Optimal Combination Of Process Parameters Research Articles

Multi-objective optimization in EDM of functionally graded Fe-Al using grey relational analysis

Abstract Functionally Graded Materials (FGMs) are multipurpose materials with a specific goal of managing changes in structural, thermal, or functional qualities. They feature a spatial variation in microstructure and/or composition. The current work prepared three layers of sample with different chemical compositions of Fe-Al FGM (50 Al-50 Fe, 45 Al-55 Fe, and 40 Al-60 Fe) at. % produced by powder metallurgy, then studied the machining behavior of these samples. The literature on the machining behavior of FGM was reviewed, and the influence of electrical discharge machine (EDM) process parameters such as voltage (V), current (I), pulse-on time, and pulse-off time on performance characteristics (material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra)) was investigated. The optimal machining settings for Fe-Al FGM samples will be ascertained by use of Grey Relational Analysis (GRA), which is based on the Taguchi approach, after an experimental investigation of the L18 orthogonal array design of trials. The GRA findings verify that V 140 volts, Ip10 A, Ton 100 μs, and Toff 75 μs is the optimal combination of process parameters. It has been found that the voltage is more significantly affected than the rest of the input parameters to obtain greater material removal rate (MRR) and lower tool wear rate (EWR) and surface roughness (Ra) through the response table. According to the study, choosing the right process parameters can improve the multi-performance feature.

Experimental investigation on in-situ laser-assisted mechanical ruling of single crystal silicon

Abstract In this paper, response surface methodology (RSM) was employed as a robust and convenient predictive tool to establish the correlation between process parameters of in situ laser-assisted mechanical ruling and the ductile-to-brittle transition of single-crystal silicon. The interaction effects among three factors laser power, ruling speed, and negative rake angle on the ductile-to-brittle transition of single-crystal silicon were investigated. The optimal combination of process parameters was determined to be a laser power of 30W, a ruling speed of 0.25 mm s−1, and a negative rake angle of 58°. Utilizing a self-assembled setup and the optimal process parameters, multiple processing experiments were conducted. The average error between the experimental and predicted values was found to be 2.8%.

Experimental Study on the Pelleting and Coating Performance of Red Clover Seeds

This study aimed to optimize the pelleting and coating process for red clover seeds, addressing the issue of low pelleting success rates. Through theoretical analysis and experimental research, coating pan fill rate, powder supply quantity, and pelleting time were identified as key factors influencing the pelleting success rate. Single-factor experiments were conducted to investigate the effects of these parameters on the quality of red clover seed pelleting and coating. Based on these results, orthogonal trials were carried out, and response surface analysis was employed to reveal the influence patterns and interactions of each factor. The research results indicate that the factors affecting the pelleting success rate, ranked in order of importance, are coating pan fill rate, pelleting time, and powder supply quantity. Through mathematical model optimization, the optimal combination of process parameters was determined to be coating pan fill rate of 35.9%, powder supply quantity of 160.2 g, and pelleting time of 6.9 s. Under these conditions, a pelleting success rate of 94.3% was achieved in validation experiments. This study provides a theoretical foundation and practical guidance for optimizing the pelleting and coating process of red clover seeds, which is significant for improving seed coating quality and promoting red clover cultivation.

Parameter Optimization Method for Centrifugal Feed Disc Discharging Based on Numerical Simulation and Response Surface

In this study, a centrifugal feeding disc device is proposed. To investigate the influence of the process parameters on the discharging efficiency and the lifting of the discharging efficiency, the centrifugal feeding disc device was dynamically simulated based on the discrete element method (DEM), and the simulation results were experimentally verified. Based on the quadratic regression orthogonal test method, a significant lossless regression model of process parameters and discharging efficiency was established, and the response surface of the interaction of process parameters was obtained. The results indicated that the order of influence of the process parameters on the discharging speed of the centrifugal feeding disc was as follows: outer turntable speed > inner turntable speed > inner turntable tilt angle > conical turntable angle. The interaction of the conical turntable angle and the inner turntable tilt angle had the greatest influence on the centrifugal feed disc discharge efficiency. The response surface method (RSM) was used to optimize the process parameters, and the optimal combination of process parameters included an outer turntable speed of 135 r/min, an inner turntable speed of 64 r/min, an inner turntable tilt angle of 7°, and a conical angle of 15°. The discharged efficiency of the optimized centrifugal feeding disc device was increased by 31.9%.

