Mathematical Regression Model Research Articles

Experimental investigation and optimization of heat pipe heat exchanger performance under two distinct heat transport mediums through TOPSIS approach

This paper presents a comprehensive thermal performance analysis of the heat pipe heat exchanger by varying the input process parameters and optimizing the best condition for waste heat recovery through the TOPSIS technique. In this work, the heat pipe is constructed with a copper tube and segmented into evaporator and condenser sections carrying hot and cold fluids with a half-fill ratio. The heat pipe is oriented in 0° horizontally with the wick material made from stainless steel and works based on the capillary principle. The efficiency of the HPHE is evaluated by varying the input operational parameters, namely heat input, mass flow rate of hot fluid, inlet temperature of hot fluid, and mass flow rate of cold fluid, on response variables, specifically thermal resistance, energy balance ratio, universal heat transfer coefficient, and effectiveness. The experiment was designed for four factors at three levels as per Taguchi’s L27 orthogonal array, and the best optimising condition was determined by employing the multi-objective technique for order preference by similarity to ideal solution (TOPSIS) approach. The best input condition found was A3B1C1D3, in which the heat input was at 90 W, the mass flow rate of hot fluid was at 0.2 kg min−1, the mass flow rate of cold fluid was at 0.1 kg min−1, and the inlet temperature was at 60 °C. The effect of process parameters and their significance on output responses was analysed by executing an analysis of variance (ANOVA) statistical method. The mathematical regression model was generated, and validation was made to investigate the prediction error percentage.

Process Optimization of Pellet Manufacturing from Mixed Materials in Ultrasonic Vibration-Assisted Pelleting

Achieving carbon neutrality and alleviating the rural energy predicament are crucial aspects in rural areas, particularly in the severe cold regions of northeast China. Pellets serve as clean, renewable energy sources and are ideal alternative fuels. This study investigated the influencing factors and effects of mixed raw materials in ultrasonic vibration-assisted pelleting (UV-A pelleting). Rice straw and corn stover were mixed to produce pellets, and a central composite rotatable design (CCRD) was conducted to analyze the variables and their interactions on pellet density and durability. Mathematical regression models for pellet density and durability were established and then validated through ANOVA analysis. The results showed that all variables significantly affected the density and durability of pellets. The mixing ratio had a greater impact on pellet durability compared to density due to differences in ingredients. The optimal combination of process parameters included a mixing ratio of 25%, molding pressure of 4 MPa, pelleting time of 37 s, and ultrasonic power output at 200 W, resulting in a pellet density of 1301.18 kg/m3 with a durability reaching 94.26%. The desirability value (0.997) under these optimal conditions confirmed the validity of the models; further experiments also verified their effectiveness. The combustion of the optimized pellet was analyzed using thermogravimetric (TG) and derivative thermogravimetric (DTG) analysis in an air atmosphere. Four combustion stages and ignition temperature were provided.

Optimization of Manifold Microchannel Heat Sink Based on Design of Experiment Method

Abstract The manifold microchannel heat sink (MMCHS) shows high potential to cope with the rapidly increasing power density of electronic devices. It is of significance to search for an optimization method to comprehensively consider multiparameter effects on the performance of microchannel. Here, we have studied the temperature and flow characteristics of the MMCHS via experimental method. Then, the effect of geometric parameters including microchannel aspect ratio (α), hydraulic diameter (Dh), and manifold block numbers (Nm) on the performance of a MMCHS were studied by numerical method. Subsequently, the design of experiment (DOE) method was proposed to optimize the geometric parameters of the MMCHS. The relation of friction coefficient and heat transfer coefficient with respect to the three geometric parameters was gained by the mathematical regression model. The statistically optimized manifold microchannel achieved a 10.0% reduction in temperature rise, 59.9% enhanced in the heat transfer coefficient, and the figure of merit (FOM) of 1.6. Our work shows a novel method to consider the effect of structure parameters on the performance of the manifold microchannel and provides a novel optimization design method for the manifold microchannel.

