Optimal Combination Of Process Parameters Research Articles

An Interactive Analysis of Influencing Factors on the Separation Performance of the Screw Press

In order to optimize the separation performance of the screw press and enable its water removal rate and production to meet the production requirements, the influencing rule of the interactive effect of multi-factor parameters on its separation performance was studied by numerical simulation and experimental means. The internal flow field of the screw press was simulated by Fluent software. The rotation speed of the screw axis, back pressure of the slag outlet, and initial water content were taken as influencing factors, and the water removal rate and production were taken as objective functions. The Box–Behnken method was used to analyze the influencing rules of the interactive factors on the water removal rate and production. The results show that the significant factors affecting the water removal rate of the screw press are in the following order: initial water content > back pressure > rotation speed. The significant factors affecting screw press production are in the following order: rotation speed > back pressure > initial water content. The optimal combination of process parameters for the screw press is an initial water content of 55%, a screw axis rotation speed of 30 r/min, and a 5 kPa back pressure at the slag outlet. The water removal rate of 48.9% and production of 234.2 kg/d were obtained.

Vibration quality evaluation of reinforced concrete using energy transfer model

Vibration is an important step to ensure the compactness of reinforced concrete. In this study, the energy transfer model is firstly proposed based on the working mechanism of the internal vibrator and vibration energy theory to evaluate the vibration quality, and the reliability of the model is verified by the experiment of sinking ball. In addition, the optimal combinations of process parameters are obtained. The worse the fluidity of concrete and the larger the aggregate proportion, the faster the energy attenuation in concrete, but the total energy absorbed by concrete and the energy transfer efficiency of the vibrator increase. The overall efficiency is greater than 57% and the highest average efficiency is 66.1%. The rebar not only accelerates the energy attenuation rate but also reduces the effective energy in the concrete. In the experiments, the average energy consumption rates of rebar are 28.5%, 57.3% and 70.9% with the reinforcement spacing of 20, 10 and 5 cm respectively. The proposed model provides a new method to evaluate the vibration quality, which contributes to dealing with unqualified areas in time and providing reliable process parameters for vibrating construction.

Formability and surface finish analysis of AA-6061 sheets in multipoint incremental forming process

ABSTRACT Incremental sheet metal forming (ISF) is` a versatile manufacturing process carried out without the help of a die and is one of the most sought-after research areas. This research work provides a deep investigation into the impact of input parameters on formability and surface roughness. A Multipoint Incremental Forming (MPIF) tool is used in this work. Through MPIF, better surface finish and improved formability can be achieved. Also, the time required for metal forming can be reduced. The main goal in this work is to obtain improved formability and excellent surface finish in the MPIF process. Response Surface Methodology (RSM) and Multi-Objective Genetic Algorithm (MOGA) are used in this work to find the optimum combination of process parameters, including sheet thickness, vertical step depth, speed and feed rate to obtain improved formability and surface finish.

The Microstructure and Properties of Ni-Si-La2O3 Coatings Deposited on 304 Stainless Steel by Microwave Cladding

In this investigation, microwave radiation was used alongside a combination of Ni powder, Si powder, and La2O3 (Lanthanum oxide) powder to create surface cladding on SS-304 steel. To complete the microwave cladding process, 900 W at 2.45 GHz was used for 120 s. "Response surface methodology (RSM)" was utilized to attain the optimal combination of microwave cladding process parameters. The surface hardness of the cladding samples was taken as a response. The optimal combination of microwave cladding process parameters was found to be Si (wt.%) of 19.28, a skin depth of 4.57 µm, irradiation time of 118 s, and La2O3 (wt.%) of 11 to achieve a surface hardness of 287.25 HV. Experimental surface hardness at the corresponding microwave-cladding-process parameters was found to be 279 HV. The hardness of SS-304 was improved by about 32.85% at the optimum combination of microwave cladding process parameters. The SEM and optical microscopic images showed the presence of Si, Ni, and La2O3 particles. SEM images of the "cladding layer and surface" showed the "uniform cladding layer" with "fewer dark pixels" (yielding higher homogeneity). Higher homogeneity reduced the dimensional deviation in the developed cladding surface. XRD of the cladded surface showed the presence of FeNi, Ni2Si, FeNi3, NiSi2, Ni3C, NiC, and La2O3 phases. The "wear rate and coefficient of friction" of the developed cladded surface with 69.72% Ni, 19.28% Si, and 11% La2O3 particles were found to be 0.00367 mm3/m and 0.312, respectively. "Few dark spots" were observed on the "corroded surface". These "dark spots" displayed "some corrosion (corrosion weight loss 0.49 mg)" in a "3.5 wt.% NaCl environment".