Optimization of hydrogel extrusion printing process parameters based on numerical simulation

The printing quality of biological scaffold is not only affected by the fluidity of bio-ink but also by the printing process parameters, such as the size of the needle, printing height, extrusion speed, and printing speed. Therefore, optimizing the printing process parameters can further improve the molding quality of the biological scaffold. In this study, the printing and deposition process of sodium alginate hydrogel was modeled and analyzed based on the Herschel–Bulkley model by the finite element simulation method. The orthogonal experiment method, control variable method, and response surface method were used to design experiments, and the influences of different printing process parameters on the hydrogel deposition process were investigated. Finally, the optimal combination of printing process parameters was obtained by taking the molding degree and offset of the hydrogel line as optimization objectives. The results show that the strength relationship of the factors affecting the molding degree of the hydrogel line is as follows: printing height > needle diameter > printing speed > extrusion speed, and the strength relationship of the factors affecting the printing offset is as follows: printing height > needle diameter > extrusion speed > printing speed. The optimal combination of printing process parameters is d = 0.34 mm, H = 0.51 mm, v1 = 10 mm/s, and v2 = 7.91 mm/s. Compared with the printing experiment results of the hydrogel line molding degree under the optimal process parameters, the error range is within −11.55%–1.27%, which further demonstrates the reliability of the optimization method of hydrogel extrusion printing process parameters based on numerical simulation and response surface method.

Optimization of Magnetic Field-Assisted Laser Cladding Based on Hierarchical Analysis and Gray Correlation Method

Process parameters directly affect the quality of laser cladding. In this study, magnetic field-assisted laser cladding experiments were carried out on the surface of 300 M ultra-high-strength steel by setting laser energy density, magnetic field strength, and frequency as processing parameters. The optimization of laser cladding process parameters was investigated based on evaluating the quality of the laser cladding layer through hierarchical analysis and gray correlation analysis. Based on orthogonal test data, the correlation coefficients of the process parameters with the single objective function and the correlation degree of the multi-objective function were calculated by using the gray theory. Then the comprehensive objective optimization was carried out according to the gray correlation degree. The optimization problem with multiple process objectives was transformed into a single gray correlation degree optimization method to realize the optimization of process objectives and obtain the optimal combination of process parameters. The validation experiments indicate that the quality of the laser cladding layer can be greatly improved by employing optimal process parameters. The optimized laser cladding layer shows a reduced microstructure size and enhanced wear resistance, indicating the effectiveness of the optimization approach.

Optimization Study on Hardness of Cold Rolled Strip Steel Based on a Data-Driven Approach

As a key index to measure product quality and processability, the hardness of cold-rolled strip steel has an important influence on the production efficiency of iron and steel enterprises. However, due to the coupling between the process parameters at each stage of the continuous annealing process, and the setting of traditional process parameters depending on the experience of the operator, the lack of scientific and systematic optimization methods, which easily leads to the problem of high hardness fluctuation and low resource utilization. In this study, the data-driven method is used to conduct a preliminary correlation analysis to further clarify the optimal combination of process parameters for achieving the target hardness. The Pearson correlation coefficient is used to determine that carbon content, quenching furnace temperature, and strip speed significantly affect strip hardness. To further clarify the optimal combination of process parameters to achieve the target hardness, a Genetic Algorithm (GA) was tried in this study. The results show that the optimized process parameter combination can effectively improve the stability and consistency of strip hardness, and provide a scientific basis for the process parameter setting in the cold rolled strip production process, which is helpful for enterprises to improve product quality, reduce cost, and enhance market competitiveness.

Multi-Objective Optimization of the Forming Process Parameters of Disc Forgings Based on Grey Correlation Analysis and the Response Surface Method

This paper introduces a multi-objective optimization problem (MPO) for the forming process parameters of disc forgings using grey relational analysis (GRA) and the response surface methodology (RSM). Firstly, an experimental design based on the Box–Behnken design (BBD) principle was established, and simulations were performed in Deform to obtain response data. Secondly, GRA was used to transform the MPO into a grey relational degree (GRD) problem, and the entropic weight method was integrated to ascertain the influence weights of each variable on GRD. Then, a quadratic polynomial prediction model based on the RSM was constructed, and its accuracy was ensured through model validation. Finally, the optimal process parameter combination was determined through the particle swarm optimization algorithm, which included a friction coefficient of 0.3, an initial temperature of 1250 °C, and a downward pressing speed of 7.5 mm/s. The results of the experimental investigation indicate that optimized process parameters significantly reduce the forming load, equivalent stress, and damage value, effectively enhancing the overall quality of forged parts.