Modelling and Optimisation of Cooling-slope Parameters of Magnesium AZ91D using Improvement Multi-Objective Jaya Approach for Predicted Feedstock Performance

The cooling-slope (CS) casting technique is one of the simple semi-solid processing (SSP) processes a foundryman uses to produce the feedstock. This study attempts to develop mathematical regression models and optimise the CS parameters process for predicting optimal feedstock performance, which utilises tensile strength and impact strength to reduce the number of experimental runs and material wastage. This study considers several parameters, including pouring temperature, pouring distance, and slanting angles for producing quality feedstock. Hence, multi-objective optimisation (MOO) techniques using computational approaches utilised alongside the caster while deciding to design are applied to help produce faster and more accurate output. The experiment was performed based on the full factorial design (FFD). Then, mathematical regression models were developed from the data obtained and implemented as an objective function equation in the MOO optimisation process. In this study, MOO named multi-objective Jaya (MOJaya) was improved in terms of hybrid MOJaya and inertia weight with archive K-Nearest Neighbor (MOiJaya-aKNN) algorithm. The proposed algorithm was improved in terms of the search process and archive selection to achieve a better feedstock performance through the CS. The study’s findings showed that the values of tensile and impact strengths from MOiJaya_aKNN are close to the experiment values. The results show that the hybrid MOJaya has improved the prediction of feedstock using optimal CS parameters.

Simulation and Optimization of Surface Roughness and Process Performance during Machining of HSS by Micro-WEDM Technology.

When machining high-speed steels (HSS) with micro-wire electrical discharge machining (micro-WEDM), high surface quality is achieved as standard. The value of the roughness parameter Ra is less than 0.2 μm. However, the problem is the performance of the electroerosion process (MRR), which is low. This problem is related to the mechanical and physical properties of the HSS in combination with the setting of the main technological parameters (MTP). The proposed solution to eliminate this problem relies on the selection of proper procedures for the determination of optimization criteria in relation to Ra and MTP, with the inclusion of properties of the machined material. The solution consisted in the identification of four significant physical (ρ, κ) and mechanical (Rm, HRC) indicators of HSS properties, on the basis of which a suitable combination of the process output parameters Ra and MRR can be determined through established mathematical regression models using simulation and optimization. In the next step, the proper values of the MTP output process parameter settings, which correspond to the optimized output parameters Ra and MRR during machining of HSS by micro-WEDM technology, were then obtained by the same approach.

Research on a Vibrationally Tuned Directional Seed Supply Method Based on ADAMS-EDEM Coupling and the Optimization of System Parameters

We have combined the theory of bulk dynamics and the agronomic requirements of precision sowing with the aim of resolving the technical problems of poor seed mobility and the difficulty in controlling suction posture, which leads to an increase in the leakage rate and a reduction in seed qualification index scores. In this study, a vibrationally tuned directional seed supply method and system are proposed. We carried out a force analysis of seeds, constructed kinematic equations for seeds and seed boxes to specify the state of the seed motion, and determined the structural parameters and the range of structural parameters that affect the seed suction posture. In addition, we coupled the ADAMS-EDEM simulation of the motion process of the seed and seed boxes and analyzed the vibrational tuning process of the seeds and the angle of inclination of the bottom surface of the seed box. The speed of the eccentric wheel and the eccentric distance were used as test factors. Three-factor and three-level Box–Behnken central combination testing with a single-grain rate, multiple-grain rate, and cavity rate were used as response indicators. Mathematical models were obtained between the experimental factors and the response indicators. Multi-objective optimization of mathematical regression models was carried out with Design-Expert 10.0.4 software. The optimal parameter combination obtained was a tilt angle of 14.27°, an eccentric wheel speed of 4.48 rad/s, and an eccentricity of 1.94 mm. The rate of single grains was 90.75%, the rate of multiple grains was 3.63%, and the rate of cavities was 5.62%. In bench performance tests, using an angle of inclination of 14°, the speed of the eccentric wheel was 4.50 rad/s and the eccentricity was 2 mm. The mean value of the single-grain rate was 89.28%, the mean value of the multiple-grain rate was 3.89%, and the mean value of the cavity rate was 6.83%. The test error was within permissible limits, and reliable results were achieved for parameter optimization. The results met the technical requirements for precision sowing. The results of the study can provide academic references for theoretical research on the methodology of posturing and directional seed supply. They can also provide ideas for the design and development of seed supply systems for precision sowing machinery.

