

Identification of Optimal Process Parameter Settings Based on Manufacturing Performance for Fused Filament Fabrication of CFR-PEEK

Fused filament fabrication (FFF) has been proven to be an effective additive manufacturing technique for carbon fiber reinforced polyether–ether–ketone (CFR-PEEK) due to its practicality in use. However, the relationships between the process parameters and their trade-offs in manufacturing performance have not been extensively studied for CFR-PEEK although they are essential to identify the optimal parameter settings. This study therefore investigates the impact of critical FFF parameters (i.e., layer thickness, build orientation, and printing speed) on the manufacturing performance (i.e., printing time, dimensional accuracy, and material cost) of CFR-PEEK outputs. A full factorial design of the experiments is performed for each of the three sample designs to identify the optimal parameter combinations for each performance measure. In addition, multiple response optimization was used to derive optimal parameter settings for the overall performance. The results show that the optimal parameter settings depend on the performance measures regardless of the designs, and that the layer thickness plays a critical role in the performance trade-offs. In addition, lower layer thickness, horizontal orientation, and higher speed form the optimal settings to maximize the overall performance. The findings from this study indicate that FFF parameter settings for CFR-PEEK should be identified through multi-objective decision making that involves conflicts between the operational objectives for the parameter settings.

Taguchi Grey Relational Approach for Optimizing Process Parameters of Laser Peening on Titanium Alloy to Induce Enhanced Compressive Stress based on Finite Element Simulation

Laser Shock Peening (LSP) turned out to be the most efficient surface engineering process for advanced materials to induce beneficial deep compressive residual stress which helps in improving mechanical, fatigue properties and surface damage resistance. But, analyzing the nonuniform distribution of residual stresses in the treated sample with X-ray diffraction (XRD) is much time taking and a costly process. This problem can be resolved with LSP finite element numerical simulation model which is feasible with the realistic experimental process. The FE model allows the user to control the laser parameters in order to achieve the optimal level of all controllable parameters. This study is intended to analyze and optimize the influence of laser processing parameters that assists in inducing the residual compressive stress with minimal surface deformation. A Ti6Al4V material model with Johnson–Cook’s visco-elastic–plastic material behavior law is prepared for LSP simulation. Gaussian pressure profile is utilized for uniform loading of the targeted zone for the proposed model. Taguchi Grey Relational Analysis (TGRA) with L27 orthogonal array is applied to LSP simulation, and the results were analyzed with consideration of multiple response measures. It is noted that surface deformation is increased with the rise in a number of laser shots and pressure pulse duration. Maximum compressive residual stresses are falling for higher levels of laser spot diameter, laser spot overlap and laser power density. The correlation is observed between the FE simulation and the published results. The optimal set of process parameters are obtained for improving the LSP on Ti alloys.

Simulation Based Optimization of Thin Wall Injection Molding Parameter Using Response Surface Methodology

.Improper mold design or processing parameter setting could make a bad impact on the appearance of molded part. It becomes more challenging if the ratio of the part size compared with its wall thickness is greater. Process parameters such as mold and melt temperature, injection temperature including the cavity layout directly affect product quality and cost. It is a complex and difficult task to improve these multi parameters. The objective of this research is to determine optimum settings for processing parameters for a thin walled product by using Response Surface Methodology. Melting temperature, mold temperature, injection pressure and cavity layout are selected as processing parameters and Polyurethane material is selected for this research. Volumetric shrinkage and warpage are selected as the main quality criteria to be controlled respected with the product design specifications. As the result, optimum process parameter settings were mold temperature of 29.89°C, melting temperature of 220°C, and 181.30MPa injection pressure with ‘H’ branching cavity layout. With small differences error value between solution and simulation, 0.31% for volumetric shrinkage and 0.126% for warpage, the result was acceptable.

Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling

Metal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers towards the fabrication of complex objects through localized melting and resolidification using high-power energy sources. Powder bed fusion and directed energy deposition are two widespread metal AM processes that are currently in use. During layer-by-layer fabrication, as the components continue to gain thermal energy, the melt pool geometry undergoes substantial changes if the process parameters are not appropriately adjusted on-the-fly. Although control of melt pool geometry via feedback or feedforward methods is a possibility, the time needed for changes in process parameters to translate into adjustments in melt pool geometry is of critical concern. A second option is to implement multi-physics simulation models that can provide estimates of temporal process parameter evolution. However, such models are computationally near intractable when they are coupled with an optimization framework for finding process parameters that can retain the desired melt pool geometry as a function of time. To address these challenges, a hybrid framework involving machine learning-assisted process modeling and optimization for controlling the melt pool geometry during the build process is developed and validated using experimental observations. A widely used 3D analytical model capable of predicting the thermal distribution in a moving melt pool is implemented and, thereafter, a nonparametric Bayesian, namely, Gaussian Process (GP), model is used for the prediction of time-dependent melt pool geometry (e.g., dimensions) at different values of the process parameters with excellent accuracy along with uncertainty quantification at the prediction points. Finally, a surrogate-assisted statistical learning and optimization architecture involving GP-based modeling and Bayesian Optimization (BO) is employed for predicting the optimal set of process parameters as the scan progresses to keep the melt pool dimensions at desired values. The results demonstrate that a model-based optimization can be significantly accelerated using tools of machine learning in a data-driven setting and reliable a priori estimates of process parameter evolution can be generated to obtain desired melt pool dimensions for the entire build process.

Synthesis of Stir-Cast AA7050/5%B<sub>4</sub>C Composite and Optimization of EDM Response Parameters Using Taguchi Analysis

Machining of Automobile and aerospace components are the prime concern for close tolerance and eastern mechanical design. AA7050 is the established aluminium alloy for automobile and aerospace components. AA7050/B4C composite enhances specific strength and wear property of the alloy. The present paper aims to develop AA7050/5%B4C Composite through flux assisted Stir casting method. Characterized its microstructure for proper distribution and incorporation of the particles. Furthermore, non-conventional machining done on the composite through CNC die-sinking EDM with process parameters Peak current (Ip), Pulse on time (Ton), and Pulse off time (Toff). The experiments are designed by Taguchi L9 orthogonal array to obtain the machining responses like Material removal rate (MRR), Surface roughness, (SR) and cutting speed (Cs). Further the responses are optimised using Taguchi analysis considering higher the better criterion for MRR and Cs and lower the better for SR. The optimal set of process parameters obtained for MRR is X3Y3Z2, for SR X3Y2Z3 and Cs X3Y3Z2. The confirmation tests conducted with optimal set of process parameters for each response. The error estimated between predicted and experimental at optimal sets is within the acceptable threshold.

Multi-response optimization of magnetic field assisted EDM through desirability function using response surface methodology

Abstract Magnetic field assisted electrical discharge machining (MFAEDM) is the modification of in conventional EDM process by use of magnetic field on EN-31. This article explain the application of response surface methodology to analyzes the effect of various process parameters such as Ton, Toff and Ip on performance measures such as material removal rate (MRR), electrode wear rate (EWR) and overcut (OC). Analysis of variance was used to check the adequacy of response surface model and significance of process parameters on performance measures. Multi-objective desirability function has been applied to obtain the optimal process parameter settings. Thereafter, machined surface of EN-31 characterized through SEM and EDX. The novelty of this paper is to improve the strategies for flushing the debris which remain clogged in standard EDM in-between machining gap that will interrupts the regular discharge conditions and reduces cutting rate as well as deteriorate the surface characteristics.

Grey-Taguchi and TOPSIS-Taguchi-Based Optimisation of Performance Parameters of Spark EDM on Heat-Treated AA7050/5 B4C Composite

EDM is a non-contact type machining process where subsurface damage significantly minimised. The present paper investigates the electro-discharge machinability of AA7050/5 B4C composite in terms of material removal rate, white layer thickness and surface roughness with the concurrent effect of process parameters such as peak current (Ip), pulse on time (Ton) and pulse off time (Toff). The experiments were designed as per Taguchi L9 orthogonal array. The responses parameters are optimised using Grey-Taguchi method and TOPSIS-Taguchi method. The optimum configurations of process parameters are evaluated by both the ways. For the confirmations, new experiments conducted with optimum settings of parameters. The experimental outputs compared with the predicted output. As per Grey-Taguchi, the optimal settings of process parameters are obtained as Ip3Ton3Toff1, where the error estimated between experimental and predicted GRG is 3.4%, whereas the optimal configuration of process parameters obtained from TOPSIS-Taguchi method is Ip1Ton1Toff2, and the error anticipated between experimental and predicted RC is 4.45%. From both optimisation methods, Grey-Taguchi method gives better results than TOPSIS-Taguchi Method.