Experimental investigation on in-situ laser-assisted machining of single crystal silicon based on response surface methodology

Abstract In this paper, Response Surface Methodology (RSM) was utilized as a robust and convenient predictive tool to establish the correlation between process parameters in in situ laser-assisted machining and the surface roughness of single-crystal silicon. An optimized design of the diamond tool, a novel temperature field analysis method, and Response Surface Methodology (RSM) were utilized. The contribution rate of each process parameter on surface roughness was laser power > rotation speed > cutting depth > feed rate. The optimal process parameter combination is: rotation speed as 1001 r min−1, feed rate as 4.9 μm/r, cutting depth as 7.55 μm, and laser power as 28.81 W. Experimental validation of these optimal parameters compared surface roughness values obtained experimentally with those predicted. The surface roughness model showed a maximum relative error of 5.2%, with an average error of 4.8% across three experimental sets. These errors are within acceptable limits, indicating an alignment between predicted and experimental results.

Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing

AbstractThe process parameters used for building a part utilizing the powder-bed fusion (PBF) additive manufacturing (AM) system have a direct influence on the quality—and therefore performance—of the final object. These parameters are commonly chosen based on experience or, in many cases, iteratively through experimentation. Discovering the optimal set of parameters via trial and error can be time-consuming and costly, as it often requires examining numerous permutations and combinations of parameters which commonly have complex interactions. However, machine learning (ML) methods can recommend suitable processing windows using models trained on data. They achieve this by efficiently identifying the optimal parameters through analyzing and recognizing patterns in data described by a multi-dimensional parameter space. We reviewed ML-based forward and inverse models that have been proposed to unlock the process–structure–property–performance relationships in both directions and assessed them in relation to data (quality, quantity, and diversity), ML method (mismatches and neglect of history), and model evaluation. To address the common shortcomings inherent in the published works, we propose strategies that embrace best practices. We point out the need for consistency in the reporting of details relevant to ML models and advocate for the development of relevant international standards. Significantly, our recommendations can be adopted for ML applications outside of AM where an optimum combination of process parameters (or other inputs) must be found with only a limited amount of training data.

Microstructure phase analysis and process optimization of ZL205A metal mold casting

In this paper, we take an elastomeric transmission structure part made of ZL205A alloy as the research object, and use the metal type gravity casting technology for casting trial production, analyze the quality and mechanical properties of the molded parts of ZL205A alloy parts under different process parameters, optimize the casting process parameters, get the optimal combination of process parameters, and obtain large ZL205A castings with less internal quality defects and high mechanical properties.

Simulation analysis and test of the spinning process of 304 stainless steel cylindrical parts

Abstract Taking the difference in wall thickness as the evaluation index, the simulation analysis of the spinning process of 304 stainless steel cylindrical parts is carried out. The influence of the roller nose radius, feed speed, and percent thickness reduction on the stress and strain of the roller is studied, and the optimum combination of process parameters is obtained. Through the spinning test, the stainless steel cylindrical parts with an outside diameter of 300.8 mm are successfully spun. After spinning forming and solution treatment, the tensile strength of stainless steel reaches 557~566 MPa, the yield strength reaches 220~232 MPa, and the elongation reaches 41.3%~42.5%.

Optimization Analysis of Response Surface for Ultrasonic Welding of Battery Tabs

Abstract The response surface methodology was used to optimize the ultrasonic welding process parameters of battery tabs. The three process parameters of welding pressure, welding energy and welding amplitude were selected as the influencing factors, the peel strength and welding time were selected as the response variables, a three-factor and three-level experimental matrix was designed. The experimental results were analyzed by multiple regression fitting and the regression model was established. The optimal parameters derived from the model were verified. The optimum combination of process parameters is pressure (0.25Mpa), energy (649J) and amplitude (75%) with the goal of maximum peel strength and minimum welding time. The test results show that the relative error is within a reasonable range, and the established response surface model has a higher precision, which provides a certain reference for reasonable selection of ultrasonic welding process parameters for battery tabs.