The sports industry and its effect on the quality of life and regional development

AbstractBackgroundSport plays a significant role in shaping social life, the health of the nation and the development of regions. Studying the impact of the sports industry on the quality of life and regional development can help to better understand how sports influence the social, economic and cultural aspects of people's lives.ObjectiveThis study aimed to investigate the impact of the sports industry on the quality of life and human development in developed and developing countries by analyzing the correlation and modeling of recognized world indices.MethodsThe research employed correlation analysis, regression analysis, and scenario modeling, utilizing publicly available international indices to determine the level of impact of sports on the country's development indicators. While investment in sports development may not be a straightforward decision for developing countries facing socio‐economic challenges, the study demonstrated the long‐term benefits of such investments through evidence‐based mathematical regression models and scenario modeling.ResultsThe study findings revealed that the advancement of the sports industry leads to improvements in the quality of life and human development in most of the studied regions.ConclusionMoreover, the scenario modeling suggested that the evolution of this field would further enhance the development of the quality of life.

Mechanical properties of poplar (NL-3412) branches

ABSTRACT The mechanical properties of poplar (NL-3412) branches are the basis for designing and optimizing the working parameters of branch pruning and crushing machinery. In this study, the tensile, compression, bending and shear properties of poplar branches were investigated by utilizing a precision microcomputer-controlled electronic universal testing machine. The test results suggested that the elastic constitutive relationship of the stress–strain curves of poplar branches under longitudinal compression, transverse compression, and bending conformed to Hooke's law, and longitudinal tensile and shear exhibited plastic damage; the anti-failure strength, anti-failure elastic modulus, and external work of poplar branches under longitudinal compression were greater than those under transverse compression. The analysis results indicated that the correlation function between the anti-failure strength and the external force work of poplar branches is positively correlated, and the external force work required to destroy poplar branches can be estimated by the established regression mathematical model. The results of the study can provide important data support for the optimal design of poplar branch pruning and shredding machinery and related simulation experimental research. HIGHLIGHTS The basic parameters of mechanical properties of poplar branches were determined. The variation law of stress–strain curve of poplar branch mechanical test was proved. The mathematical model between the strength and external force work was established. Provide reference for the optimal design of branch pruning and crushing machinery. Provide data support for the simulation cutting test of poplar branch.

Linear Cutting Performance Tests and Parameter Optimization of Poplar Branches Based on RSM and NSGA-II

To reduce the cutting force and cutting energy consumption during the operation of tree-climbing pruning machines for poplar trees, a linear cutting test bench device for branches was designed according to the growth characteristics of poplar branches and pruning forestry requirements in this study. Firstly, the cutting mechanical analysis of poplar branches was carried out to explore the significance parameters affecting the cutting force, and then the cutting performance test and parameter optimization of poplar branches was carried out through the response surface method (RSM). The test results indicated that cutting speed, tool edge angle and tool back angle had significant effects on the ultimate shear stress, cutting power consumption per unit area, and the branch damage rate of poplar branches, and the established regression equation demonstrated high goodness of fit. Meanwhile, a second-order regression mathematical model was developed between ultimate shear stress, cutting the power consumption per unit area of the cut and the branch damage rate, and the significance parameter. The non-dominated Sorting Genetic Algorithm II (NSGA-II) was used for multi-objective optimization computation to obtain the optimal combination of cutting parameters as 3.02 m/s for cutting speed, 15° for tool edge angle, and 3° for tool back angle. In this case, the ultimate shear stress, cutting power consumption per unit area, and branch damage rate of poplar branches were small, which were 346.63 kPa, 9.35 mJ/mm2, and 12.36%, respectively. Through the test verification, it can be seen that the relative error between the verification test and the predicted value of model was less than 7%. Moreover, under a cutting tool edge angle of 15°, the ultimate shear stress, cutting power consumption per unit area, and branch damage rate were, respectively, reduced by 17.29%, 14.98%, and 34.21% compared with those under a cutting tool edge angle of 20°, which verifies the validity and reliability of the test results and the research method. This study can provide data support and reference for the research and development of energy-efficient poplar tree-climbing pruning equipment and related branch-cutting performance tests.