Optimization and Experiment of Hot Air Drying Process of Cyperus esculentus Seeds

To improve the drying efficiency, drying uniformity and germination rate after hot air drying of Cyperus esculentus seeds, this paper proposed a drying method that combines tempering and hot air drying. The drying curve of Cyperus esculentus was obtained by experiment. The influence of the drying method on the drying quality of Cyperus esculentus seeds and the trend of drying quality with the tempering process parameters (the tempering temperature, the moisture content of Cyperus esculentus at the beginning of tempering and the tempering duration) were analyzed by single-factor experiments. The regression models were established by star point design response surface methodology, and the relationships between the tempering process parameters and seed production quality indexes were analyzed. The results showed that the optimal combination of tempering process parameters was a tempering temperature of 24 °C, a moisture content of Cyperus esculentus at the beginning of tempering of 23%, and a tempering duration of 3 h. Under this combination, the tempering process increased the drying uniformity, germination rate and drying efficiency of Cyperus esculentus seeds by 21.122%, 4.205% and 22.832%, respectively. The error between the verification test value and the software optimization parameter value was acceptable. The study showed that the optimized tempering drying process significantly improved the drying quality of Cyperus esculentus seed production, and the results could provide a theoretical basis for production practices.

Process Parameter Optimization for Particles Reinforced Weld-Bonding Joints of DP780 Dual-Phase Steel

Alumina particles were added in the adhesive layer to stabilize the welding process and enhance the load capacity for weld-bonding joints. In this study, the strength of alumina particles reinforced weld-bonding joints of DP780 dual-phase steel sheets was investigated by conducting experiments based on the Box–Behnken Design (BBD). The multivariate regression models between process parameters (welding current, welding time and electrode pressure) and response values (failure load, nugget diameter) were established, and the optimal combination of weld-bonding process parameters was obtained. The findings indicate that welding current has the biggest impact on the strength of joints. The optimum process parameters ranges are: welding current of 8.5 kA, welding time of 125 ms, and electrode pressure of 0.5 MPa. Developed regression models were validated by experiments.

Multi-response optimization of crashworthy performance of perforated thin walled tubes

It is advised to add cuts as circular holes to constructions’ walls for technical requirements such as the assembly and to reduce the weight. Since these induced cutouts have a substantial influence on crashworthiness and failure mechanisms, the size, location, and number of these cutouts should be carefully selected for energy absorption components. This research concentrates on the optimization of process parameters for the crushing performance of perforated thin-walled glass/epoxy (GFRP) square tubes. Taguchi model was adapted to formulate the design of the experiment while desirability function analysis (DFA) was used for process parameters optimization in terms of crashworthiness indicators. Test specimens were fabricated via wet warping by hand lay-up technique and tested under quasi-static axial crushing. Finally, the specimen yielding the optimum process parameter combination was compared with the intact sample. GFRP samples with circular holes exhibit optimum initial peak force ([Formula: see text]) and total absorbed energy [Formula: see text] with values of 38.35 and 4.47 % which are smaller than those of the intact samples. On contrary, the optimum specific absorbed energy [Formula: see text] and crush force efficiency ([Formula: see text]) present, respectively, 31.08 and 80.28 % greater than those of intact one. The considered process parameters were noticed to be of substantial influences on the crush behavior of perforated GFRP tubes exposed to axial compression.

Application of Taguchi coupled with genetic algorithm (GA) for optimizing surface quality in drilling of duplex stainless steel 2205

This paper aims to explore the drilling performance of duplex stainless steel (DSS) 2205 under sustainable machining, that is dry, MQL, and LCO2. Furthermore, for the correct choice of process parameters, the Taguchi statistical method is used to create a relationship between input parameters, that is cooling environment, spindle speed ( N), feed rate ( Vf), and responses, that is Ra, and hole deviation. From the ANOVA analysis, it was discovered that for measured Ra and hole deviation, the most effective parameter was the cooling environment. The model for Ra and hole deviation was obtained with a precision of 99.32% and 99.70%, respectively. In addition, a genetic algorithm (GA) was implemented for an optimum combination of process parameters and an optimized value of responses. From GA, it was obtained that the optimum process parameters were an LCO2 cooling environment with a high spindle speed (1120 RPM) and a low feed rate (0.02, 0.04 mm/rev), respectively. Also, the optimized value for Ra and hole deviation is 0.375271 and 0.0207519, respectively.