Research on laser polishing process of zirconia ceramic surface

Abstract In order to investigate the polishing results of carbon dioxide pulsed laser on the surface of zirconia ceramics, this paper analyzes the significance of the influence of each process parameter on the surface roughness in the laser polishing process based on the experimental design of the response surface method and taking the surface roughness as the optimization objective. And the best combination of process parameters for the laser polishing process is predicted by establishing a surface roughness prediction model. The results of the experiments show that among the laser polishing process parameters, the scanning speed and laser power have an extremely significant effect on surface roughness, while the pulse frequency has a more significant effect on surface roughness. The model predicts the optimum combination of working parameters as: laser power P = 75W, pulse frequency f = 4 kHz, scanning speed v = 380 mm/s. Under the optimum combination of process parameters, surface roughness Ra = 0.595μm of the material, which is 76% lower than that of the original surface roughness of 2.479μm after laser polishing.

Optimization of process parameters for Trifolium pratense L. seed granulation coating using GA-BP neural network

Regarding the issue of low granulation qualification rates during the granulation coating of red clover seeds, this study theoretically analyzed the force conditions of seeds and powder particles under the action of liquid to obtain the main factors affecting seed coating quality. During the seed granulation coating process, an intermittent powder supply method combined with continuous liquid supply was utilized to control the ratio of powder to liquid. Using the granulation qualification rate as the evaluation index, single-factor experiments were conducted to investigate the effects of coating pan fill ratio, single powder supply amount, powder supply interval, and liquid supply amount on the quality of red clover seed granulation coating. Based on the results of the single-factor experiments, orthogonal experiments were conducted, revealing that the interaction of factors would influence the experimental results. To further optimize the quality of seed granulation coating, the mechanisms of powder and liquid in the adhesion process on granulation coating were explored. Orthogonal experiments were conducted on the process parameters of the granulation coating machine, and the GA-BP model was employed for optimization and solution. The optimal process parameter combination obtained was a coating pan fill ratio of 33.78 %, a single powder supply amount of 5.17 g, a powder supply interval of 7.7 s, and a liquid supply amount of 0.42 mL/s. Under this optimal parameter combination, granulation coating experiments with red clover seeds were performed, and the seed granulation coating quality was relatively high, with a granulation qualification rate of 97.7 %. The research results can provide a reference for optimization experiments on coating irregular seeds.

Influence of spatial position of sound source on dual-frequency ultrasound-assisted preparation of magnesium alloy

In this work, a dual-frequency ultrasonic field was employed to improve the quality and mechanical properties of magnesium alloy ZW61 ingot. The effect of the spatial position of the sound source on the cavitation volume, microstructure size, and morphology was discussed in detail combined with numerical simulations and experiments. The mechanical properties were analyzed elaborately and the relationship between yield strength (YS), ultimate tensile strength (UTS), and average grain size (d) was established. The results showed that the dual-frequency ultrasonic field (DUF) had a larger cavitation range and superior grain refinement ability than the single-frequency ultrasonic field (SUF) at the same energy consumption, with the grain refining from 578 μm (untreated) and 395–433 μm (SUF) to 55.2 μm. The grain refining effect of DUF weakened with increasing sound source spacing, with d increasing from 55.2 μm to 163 μm. The radiating angle had little effect on the grain size. Too shallow or too deep inserting depth of the ultrasonic rods could seriously weaken the grain refinement. In this case, 30 mm source spacing, 60° radiating angle, and 30 mm inserting depth were the optimal process parameter combinations, and the ingot strength was increased from YS of 83 MPa and UTS of 151 MPa to 138 MPa and 241 MPa, respectively. YS and d for all conditions conformed to the Hall-Petch relationship, with σ0 and k values of 71.3 MPa and 503.9 MPa μm−1/2. The relationship between UTS and d could be expressed as UTS = 126.7 + 907.0 d−1/2.

Optimization of multiple responses in the laser cladding process parameters

ABSTRACT An orthogonal test method was used to optimize parameters for Ni60A-25% tungsten carbide (WC) laser cladding layers. Meanwhile, the tensile properties of the optimized laser cladding layer are explored. A multi-factor analysis of variance (ANOVA) was conducted to examine the effect of factors (laser power-A, scanning speed-B, powdering rate-C, and spot diameter-D) and interactions on responding variable. The results showed that the interaction of scanning speed and powdering rate with spot diameter has a more pronounced impact on the dilution rate compared to a single factor. Based on the ANOVA of the dilution rate, the optimal combination of process parameters was A3C2B1D3. Furthermore, the optimal combination of process parameters was C3B2D3A3 for the effective area. The results showed that the yield strength of the specimens was 512.3 MPa and 509.7 MPa, and the tensile strength was 646.3 MPa and 605.7 MPa for process parameters I and II, respectively.