Design and Experiment of Adaptive Profiling Header Based on Multi-Body Dynamics–Discrete Element Method Coupling

To promote the germination of rice panicles during the regeneration season, it is necessary to ensure a stubble height of 300–450 mm when mechanically harvesting the first-season rice. However, due to variations in the depth of the paddy soil and fluctuations in the height of the header during harvesting, maintaining the desired stubble height becomes challenging, resulting in a significant impact on the yield during the regeneration season. This study presents the design of an adaptive profiling header capable of adjusting the height and level of the header adaptively. Based on the theoretical analysis of the profiling mechanism, a quadratic regression orthogonal rotation combination experiment is designed. Considering the actual field conditions, the range of each factor is determined, and simulation experiments are conducted based on the MBD-DEM coupling to establish a mathematical regression model between each factor and indicator. In the case of the profiling wheel linkage length of 562 mm, profiling wheel width of 20 mm, and profiling wheel mass of 3.6 kg, the supporting force of the header on the profiling wheel would be greater than zero, the supporting force of soil on the profiling wheel and the depth of soil subsidence represent the smallest values, and the highest sensitivity and accuracy of the profiling wheel are achieved. Bench tests demonstrated that the header exerts a force on the profiling wheel, confirming the normal functioning of the profiling. The average magnitudes of forces exerted by the soil on the profiling wheel are obtained to be 31.98 N, 31.63 N, and 30.86 N, whereas the corresponding average soil subsidence depths are obtained as 3.4 mm, 5.6 mm, and 8.3 mm, aligning closely with the simulation values. The results indicate that the profiling mechanism achieves high accuracy in ground profiling and that the structural design is reasonable. By employing fuzzy PID control to adjust the height of the header, the average error in adjustment is obtained as 6.75 mm, while the average error in the horizontal adjustment is derived as 0.64°. The header adjustment is fast, offering high positioning accuracy, thereby meeting the harvesting requirements of the first season of ratooning rice.

The analysis of classical, polynomial regression and cubic spline mathematical models in hemp biodiesel optimization: an experimental comparison

Post-pandemic inflationist pressures, climate changes and extremes, regional conflicts, and soaring food prices caused the food crisis to increase rapidly worldwide. This global problem directs producers and researchers to use oils used as feedstock in biodiesel production effectively. In this context, it is important to assay the transesterification parameters and conduct new optimization studies to increase biodiesel yield. In this study, methyl ester was produced from hemp oil by transesterification using sodium hydroxide (NaOH). Next, classical optimization study was carried out to determine the effects of catalyst amount, alcohol:oil molar ratio, reaction temperature, and reaction time variables on biodiesel yield. Secondly, the cubic spline mathematical model (CSMM) and polynomial regression mathematical model (PRMM) were applied to the first data of this optimization. Among these optimization methods, the utmost biodiesel yield registered was 96.115% at hemp seed oil (HSO):methanol molar ratio of 5.59:1, catalyst concentration of 0.531 wt%, reaction temperature of 42.5 °C, reaction time of 62.1 min, and agitation intensity of 600 rpm at PRMM. Some vital fuel properties obtained from HSO biodiesels as a result of three optimizations satisfied the EN 14214 standard. The results illustrated that the optimal yields from CSMM and PRMM are 0.765% and 1.065% higher, respectively, according to the maximum efficiency obtained from the classical optimization. The outcomes showed that CSMM and PRMM are cost-effective, easy to handle, and promising new approaches.

Experimental study and modelling of continuous SO2-depolarized electrolysis in hybrid sulfur cycle for hydrogen production

SO2-depolarized electrolysis (SDE) is the key process in the hybrid sulfur cycle for green hydrogen production, but the detailed SDE characteristics including products generation, current efficiency variation, and side reactions are unclear. Herein, a continuous SDE process was experimentally conducted and theoretically modelled. The H2 and H2SO4 production was accelerated and the current efficiency for both electrodes was improved with a proper increase in current density (50−100 mA/cm2) or temperature (293−318 K), but further increasing current density to 150 mA/cm2 or temperature to 333 K led to a negative trend because of the anode corrosion or declined SO2 solubility. The increasing initial H2SO4 concentration (10−40 wt%) facilitated the cathodic H2 production and current efficiency, while negatively influenced the anodic SO2 conversion. The parasitic reactions occurred at cathode consumed H2 and resulted to the cathodic current efficiency well below 100%. A rigorous mathematical regression model correlating the production rates of H2 and H2SO4 with current density, temperature, and initial H2SO4 concentration was developed, and combined with the analysis of variance, current density was suggested the biggest factor affecting products generation in SDE process. These findings demonstrate a practical avenue for efficient SO2 conversion and H2 production in SDE process of hybrid sulfur cycle.