Investigation of the effect of wear behaviour of 3D printed electric arc sprayed 316L SS coated PLA parts

3D Printing is a globally adopted, rapidly growing technology with promising results. It possesses several key properties such as part customization, simple operation, low cost, waste minimization, design flexibility, fabricating intrinsic shapes etc. which are not possible by conventional methods. Thus, 3D Printing gained widespread popularity in bio-medicals, aerospace, automotive, training aids, concept testing, end-use products etc. Hence, in the current investigation, Polylactic Acid (PLA) samples were fabricated by using a cost-effective 3D printing technique called Fused Deposition Modelling at different parameters. Thereafter, the 316L stainless steel coatings at three different levels (50 µm, 100 µm, and 150 µm) were deposited on the prepared 3D printed samples. Finally, the wear behaviour of coated samples was analysed using the pin on disc wear apparatus. The experimental results showed that the coefficient of friction of coated sample having 100 µm coating thickness was found to be lesser (0.11) than the samples having 50 µm and 150 µm coating thickness. The higher wear resistance of sample having coating thickness of 100 µm may be due to the optimal combination of processing parameters (raster angle: 30°, number of top and bottom layer: 3 and coating thickness: 100 µm).

Multi-objective optimization for laser cladding refractory MoNbTiZr high-entropy alloy coating on Ti6Al4V

In this study, a novel MoNbTiZr refractory high-entropy alloy (RHEA) coating was synthesized on Ti6Al4V surfaces by laser cladding (LC) with different processing parameters. The mathematical models between laser processing parameters (laser power, powder feeding rate, scanning speed) and dilution, porosity, and microhardness were developed by response surface methodology (RSM) and validated by ANOVA based on single-factor experimental results. The results show that the dilution and porosity of the coating are negatively correlated due to the significant difference in melting point between the Ti6Al4V substrate and the RHEA powder. The rise in the dilution causes a decrease in the content of the strengthening elements in the coating, which is the main reason for the sudden drop in the microhardness to 425 HV. Compared to laser power and powder feeding rate, the analyses of variance show that the scanning speed has a relatively high F-value of 70.38. Also, the dilution has a positive correlation with laser power and scanning speed, and is inversely correlated with the powder feeding rate. Finally, a multi-objective optimization in terms of dilution, porosity, and microhardness was performed, and the optimal combination of processing parameters was a laser power of 2700 W, a powder feeding rate of 8 g/min, and a scanning speed of 3 mm/s. The predicted results are in good agreement with the validation results.

Preparation and Process Parameter Optimization of Continuous Carbon Fiber-Reinforced Polycarbonate Prepreg Filament.

Continuous fiber-reinforced composite 3D printing (CFRC 3DP) has become a hot topic of interest for many experts and scholars. Continuous fiber-reinforced prepreg filament (CFRPF) for printing needs to be prepared in advance. In this paper, on the basis of the resin fusion impregnation theory, a fabrication device was designed for continuous carbon fiber-reinforced polycarbonate prepreg filament (CCFRPF). Then, according to the orthogonal test and the TOPSIS entropy weight optimization theory, the optimization method for CFRPF/PC preparation process parameters was proposed, and the relationship between the preparation process parameters and the performance indexes was discussed. The results show that when preparing CCFRPF/PC, the weight of diameter performance index is the largest, about 0.75. The optimal combination of process parameters for CCFRPF/PC is, respectively, 285 °C for the outlet mold temperature, 305 °C for the impregnation mold temperature, and 1 m/min for the winding speed. In this case, the diameter, roundness, minimum curvature radius and tensile strength of 0.375 mm, 29.4 μm, 9.775 mm and 1298 MPa were achieved, respectively.

Multi-Objective Optimization of Liquid Silica Array Lenses Based on Latin Hypercube Sampling and Constrained Generative Inverse Design Networks.