Experimental Investigation of the Micro-hardness of EN-31 Die Steel in a Powder-Mixed Near-Dry Electric Discharge Machining Method

The Powder-Mixed Near-Dry Electric Discharge Machining (PMND-EDM) methodology has proven to be efficient in terms of machining rate, surface morphology, and environmental friendliness, unlike traditional EDM. In this study, the presence of a conductive metallic powder (zinc) in the dielectric medium was responsible for changing the topography of the workpiece (EN-31) and resulted in a higher micro-hardness value of the machined component. In this research, an approach has been made to optimize the significant process parameters by using a Taguchi L9 orthogonal array (OA) to obtain machined components with higher values of micro-hardness, which was measured in terms of Vickers hardness HV. The selected process parameters were tool diameter, mist flow rate, metallic powder concentration, and dielectric mist pressure. By introducing foreign particles (metallic powder), the topography of the machined products has been improved, and the micro-hardness value was found to be enhanced. The confirmation experiment was performed for optimal process parameter settings, and the enhanced microhardness value was found to be 506.63 HV in the machined EN-31 die steel.

Optimization of injection-molding processing conditions for plastic double-convex Fresnel lens using grey-based Taguchi method

Injection molding is an ideal technique for the low-cost mass production of plastic optical lenses. However, the optical quality of the lens is significantly dependent on the processing parameters employed in the molding process. In particular, the parameters should be set in such a way as to minimize both the shrinkage displacement-induced warpage (in order to reduce optical aberrations) and the residual stress (in order to reduce the birefringence). However, in practice, the two performance targets are mutually exclusive. That is, a smaller warpage results in a greater residual stress, and vice versa. Thus, in the present study, Taguchi experiments are first performed to determine the processing parameters which individually minimize the warpage and retardation of a symmetric plastic double-convex Fresnel lens, respectively. A grey relational analysis technique is then applied to the Taguchi results to establish the processing parameters which achieve the optimal tradeoff between the two performance objectives. The validity of the proposed method is demonstrated by means of mold-flow analysis simulations. The results show that in simultaneously optimizing both the warpage and the retardation, the injection molding control factors are ranked in order of decreasing influence as follows: packing pressure, packing time, melt temperature, filling time, cooling time and mold temperature. Given the optimal process parameter settings, the warpage and retardation are reduced by 16.42% and 74.74%, respectively, compared to the original design. In other words, the proposed grey-based Taguchi method provides a viable technique for optimizing both performance targets for the considered double-convex Fresnel lens.

Optimization of vacuum counter-pressure casting process for an aluminum alloy casing using numerical simulation and defect recognition techniques

Vacuum counter-pressure casting (VCPC) process is an effective forming process of producing components with complex thin-walled characteristics. In this study, a surrogate-based optimization method with five process parameters is used to reduce the shrinkage porosity defect during the VCPC process of a complicated thin-walled aluminum alloy casing. The prediction of the shrinkage porosity defect is implemented by using the numerical simulation of the VCPC process. In general, performing the optimization directly based on the casting simulation may not be practical due to the computational and time constraints. To mitigate this problem, the response surface model (RSM) constructed via computer experiments is employed to accurately approximate the functional relationship between the process parameters and the response variable which is defined as the area of defects in the defect image generated from the casting simulation. To measure the area of defects in the image, a histogram-based threshold segmentation approach is proposed to recognize and quantify the defects from the image. The optimization problem is subsequently formulated using a desirability function-based optimization method, and the objective function is estimated using the built RSMs. The final results indicate that a set of optimal process parameters with minimum defects can be obtained.

Optimization design of spinning process of titanium alloy cylinder based on numerical simulation technology

Power spinning is a very complicated plastic forming process, and its processing quality involves many and complicated process parameters. The simufact.forming software is used to simulate the power spinning of the titanium alloy cylindrical parts. The effects of technological parameters such as attacking angle, roller nose radius, feed rate, spinning temperature, etc. on cylindrical parts accuracy, spinning force, and stress-strain distribution are systematically studied. The causes of spinning process defects are analyzed, and the sensitivity of process parameters that affected spinning quality is ranked. Through orthogonal test analysis, it is determined that a set of optimal process parameters for the titanium alloy cylindrical part spinning process: attacking angle is attacking angle 20°, the roller nose radius is 10mm, feed rate is 1mm/r, spinning temperature is 850°C. The spinning process of titanium alloy cylindrical parts is initially formed, which lays the foundation for the subsequent spinning process tests of titanium alloy cylindrical parts.