Performance optimization and investigation of metal-cored filler wires for high-strength steel during gas metal arc welding

Abstract This study examines the utilization of metal-cored filler wire in conjunction with the gas metal arc welding (GMAW) technique for welding high-strength S690QL steel. Since welding parameters significantly impact the bead quality and weld joint integrity, the main objective was to identify the optimal welding parameters. To achieve this, the input variables including the current (A), voltage (V), and gas flow rate (GFR), and their effects were evaluated for reinforcement (R), width (W), depth of penetration (DOP), and the width of the heat-affected zone (HAZ). For a more efficient and cost-effective investigation, a Box–Behnken design, which is based on response surface methodology, was used for bead-on-plate trials. Mathematical regression models, derived from experimental data, were rigorously validated using the analysis of variance, main effects plots, residual analysis, and the R 2 and Adj. R 2 values. Additionally, the heat transfer search (HTS) algorithm was employed for process optimization. While single-objective optimization provided optimal settings for individual responses, simultaneous optimization aimed to strike a balance between multiple, sometimes conflicting, objectives. This comprehensive approach resulted in specific values, including a reinforcement (R) of 4.285 mm, a width (W) of 9.906 mm, a DOP of 2.039 mm, and an HAZ width of 2.020 mm. These values were achieved with specific input parameters: current (221 A), voltage (24 V), and GFR (21 L·min−1). The Pareto solutions offered a nuanced selection of the most suitable configuration, taking into account the desired values for R, W, DOP, and HAZ. The close alignment between predicted and experimentally measured values for the responses highlights the precision and suitability of the HTS algorithm in estimating critical bead geometries during GMAW of S690QL plates.

Design and Experimental Study of Single Plant Harvester for Potato Breeding Experiments

In order to solve the current problems of time-consuming manual work and low harvesting efficiency during the harvesting of potato breeding trials and the inability of traditional harvesting machinery to meet the requirements of potato single plant harvesting, a single plant harvester for potato breeding with a combined excavation device and a conveying and separating device with alternating concave and straight units is designed. This paper analyzes the force, motion, and collision of potato plants in the digging transition stage and conveying and separating stage. The key factors affecting the rate of single plant integrity and the rate of injured potatoes are determined. A three-factor quadratic regression orthogonal rotating combination experiment is conducted with the rate of single plant integrity and the rate of injured potatoes as experiment indexes, forward speed, conveying speed to forward speed ratio, and the rotational speed of the rear-vibration device as influencing factors. Based on the experiment results, a mathematical regression model is established, and response surface analysis and parametric analysis are conducted to determine that when the forward speed is 0.65 m∙s−1, the ratio of conveying speed to forward speed is 0.98, and the speed of the post-vibration device is 11.52 rad∙s−1, the single plant integrity rate is 97.14% and the rate of injured potatoes is 0.67%. The machine can better meet the requirements of potato single plant harvesting and solve the problem of plant mixing. From the perspective of potato damage rate, potato breeding harvesters are also suitable for traditional potato harvesting.

Design and experiment of a shovel-tooth removal end-effector for abnormal plants in hybrid rape breeding based on MBD-DEM coupling.