Injection molding process parameters have a great impact on plastic production quality, manufacturing cost, and molding efficiency. This study proposes to apply the method of Latin hypercube sampling, and to combine the response surface model and "Constraint Generation Inverse Design Network (CGIDN)" to achieve multi-objective optimization of the injection process, shorten the time to find the optimal process parameters, and improve the production efficiency of plastic parts. Taking the LSR lens array of automotive LED lights as the research object, the residual stress and volume shrinkage were taken as the optimization objectives, and the filling time, melt temperature, maturation time, and maturation pressure were taken as the influencing factors to obtain the optimization target values, and the response surface models between the volume shrinkage rate and the influencing factors were established. Based on the "Constraint-Generated Inverse Design Network", the optimization was independently sought within the set parameters to obtain the optimal combination of process parameters to meet the injection molding quality of plastic parts. The results showed that the optimal residual stress value and volume shrinkage rate were 11.96 MPa and 4.88%, respectively, in the data set of 20 Latin test samples obtained based on Latin hypercube sampling, and the optimal residual stress value and volume shrinkage rate were 8.47 MPa and 2.83%, respectively, after optimization by the CGIDN method. The optimal process parameters obtained by CGIDN optimization were a melt temperature of 30 °C, filling time of 2.5 s, maturation pressure of 40 MPa, and maturation time of 15 s. The optimization results were obvious and showed the feasibility of the data-driven injection molding process optimization method based on the combination of Latin hypercube sampling and CGIDN.

Surface Feature Prediction for Laser Ablated 40Cr13 Stainless Steel Based on Extreme Learning Machine.

Determining an optimal combination of laser process parameters can significantly improve the efficiency and quality of 40Cr13 steel surface processing. In this study, two machine learning models (ELMSS and ELMPS) were proposed to predict the processing results of surface features to optimize process parameters. The prediction accuracies of the proposed models were always higher than those of traditional back propagation (BP) and radial basis function (RBF) neural networks, and the calculation time of the proposed models was significantly reduced. In comparison, the prediction accuracy ranking for ablation depth was ELMSS (92.6%), BP (89.8%), and RBF (89.6%), and for the ablation width, it was ELMSS (98.3%), BP (97.4%), and RBF (96.1%). The material removal rate was 92.4%, 91.1%, and 89.1% for ELMSS, BP, and RBF, respectively. Finally, the prediction accuracy ranking for surface roughness was 86.8%, 80.7%, and 79.5% for ELMPS, BP, and RBF, respectively. After optimization by the genetic algorithm, the prediction accuracies of the proposed models for the depth, width, material removal rate, and surface roughness reached 94.0%, 99.0%, 93.2%, and 91.2%, respectively. With the support of ELMSS and ELMPS, the results of the surface features can be predicted before machining and the appropriate process parameters can be selected in advance.

Automation of the single-track study for L-PBF additive manufacturing processes (for AlSi10Mg powder)

When using a new powder material in each individual selective laser melting unit, the process engineer has to conduct process development works (PDW) to find the optimal combination of process parameters. The results of the PDW are analysed manually by the process engineer without the use of process decision support tools. Due to the expanding technological capabilities of the equipment and the available range of material properties, the possible set of parameters is also expanding, complicating the already non-trivial process of searching for printing process parameters. In order to reduce the PDW time, in this paper we proposed a rapid analysis tool to select individual experimental results with single welded tracks of AlSi10Mg powder. The process of conducting the experiment on domestic equipment was reviewed and the efficiency of the automation tool was investigated in comparison with the traditional manual selection approach. It is concluded that it is possible to apply the developed tool for materials and installations of additive manufacturing not considered in the study.

Optimize the process parameters of wire EDM and to analyse the welding characteristics of tig welding joints using AISI 308

Wire Cut EDM technique is widely used to make small and precision cuts. Generally, a material with fragile geometry and hard structure is cut using a wire cut EDM. The main advantage of this method is that, it is a non-conventional machining process therefore it does not have a contact between the tool (wire) and the workpiece. This advantage gives it an edge over other machining techniques and it makes the process capable of machining even the weak structured and delicate materials. In the present experimental work, by using various machining parameters (Pulse on Time, Pulse off Time, and Wire Speed) were optimized using Taguchi’s L9 orthogonal array. Welding is most the important industrial processes, therefore lots of techniques have been developed to get an efficient and low-cost welding process for different types of materials. In this we will discuss about the welding characteristics of the of tig welding joints produced using different currents. That is, by changing the voltages for the number of passes to improve and strengthen the welded joint The research work aims to optimize the wire cut EDM process parameters for AISI 308 steel and analyze the welding characteristics of Tungsten Inert Gas (TIG) welded joints of the same material. The study intends to find the optimum combination of process parameters that would lead to the highest material removal rate, lowest wear rate, and best tensile properties of the material.