Sensitivity analysis-based quality optimization strategy for multi-correlation parameters in spinning process

To address the problem of quality optimization of multi-correlation parameters in the spinning process, this paper proposes a new method based on a sparrow search algorithm (SSA). Firstly, a generalized regression neural network (GRNN) is used to investigate the impact of the spinning process parameters on yarn quality, and quality forward modeling in the spinning process is established. And based on the coupling and correlation characteristics of spinning process parameters, sensitivity analysis is used to analyze the influence of each spinning process parameter on yarn quality, the correlation spinning process parameters for further analysis. Then a model of quality optimization with spinning process parameters is established, and SSA is used to solve the model of quality optimization with multi-correlation parameters in the spinning process. Finally, the effectiveness of the proposed method was validated through an instance. The results show that the optimal spinning process parameters combination generation of [32.159 5.2 0.8 14.8 24.540 8588.677 21.708] occurs in a configuration with a fitness value of 0.0003. The proposed sensitivity analysis-based quality optimization strategy reveals good performances in terms of both convergence speed and optimization accuracy, which will provide guidance for improving yarn quality.

Optimization of laser-based glass frit bonding for optoelectronic packaging using response surface methodology

Laser-based glass frit bonding presents considerable advantages, including fast bonding speed and minimal thermal impact, showing significant application prospects in the packaging of temperature-sensitive optoelectronic devices. In this study, the individual effects of critical process parameters on bonding performance have been investigated through single-factor experiments. Response surface methodology is employed to design experiments systematically for the laser-based glass frit bonding process, focusing on exploring the interactive effects among these process parameters. The Box-Behnken design method is implemented for experimental setup, and a regression model has been developed to analyze the resulting data. Analysis of variance, 3D response surface plots, and contour plots are conducted to comprehensively evaluate the interaction effects of parameters on the bonding seam width and strength. The mathematical model is validated and an optimal combination of process parameters is identified, which significantly enhances the bonding quality. The optimized process parameters are also applied for the laser bonding of square-shaped seam to validate the feasibility of the optimized results and to promote the engineering application of glass laser-based glass frit bonding technology in microelectronic component packaging. Inspection of the bonded specimens shows excellent formation quality of the square-shaped bonding seams, confirming the methodology’s effectiveness in advancing optoelectronic packaging technology.

RSM-BASED STATISTICAL APPROACH TO ENHANCE THE COMPRESSIVE PERFORMANCE OF ABS P400 PARTS FABRICATED VIA FDM TECHNOLOGY

This research investigates the performance of ABS P400 parts under compressive loading. These ABS P400 parts are fabricated by fused deposition modeling (FDM) process using omnidirectional printing. The effects of three critical parameters, raster angle (A), air gap (B), and raster width (C), each at three different levels, were analyzed on three different performance measures, i.e. total deformation up to failure ([Formula: see text]) measured in %, maximum energy ([Formula: see text]) in MJ/m3 and break energy ([Formula: see text]) in MJ/m3. Results of experimentations were analyzed using analysis of variance (ANOVA), perturbation curve and 3D surface plots. The research established the anisotropic nature, durability and less brittleness of the FDM fabricated ABSP400 part, which leads to lower strength and also influence the energy absorbing behavior while deformation under compression. Complex relationships were exhibited between the chosen parameters and studied measures. It was also observed that [Formula: see text] is noticeable due to less brittleness of ABS P400 however, [Formula: see text] and [Formula: see text] vary in a contradictory fashion with changes in process parameters. The models were validated using normality plots. Multi-objective optimization was done using the desirability approach to determine the optimal combination of FDM process parameters for optimum responses. The desirability result showed that the optimized input parameter is [Formula: see text][Formula: see text]mm, and [Formula: see text][Formula: see text]mm which yield maximized optimum responses as [Formula: see text]%, [Formula: see text][Formula: see text]MJ/m3, and [Formula: see text][Formula: see text]MJ/m3. The work not only reflects the effect of three FDM parameters on the total deformation up to failure ([Formula: see text] in %) but, in addition, energy absorption behavior of FDM parts under compression is also investigated. The study on the behavior of energy at ultimate stress or ultimate load and breaking stress or breaking load were rarely investigated in earlier research which reflects the novelty of our research.