In view of the problem that removing abnormal plants in breeding rape requires a large amount of labor and is inefficient, combined with the planting requirements of breeding rape, a shovel-tooth end-effector was designed, and a shovel-tooth removal test bench was built. A simulation model based on MBD (Multibody Dynamics)-DEM (Discrete Element Method) coupling was constructed. Then we conducted a Box-Behnken test with four factors and three levels. Taking the angle of soil penetration, speed of soil penetration, depth of soil penetration and speed of shovel-tooth gathering as the test factors, the soil penetration force and shovel-tooth gathering force as the test indicators. The mathematical regression model between test indicators and test factor was established. After optimizing the parameters of the model, the best combination of parameters with low soil penetration force and low shovel-tooth gathering force was obtained: angle of soil penetration of 84°, speed of soil penetration of 9 cm/s, depth of soil penetration of 8cm, and speed of shovel-tooth gathering of 6 cm/s. The simulation model was validated by field experiments. The average soil penetration force and average shovel-tooth gathering force of the three groups of pull-out tests were 34.8 N and 763.0 N, respectively. The removal rates were 96%, 92%, and 94%, all greater than 90%, indicating that the removal effect of the shovel-tooth end-effector was good, and the parameters were reasonably designed. The results can serve as reference for the design of rape abnormal plants removal device and the operation of MBD-DEM coupling simulation end-effector.

EDM Electrode Design and Analysis to Enhance Process Performance

Electric discharge machining (EDM) is regarded as one of the most energy-efficient manufacturing processes for exceptionally accurate processing of any electrically conductive material, regardless of mechanical properties. EDM is a non-contact process utilized in various industries, including aerospace, industrial, instruments, molds, dies, and medical tools; also, it is beneficial for hard materials with simple or proficient geometries and shapes. Although EDM is for machining "difficult to machine" metals, such as those used in the mold and die industries, metal properties indicate potential capability in the domain of surface modification due to the electrical spark between the tool and the work material. This paper investigates the machining of Alloy steel X210 by using the most affecting parameters and how electrode angles contribute to responses, as MRR, Electrode wear ratio, and surface finish, which the manufacturing industry uses to make die tools, molds, automotive parts, and other products. Electrodes with angles of 0o and 90o are used to machine workpieces using WEDM (dk7740). Electrode Angle, peak discharge current, pulse-on time, and pulse-off time were controllable parameters in this study. While all other criteria are constant, variables have been studied. The effects of the four aspects on the four variables were statistically analyzed using ANOVA by MINITAB. Regression models for the responses were developed. Regression mathematical models have been developed for Alloy steel X210 workpieces to increase productivity and industry results.

Design and Experiment of Grain Lifter for Sorghum Harvester

In order to solve the problems of grain lifter in sorghum harvesting, such as ear loss and serious crop leakage loss, combined with the physical and mechanical characteristics of sorghum, the segmented and reverse spiral grain lifter for sorghum harvesting and cutting table was developed, and the design method of the main structural parameters of the grain lifter was determined. The comparative test of the working effect of the clasp showed that the working effect of the cutting table with the clasp was better than that without the clasp, which effectively reduced the harvest loss of the cutting table. By using Box–Behnken experimental design method, the influence law of forward speed, tilt angle, and rotation speed of grain lifter on the rate of ear loss and harvest loss in sorghum harvesting was investigated. The regression mathematical model and response surface of the rate of ear loss and harvest loss and analysis factors were established, and the optimal working parameters of the grain lifter were determined. The forward speed was 0.8 m/s, the tilt angle of the grain lifter was 28°, and the rotation speed of the grain lifter was 330 r/min. Under these conditions, the spike loss rate was 2.01, the leakage loss rate was 2.19, and the error with the theoretical value was less than 3%, which proved the rationality of the optimized combination parameters. In the harvest of crooked and fallen sorghum, the grain lifter can effectively reduce the loss of sorghum head drop and lodging leakage, ensure the reliability of the cutting table, and achieve low loss and efficient harvest of sorghum.

Analysis of Strategies for Enhancing Informatization Teaching Ability of College Teachers Combined with Mathematical Statistics Multiple Regression Models

Abstract In this paper, from the perspective of education informatization, for the problem of teachers’ informatization teaching ability in colleges and universities, the loss function of the analysis of teachers’ informatization teaching ability in colleges and universities can be calculated by using a Logistic regression algorithm. In order to prevent the overfitting problem of multiple regression analysis, it is necessary to optimize the multiple regression analysis by regular transformation, and then construct the model of teachers’ informatization teaching ability based on multiple linear regression, and determine the research variables and regression equations in the model. Starting from the research objectives, the research objects and problems are determined, and two analysis methods, simulation analysis and statistical analysis, are used to empirically analyze the informatization teaching ability of college teachers. The results show that on the analysis of model instances, the correlation coefficients between the factors and the overall teacher educators’ informatization teaching ability range from 0.671 to 0.829, and there is a certain correlation between each factor and teacher educators’ informatization teaching ability. On the analysis of differences in teachers’ informatized teaching competence, the five competence constructs of Guangzhou college teachers’ education in technological literacy (P=0.653>0.05), instructional design (P=0.133>0.05), instructional implementation (P=0.584>0.05), evaluation development (P=0.287>0.05), and informatized teaching competence (P=0.413>0.05) do not significant differences exist. This study comprehensively analyzes the strategies for improving teachers’ informatization teaching ability in order to play some role in promoting the development of education informatization.