Optimization of Abrasive Water Jet (AWJ) cutting process accuracy

This paper aims to optimize the accuracy of the Abrasive Water Jet (AWJ) cutting process. The AWJ technology is a fast-growing production technology that competes with conventional machining methods due to its ability to cut various materials and conduct precise cutting of complex contours. In this research, the AWJ cutting process was tested on 18CrNiMo7-6 steel using almandine garnet type J80A as the abrasive material. The Taguchi method was used to optimize the process parameters. As control parameters chosen abrasive flow rate, pressure, and traverse speed. The output parameters measured were surface roughness and taper angle. The results shown managed to find the optimal combinations of control process parameters causing the lowest surface roughness and taper angle. The study provides insights into how the Taguchi method can be used to optimize the AWJ cutting process to achieve greater accuracy.

Optimization of Wear Parameters for Duplex-TiAlN Coated MDC-K Tool Steel Using Fuzzy MCDM Techniques

The present work evaluates the effects of different tribological process parameters on the measured responses such as hardness, coefficient of friction, surface roughness, wear mass loss and wear depth of duplex-TiAlN coated MDC-K tool steel material. The considered tribological process parameters are load, sliding velocity, and sliding distance. A full factorial design with 27 experimental runs is employed and based on the response values, an optimal combination of the tribological process parameters is subsequently determined. Different multi-objective optimization techniques, like overall evaluation criteria and fuzzy-based multi-criteria decision-making methods (fuzzy evaluation based on distance from the average solution, fuzzy technique for order of preference by similarity to ideal solution, and fuzzy complex proportional assessment) are utilized to identify the optimal intermixes of the considered tribological process parameters. Sensitivity analysis with respect to changing weights of the responses is performed to validate the derived rankings of the trials, whereas the results of analysis of variance revealed the most significant parameters were influencing the responses. In addition to this, two different published problems related to optimization of wear parameters were solved using the proposed method to check its capability.

Research and analysis of intercalated meltblown nonwovens based on machine learning

This paper explores the relationship and change law between process parameters, structural variables and product performance, and through seeking the optimal combination of process parameters, the filtration efficiency of the product is as high as possible and the filtration resistance is as small as possible. Firstly, the correlation between structural variables and product performance is analyzed by Spearman correlation coefficient method after the relevant data are processed by factor analysis method. Secondly, linear regression, random forest regression and lightGMB regression models are used to judge the relationship between process parameters and structural variables. Finally, the relationship between structural variables and product performance can be obtained by using multidimensional scaling transform (MDS). The results showed that the product filtration efficiency was the highest when the acceptance efficiency distance was 20m and the hot air velocity was 800km/h.

Optimizing the quality control of multivariate processes under an improved Mahalanobis–Taguchi system

Quality characteristics in manufacturing are correlated and do not follow a normal distribution. This study proposes a quality control method for multivariate manufacturing processes that are based on an improved Mahalanobis–Taguchi System (IMTS). The MTS has no data distribution assumptions and identifies anomalies through the Mahalanobis distance (MD). However, a covariance distance can consider the correlation between variables. Further, to address the shortcomings of the MTS in feature selection and threshold determination. A joint optimization model is proposed in this paper. Under this approach, the IMTS is employed to perform composite analyses on multiple quality characteristics and reduce dimensionality to identify abnormalities and the key quality characteristics that lead to anomalies. Further, various models are compared to construct the optimal non-parametric prediction models for each key quality characteristic. Finally, a conceptual model of process parameter optimization is proposed, which improves the Taguchi method to obtain the optimal combination of process parameters and their importance ranking, as the basis for process adjustment. By applying the proposed method, results show that the IMTS has an abnormality identification rate of 99.5%, which is higher than other methods such as MTS, support vector machine (SVM), back propagation neural network (BPNN), fast correlation-based filter solution SVM (FCBF-SVM) and sequential backward selection BPNN (SBS-BPNN). The dimensionality reduction rate is 0.5, which is higher than MTS, SVM, BPNN, and SBS-BPNN methods. The random forest (RF) algorithm is used for accurate predictions of all five key quality characteristics, the improved Taguchi method guided adjustments to manufacturing processes objectively, effectively, and economically.

Multi-objective optimization of friction stir spot welded Al 6061-T6 incorporated with silicon carbide using hybrid Grey Rational Analysis-Taguchi technique

The present research investigation focused on multi-objective optimization of friction stir spot welding process for attaining an optimal combination of process parameters in order to obtain stronger welds. Experiments were designed using Taguchi L9 orthogonal array on process parameters such as, tool pin profile, tool shoulder diameter and shoulder plunge depth. The output quality characteristics measured were tensile-shear strength and micro-hardness. Multi-objective optimization was done using Hybrid Grey Rational Analysis-Taguchi technique. The significance of parameters was determined using Analysis of variance technique. The optimality of results was verified by confirmatory test.