Optimization Of Process Parameters Research Articles

Yield Stress and Elongation Prediction and Process Parameter Optimization of Two C-Mn Steel Based on XGBoost and Non-dominated Sorting Genetic Algorithm-IIization of C-Mn steel based on XGBoost algorithm

Yield Stress and Elongation Prediction and Process Parameter Optimization of Two C-Mn Steel Based on XGBoost and Non-dominated Sorting Genetic Algorithm-IIization of C-Mn steel based on XGBoost algorithm

Mechanical properties and thermal shock resistance of TiB2‒B4C composite ceramic tool materials at microscopic scale

AbstractA finite model incorporating microstructure was developed to investigate the thermal shock resistance of TiB2‒B4C composite ceramic cutting tool materials. Numerical simulations were conducted to analyze changes in the transient temperature field and thermal stress field during the thermal shock process. TiB2‒B4C composite ceramic tool materials were fabricated using discharge plasma sintering technology. With different B4C contents, different sintering temperatures and holding times as study variables, optimization of the sintering process parameters and ceramic material components was achieved through testing the mechanical and microscopic morphology of the ceramics. Thermal shock water quenching experiments were performed, and the strength‐decay method was employed to assess the thermal shock resistance of TiB2‒B4C ceramics. The findings indicate that the optimal sintering conditions are temperature of 1700°C and holding time of 5 min. The TiB2‒B4C ceramic material containing 20 vol% B4C exhibits superior comprehensive mechanical properties and thermal shock resistance, and its relative density, fracture toughness, hardness, and flexural strength were 99.3%, 6.8 MPa m1/2, 22.8 GPa, and 598 MPa, respectively. The critical thermal shock temperature difference falls within the range of 400°C‒500°C.

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.

Safflower protein as a potential plant protein powder: optimization of extraction and spray-drying process parameters and determination of physicochemical and functional properties.

The research about sustainable and alternative plant protein sources has accelerated with the increasing need for protein. Safflower meal has a potential to be used in protein production due to its high protein content. This research aimed to produce an alternative plant-based protein powder using safflower meal. Both extraction and spray-drying parameters of safflower protein powder production were optimized using response surface methodology to achieve maximum yield. Moreover, the physicochemical and functional properties of safflower protein were determined and compared with those of commercial protein powders (soy, sunflower, pea, fava bean, and rice). The optimum extraction conditions were found to be 33.06:1 mL-1 g solvent-to-meal ratio, pH 11.00, 23.34 °C extraction temperature, and 30.86 min extraction time, which were achieved with a protein yield response of 75.21%. The highest powder yield (51.28%) was recorded for drying conditions of inlet air temperature of 160.11 °C, aspiration rate of 54.17 m3 h-1, and feed flow rate of 16.01 mL min-1. According to the amino acid profile of safflower protein, the glutamic acid content (14 475 mg (100 g)-1) was highest, while the methionine content (96 mg (100 g)-1) was lowest. Moreover, safflower protein can be regarded as a high-quality protein due to its high essential amino acid ratio (41.55%). The experiments showed that safflower protein had high solubility and good foam and emulsifying properties. Safflower protein could be a nutritional and functional protein source for the food industry. © 2024 Society of Chemical Industry.

Optimization of Process Parameters for Microwave Drying of Yellow- and Purple-Fleshed Potatoes

AbstractThe main objective of the present work was to study the optimization of microwave drying of potatoes that have different flesh colors. The effects of independent variables of microwave power (300, 450, 600 W), slice thickness (2–4, 6 mm), and steam blanching time (2, 5, 8 min) on the color, total phenolic content (TPC), antioxidant activity, starch ratio, and total monomeric anthocyanin content (TMA) were investigated by using the Response Surface Methodology (RSM). Before drying, potato slices that had different thicknesses were blanched in steam at 90 °C for indicated times. Optimization was applied to improve bioactive compounds, starch ratio, and color. The optimum drying parameters were determined as 300 W, 6 mm, and 8 min for purple-fleshed potatoes, and 450 W, 6 mm, and 2 min for yellow-fleshed potatoes. This study is beneficial to the development of the processing of potatoes in the food industry and provides more insights into the application of microwave drying technology.