Experimental and optimization of die casting parameters on Al-Si alloy with snail shell reinforcing agent

The demand for aluminum in various fields continuously grows, including in the automotive industry. In this industry, aluminum is used as the material for the spare part. Therefore, aluminum with high mechanical properties and low casting defects is required. One of the available alternatives for producing excellent aluminum is through aluminum casting, including die casting. Die casting offers low cost in mass manufacturing of complex shaped components with acceptable casting results. Further, the selection of die-casting parameters and the addition of reinforcing elements can also improve the mechanical properties of aluminum. In this study, we strengthened the Al-Si matrix using High Pressure Die Casting process with particles from snail shell powder (calcium carbonate). Further, this study also explores the mechanical properties and microstructure of the product produced through experiment and optimization. The optimization was adopted to identify the optimum parameter. For the optimization, we used the Taguchi method. Our analysis results suggested that the reinforcing agent from the snail shell powder has the CaO and Ca(OH)2 phases, with a crystallite size of 106.59 nm. The morphology of the shell powder reinforcing agent showed the presence of agglomeration and interconnected structures, such as skeletons, with average particle size of 0.4 micro. The functional group of the shell powder reinforcing agent showed the OH band during the water absorption by CaO, along with asymmetric C–O with vibration from the carbonate group and Ca–O bound. The most excellent hardness level was identified from T8, with 86.33 HRB and die casting parameters of 0.15% reinforce agents, 750 °C temperature injection, and 50 MPa pressure. Meanwhile, the best tensile strength was found from the T9 sample, with 109,95 MPa and die casting parameters of 0.15% reinforce agents, 800 °C temperature injection, and 60 MPa pressure. Microstructure on the used piston die casting sample with snail shell powder reinforcing agent showed the presence of Al, Si, dendrite, and Al4Ca phases. The multiple response analysis on the three factors indicated that the reinforcing agent presented the most significant effects toward the tensile strength and hardness, followed by pressure and temperature injection. Meanwhile, the Taguchi method and ANOVA results showed the optimal parameter die casting was obtained from a combination of 0.15 wt% reinforce agent, 800 °C injection temperature, and 50 MPa pressure. A multiple linear regression mathematical model for tensile strength and hardness was developed from the observed data. In regression model, the value of R2 of tensile strength 74.02% and R2 of the hardness is 95,18% Thus, the developed model can be effectively used to predict the tensile strength and hardness

Modeling the matching stage of HDPE hot plate welding: A study using regression and support vector machine models

AbstractThis research studies the material displacement of high‐density polyethylene (HDPE) during the initial stage of the hot plate welding (HPW) process, called the Matching stage. Newly developed mathematical models simulate the relationship between the main process parameters (force, temperature, and time) and the size of the surface area. The collected data was used to develop four mathematical models for material displacement and time. Two models are machine learning based models (support vector machines). The other two models are regression models in the form of functions. In addition, this study provides a visual representation of HDPE melt displacement with the changes in temperature and pressure. The obtained results indicate that these models can be used for developing a parameter adjustment process or determining specifications for new production equipment. The potential applications of the developed models can be extended to industries that use HPW of HDPE, such as automotive or aerospace. This could result in significant savings in the amount of scrap and waste due to inaccurate and ad hoc settings that currently take place in the industry.Highlights Study of HDPE material displacement in the hot plate welding Matching stage. Visualization of HDPE melt displacement with temperature and pressure changes. Development of SVM and mathematical regression models. Applications in parameter adjustment and equipment specification. Savings in scrap and waste by reducing inaccurate settings.