Study on microstructure and properties of 304/316L coating prepared by ultra-high frequency induction cladding with wire feeding

To improve the surface properties of blade tip made of 304 stainless steel, 316L stainless steel wire was coated on the substrate by ultra-high frequency (UHF) induction heating technology. Optimized process conditions were determined by analyzing the results of orthogonal experiments. The feasibility of this method was confirmed by studying the element distribution of coating. Distributions of temperature field and residual stress were simulated and analyzed by finite element method. The microstructure, tribological properties and corrosion behaviors of the coating were investigated. Surface morphology after experiment was measured. Parameters of the maximum dilution ratio in a certain extent were the optimal process parameter set, which was heating power ratio of 93%, wire feeding rate of 2.7 mm/s, moving speed of 2.8 mm/s, distance between substrate and induction coil of 1.0 mm, preheating temperature of 823.15 K. Friction coefficient of as-prepared coating was about 0.5, which is nearly 30 % lower than that of the substrate. Weight loss of substrate is 4 times that of the cladding layer of 0.1 mg. Mainly wear mechanism transformed from adhesive to abrasive. Capacitive arc of the coating in the low-frequency region was increased so that the surface is more capable to form a passive film. 304/316L coatings improve wear resistance and anti-corrosion. And this technology can be widely used for thermal repair of the blade tips.

Minimising Defect Formation in Sand Casting of Sheet Lead: A DoE Approach

Sand casting of lead sheet is a traditional manufacturing process used up to the present due to the special features of sand cast sheet such as their attractive sheen. Similarly to any casting process, sand casting of lead sheet suffers from the presence of surface defects. In this study, a surface defect type, hereby referred to as ‘grooves’, has been investigated. The focus has been laid on the identification of the main factors affecting defect formation in this process. Based on a set of screening experiments performed using Scanning Electron Microscopy (SEM) as well as the existing literature, a number of factors affecting the formation of such defects was identified and their corresponding significance was estimated using the Analysis of Variance (ANOVA) technique. The obtained results suggest that the most significant factor affecting defect formation in sand casting of lead sheet is the composition of the moulding mixture. Defect formation was also proven to be dependent on the sand grain fineness, the quality of the melt and some of the interactions between the aforementioned process parameters. Finally, an optimal set of process parameters leading to the minimisation of surface defects was identified.

Experimental investigation into the fabrication of green body developed by micro‐extrusion‐based 3D printing process

AbstractIn the present work, a micro‐extrusion‐based three‐dimensional (3D) printing process has been used to fabricate a metal‐polymer‐based green body. The fabricated parts consisted of carbonyl iron particles with a binder (polylactic acid) and a solvent mixture. From the pilot experiments, it was found that the process parameters, namely, Fe loading, layer thickness,and infill density, affect the green density, shrinkage, and surface roughness of the fabricated part. Moreover, to develop a statistical model with significant factors, experiments were performed based on the design of the experiment using a central composite design method. The experimental results revealed that green density and surface roughness of 3D printed parts increased with the increase in Fe loading and infill density. On the contrary, the shrinkage in the fabricated part decreased with an increase in Fe loading and increased with the rise in infill density. Further, with an increase in layer thickness, the green density decreased while shrinkage and roughness were observed to increase. To verify the accuracy of the developed model, confirmation experiments were also performed at the optimum set of process parameters obtained by the genetic algorithm optimization technique.

Drag reduction studies in water using polymers and their combinations

Enhancing the flow rate by reducing the drag in turbulent flows of fluids is a highly significant phenomenon with reference to many industries viz., oil, marine, irrigation, biomedical etc to reduce power consumption. This drag reduction could be mostly achieved by adding polymeric additives, also referred to as drag reducing agents (DRAs) to the fluid in trace amounts. In this study, pressure driven set-up with pipes of different diameters and roughness levels has been employed to study drag reduction by polymers like PEO, PAM, PAA, HPMC in water systems. The objective of the work was to identify the optimum set of process parameters in terms of polymer nature and its concentration, pipe diameter and its roughness, methodology adopted to maximize maximum drag reduction. The results achieved were then validated with well proven concepts and reported studies.