Optimization of femtosecond laser processing parameters of SiC using ANN-NSGA-II

Abstract In the field of femtosecond laser machining, it is essential to select the appropriate process parameters to obtain near thermal damage-free and high efficient machining of SiC wafer. In this work, a method of process parameter optimization for femtosecond laser machining of 4H-SiC was proposed by using the predictive ability of the Artificial Neural Network (ANN) and the optimization algorithm of the non-dominated sorting genetic algorithm (NSGA-II). Firstly, the femtosecond laser was used to fabricate microgrooves on SiC wafers, and the effects of process parameters (laser average power, scanning speed and repetition rate) on groove depth, width, heat affected zone (HAZ) and material removal rate (MRR) were investigated. Secondly, The ANN model is established based on experimental data. Other experiments verify the accuracy of the model, and the average error in the model's predictions is around 5%. Thirdly, Pareto optimal solutions are obtained by global optimization of the ANN model using the NSGA-II. The experimental results show that the Pareto optimal solutions are effective and reliable. This proposed method offers dependable guidance for the selecting and optimizing process parameters of high hardness and brittle materials in the field of femtosecond laser processing, and reduces the cost of selecting the appropriate processing parameters in the production process. The method can also be extended to other machining means, such as turning and milling.

Destruction of n-hexane from the air stream by pulsed discharge plasma: Modelling and key process parameters optimization by CCD-RSM

In this study, a Dielectric-Barrier Discharge (DBD) plasma reactor was used for the degradation of n-hexane from an air stream. a Central Composite Design (CCD) based Response Surface Methodology (RSM), (CCD-RSM) was utilized to explore the effects of experimental parameters including discharge voltage, initial concentration, gas flow rate, relative humidity and their interactions on the reactor performance. The proposed optimization model showed a satisfactory correlation between the predicted and actual results. According to the results, discharge voltage was the most significant parameter affecting the removal efficiency, CO2 selectivity and the level of O3 and NOx formation, whereas energy yield was mainly influenced by gas flow rate and initial concentration. At optimized conditions (discharge voltage of 8.5 kV, inlet concentration of 94.3 ppm, relative humidity of 55 % and gas flow rate of 733.16 ml/min with combined desirability of 0.921), removal efficiency of 92.57 %, CO2 selectivity of 58.87 %, energy efficiency of 0.25 g/kWh, O3 formation of 140.88 mg/m3 and NOx formation of 122.46 ppm were obtained. Water vapour significantly increased the removal efficiency and CO2 selectivity as well as reduced O3 and NOx. The results revealed that the CCD-RSM provided the optimum process parameters for the Non-Thermal Plasma (NTP) system.

Bioeconomic transformation of bio-oil production wastes: a novel adsorbent material for toxic dye adsorption and optimization of process parameters

In this study, for the first time, an adsorbent material was produced from the waste left behind after the bio-oil production process from Terebinth (Pistacia Terebinthus L.) seeds as part of bioconversion, and the adsorption of the hazardous dyestuff methylene blue from aquatic media was investigated. The characterization of the adsorbent was performed using FT-IR, SEM, and BET analysis. The characterization of methylene blue adsorption was conducted to fully understand its nature, including its kinetics, equilibrium, and thermodynamic works. The maximum adsorption capacity (q max) of the monolayer, as determined from the equilibrium data, was calculated to be 166.07 mg g−1. Additionally, the experimental design method was utilized to determine the optimum conditions of the methylene blue adsorption process under various conditions. This study revealed that activated carbons from Terebinth seeds can be used as an economical and environmentally friendly adsorbent, which is very suitable for the removal of highly toxic dyes.

DEVELOPMENT AND OPTIMIZATION OF RABEPRAZOLE CHRONO-MODULATED DRUG DELIVERY SYSTEMS

Objective: Development and optimization of chrono-modulated pulsatile drug delivery systems (CPDDS) loaded with Rabeprazole for treating nocturnal acid breakthrough in ulcer patients was set as the major objective of this work. Methods: CPDDS were developed to provide drug release as two pulses with predetermined gap. Separate microparticles for delayed instant release (DIR) and delayed extended-release (DER) were formulated. Through the optimization of several formulation and process parameters, ER microparticles were created as matrix microspheres. Central composite design was used to understand how the factors affected the responses. The optimized ER microspheres and plain drug were separately subjected to enteric coating to obtain DER and DIR portion microparticles, respectively. Results: With the exception of stirring speed's impact on drug release, every other factor was found to have a significant influence (p<0.05) on every response. The mechanism underlying the Rabeprazole's delayed prolonged release was explained by the SEM images. The microspheres made with Eudragit RSPO at 0.72 g and polyethylene oxide at 0.5 g for 1 g of Rabeprazole at 400 rpm were shown to be the optimal formulation based on the graphical optimization results. After being coated with a terminal enteric coating, this formulation showed delayed release for a duration of 6 h. Conclusion: After oral administration of equal doses of DIR microcapsules along with the optimized DER microspheres could release Rabeprazole effectively as two different pulses at the desired time intervals.

Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.

Process Parameter Optimization of Directed Energy Deposited QT17‐4+ Steel

AbstractThe feasibility of using argon‐atomized QT 17‐4+ stainless steel powder for directed energy deposition (DED) additive manufacturing is studied. The QT 17‐4+ steel is a novel martensitic steel designed based on the compositional modification of the standard 17‐4 precipitation‐hardened (PH) stainless steel. This modification aims to achieve better mechanical properties of as‐deposited components compared to the heat‐treated wrought 17‐4PH steel. In this study, QT 17‐4+ steel powder is used for DED, for the first time. The influence of laser power, laser scan speed, powder feed rate, and hatch overlap on the density is studied. The central composite design is used to determine the experimental matrix of these factors. The response surface methodology is used to obtain the empirical statistical prediction model. Both columnar and equiaxed parent austenite grain structures are observed. X‐ray diffraction analyses reveal a decrease in the percentage of retained austenite from 19% in the powder to 5% after DED. The microhardness of the DED processed sample in the as‐deposited state is slightly higher than that of wrought 17‐4PH steel either solution‐annealed or H900‐aged. A higher 0.2% yield strength, a lower ultimate tensile strength, and lower elongation are observed for the vertically printed test sample, when compared to the horizontal one.

Optimization of process parameters and predicting surface finish of PLA in additive manufacturing—a neural network approach

Optimization of process parameters and predicting surface finish of PLA in additive manufacturing—a neural network approach

Optimization of process parameters for improvement of joint strength of dissimilar aluminum alloys by friction stir welding with an activated flux of titanium diboride

Optimization of process parameters for improvement of joint strength of dissimilar aluminum alloys by friction stir welding with an activated flux of titanium diboride

Multi-response optimisation of wire-arc additive manufacturing process parameters for AISI 4130 steel during remanufacturing process

Wire-arc additive manufacturing (WAAM) has emerged as a critical tool for remanufacturing industrial components. A limited understanding of this technique for quality product manufacturing has hindered its utilisation for industrial applications. This study reports on the optimisation of WAAM process parameters for AISI 4130 steel towards remanufacturing of high-quality products for industrial applications. AISI 4130 steel was selected for this study due to its high strength-to-weight ratio, excellent weldability, and suitability for the WAAM process. Taguchi’s Grey Relational Analysis (GRA) used four factors and three levels in the multiple response optimisation process. The study considered process parameters voltage, current, travel speed and gas flow in the gas metal arc welding (GMAW)-based WAAM technique. Analysis of Variance (ANOVA) results show that voltage, travel speed and gas flow significantly affect material deposition. Voltage had the highest significance (31.61%) compared to other parameters. The optimised process parameters were found to be: voltage –23 V, current –100 A, travel speed −350 mm min−1, and gas flow −10 L min−1. These parameters resulted in tensile residual stresses of 25 ± 74 MPa, microhardness of 171.4 ± 12.2 HV0.3, and a relative density of 98.21%. The microstructural analysis reveals the existence of predominant ferritic and pearlitic colonies. This is due to compounded thermal stresses during the deposition process and alloy composition resulting in tailored microstructure and mechanical properties. The study provides some insights into the WAAM remanufacturing process for producing highly quality industrial components.

Energy-saving and low-carbon leather production: AI-assisted chrome tanning process optimization

Energy-saving and low-carbon leather production: AI-assisted chrome tanning process optimization

Optimization of Process Parameters of Selective Laser Melted Nickel-Based Superalloy for Densification by Random Forest Regression Algorithm and Response Surface Methodology

In this study, Random Forest Regression (RFR) and Response Surface Methodology (RSM) were employed to predict the optimized processing parameters to achieve the highest densification of a nickel-based superalloy Mar-M247LC fabricated by selective laser melting (SLM). The RFR model considered input processing parameters such as laser power, hatch distance, and scanning speed. A dataset of 223 samples, was used to train the RFR model. As a result, the RFR model exhibited accuracy of 99.57%, R2 value of 0.976, Mean Square Error (MSE) of 0.402, and Mean Absolute Percentage Error (MAPE) of 0.426% on testing set. In addition to the RFR model, this study also employed Central Composite Design (CCD) and RSM to optimize the processing parameter sets. Subsequently, this study conducted Box-Behnken Design (BBD) to experimentally validate the accuracy of this RFR model. In the end, a set of the optimal processing parameters was tested and resulted a sample densification of 99.959%, outperformed that in the original database before building the RFR model, which was 99.734%. In summary, the RFR models was able to predict densification with accuracy, and by coupling with RSM, the optimal processing parameter could be obtained, so better densification of the build could be achieved.

Deciphering and predicting changes in antibiotic resistance genes during pig manure aerobic composting via machine learning model.

Livestock manure is one of the most important pools of antibiotic resistance genes (ARGs) in the environment. Aerobic composting can effectively reduce the spread of antibiotic resistance risk in livestock manure. Understanding the effect of aerobic composting process parameters on manure-sourced ARGs is important to control their spreading risk. In this study, the effects of process parameters on ARGs during aerobic composting of pig manure were explored through data mining based on 191 valid data collected from literature. Machine learning (ML) models (XGBoost and Random Forest) were utilized to predict the rate of ARGs changes during pig manure composting. The model evaluation index of the XGBoost model (R2 = 0.651) was higher than that of the Random Forest (R2 = 0.490), indicating that XGBoost had better prediction performance. Feature importance was further calculated for the XGBoost model, and the XGBoost black box model was interpreted by Shapley additive explanations analysis. Results indicated that the influencing factors on the ARGs variation in pig manure were sequentially divided into thermophilic period, total composting period, composting real time, and thermophilic stage average temperature. The findings gave an insight into the application of ML models to predict and decipher the ARG changes during manure composting and provided suggestions for better composting manipulation and optimization of process parameters.

Experimental investigations and optimization of process parameters using COPRAS method during laser beam machining of Hardox-400 spur gears

Experimental investigations and optimization of process parameters using COPRAS method during laser beam machining of Hardox-400 spur gears

Metaheuristic optimization and computational investigation of resistance spot welding process parameter employing Jaya, TLBO and Rao-II algorithm

Metaheuristic optimization and computational investigation of resistance spot welding process parameter employing Jaya, TLBO and Rao-II algorithm

Optimization of process parameters for minimizing the temperature field of high-speed milling of titanium alloy thin-walled parts

Optimization of process parameters for minimizing the temperature field of high-speed milling of titanium alloy thin-walled parts