Optimal Parameter Combination Research Articles

Design and Experimentation of Rice Seedling Throwing Apparatus Mounted on Unmanned Aerial Vehicle

In order to further exploit the production advantages of rice throwing, this paper proposes a systematically designed throwing device suitable for integration with unmanned aerial vehicles (UAVs). The device primarily comprises a seedling carrying and connection system, a seedling pushing mechanism, and a seedling guiding device. The operational principles and workflow of the device are initially elucidated. Subsequently, an analysis of factors influencing rice throwing effectiveness is conducted, with throwing height, working speed, and the bottom diameter of the seedling guide tube identified as key factors. Seedling spacing uniformity and seedling uprightness are designated as performance indicators. A three-factor, three-level response surface experiment is conducted, yielding regression models for the experimental indicators. Through an analysis of the response surface, the optimal parameter combination is determined to be a throwing height of 142.79 cm, a working speed of 55.38 r/min, and a bottom diameter of the seedling guide tube of 43.51 mm. At these parameters, the model predicts a seedling spacing uniformity of 88.43% and a seedling uprightness of 88.12%. Field experiments validate the accuracy of the optimized model results. Experimental data indicate that under the optimal operational parameters calculated by the regression model, the seedling spacing uniformity is 86.7%, and the seedling uprightness is 84.2%. The experimental results meet the design requirements, providing valuable insights for UAV rice-throwing operations.

The Simulation and Parameter Optimization of the Hole-Forming Process of a Duckbilled Hole-Forming Device

This paper addresses the hole-forming process of a duckbilled hole-forming device. Based on a coupled simulation using the multi-body dynamics software RecurDyn and the discrete element software EDEM, the hole-forming mechanism of a duckbilled hole-forming device and the influence of control parameters on the hole-forming performance of the hole-forming device were studied. In this paper, we analyze the direction and speed of soil particles transported under soil disturbance by a hole-forming device through the simulation and study of the hole-forming mechanism of the hole-forming device. By controlling parameters such as the traction angle, forward speed, and mass of the hole-forming device, the influence of the control parameters on the hole-forming trajectory of the duckbilled hole-forming device was investigated. Orthogonal tests determined the optimal combination of control parameters. The results show that the hole-forming process of the hole-forming device mainly comprises squeezing and shearing the soil to form holes, and the hole-forming performance of the hole-forming device was optimal when the traction angle was 17.3°, the forward speed was 1.11 m/s, and the mass of the hole-forming device was 17.9 kg.

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.

Parametric analysis, modeling and optimization of the process parameters in electric discharge machining of aluminium metal matrix composite

Optimizing electric discharge machining (EDM) for aluminum/SiCp metal matrix composites poses challenges due to intricate machine parameters and process complexity, impacting process economy and elevating product costs. The research aims to find the optimal combination of process parameters which include pulse on-time, pulse current, duty cycle (%), gap voltage, sensitivity and flushing pressure for EDM of Al/SiCp-MMC using a copper electrode for the selected response factors such as material erosion rate and surface roughness, Ra. The experiments were designed using the central composite design of response surface methodology and an advanced optimization technique known as Teaching–learning-based optimization (TLBO), is applied to find the optimal combination of process parameters to obtain maximum material erosion rate subject to the desired range of surface roughness (SR), Ra. The combination of the high pulse on-time (i.e. 150 μs) and high pulse current (i.e. 12A) results in high material removal rate with deep craters on the machined surface clearly visible in SEM images contrasting the minimized surface roughness at lower values of pulse on-time (50 μs) and the pulse current (4A). Pulse on - time (Ton) is found to be the most significant factor for material erosion rate and surface roughness with percentage contribution of 70.86 and 54.9 respectively for optimization of the response. The regression models were developed at 95% confidence level for material removal rate and surface roughness with R2 value of 0.93 and 0.95 respectively signifying high degree of accuracy in predicting the response. Confirmation tests conducted to check the adequacy of the established models revealed that the percentage error between the predicted and experimental responses is found to be within acceptable levels. Electron discharge machining of the aluminium metal matrix composite at the optimized conditions could provide economical aspect in the aerospace and automobile industry.

Parameter Design of Sand Casting to Minimize Shrinkage Defects, Porosity Defects and Incomplete Defects

Casting is one of the most commonly used manufacturing method for geometrically complex product. Among the casting technologies, sand casting is often found in application. Beside the advantages of the sand-casting process, it also has a drawback that is often encountered, namely product defects. Product defects can be caused by a lack of attention to sand casting parameters. This work aims to minimize those defects, including porosity defects, shrinkage defects and incomplete defects by optimizing process parameters using the Taguchi method. The identified sand-casting process parameters include gating location, riser location, molding conditions and pouring temperature with each parameter consisting of 3 levels. This research using the fractional factorial L9 (34). Data processing is carried out by analysis of mean (ANOM) to obtain plot effects. The results showed that the optimal combination of parameters that resulted in shrinkage, porosity and minimal incomplete defects were at gating location level 2, riser location level 1, mold condition level 2 and pouring temperature level 3. The results of the comparison before and after optimization were an increase of 0.77, where previously it was 1.30 to 2.07 based on normalized data.

SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal

The advantage of the Support Vector Machine (SVM) is that it can solve classification and regression problems both linearly and non-linearly. SVM also has high accuracy and a relatively low error rate. However, SVM also has weaknesses, namely the difficulty of determining optimal parameter values, even though setting exact parameter values affects the accuracy of SVM classification. Therefore, to overcome the weaknesses of SVM, optimizing and finding optimal parameter values is necessary. The aim of this research is SVM optimization to find optimal parameter values using the Grid Search Cross-Validation method to increase accuracy in schizophrenia classification. Experiments show that optimization parameters always find a nearly optimal combination of parameters within a specific range. The results of this study show that the level of accuracy obtained by SVM with the grid search cross-validation method in the schizophrenia classification increased by 9.5% with the best parameters, namely C = 1000, gamma = scale, and kernel = RBF, the best parameters were applied to the SVM algorithm and obtained an accuracy of 99.75%, previously without optimizing the accuracy reached 90.25%. The optimal parameters of the SVM obtained by the grid search cross-validation method with a high degree of accuracy can be used as a model to overcome the classification of schizophrenia.

Numerical Simulation of Lost-Foam Casting for Key Components of A356 Aluminum Alloy in New Energy Vehicles.

The intricate geometry and thin walls of the motor housing in new energy vehicles render it susceptible to casting defects during conventional casting processes. However, the lost-foam casting process holds a unique advantage in eliminating casting defects and ensuring the strength and air-tightness of thin-walled castings. In this paper, the lost-foam casting process of thin-walled A356 alloy motor housing was simulated using ProCAST software (2016.0). The results indicate that the filling process is stable and exhibits characteristics of diffusive filling. Solidification occurs gradually from thin to thick. Defect positions are accurately predicted. Through analysis of the defect volume range, the optimal process parameter combination is determined to be a pouring temperature of 700 °C, an interfacial heat transfer coefficient of 50, and a sand thermal conductivity coefficient of 0.5. Microscopic analysis of the motor housing fabricated using the process optimized through numerical simulations reveals the absence of defects such as shrinkage at critical locations.

Modelling and optimization of electrodeposited amorphous Fe-P alloys using central composite design and response surface method

Central composite circumscribed (CCC) design and response surface methodology (RSM) were used to model and optimize the electrodeposition of amorphous Fe-P alloys. Based on our previous single-factor experimental results, the significance of influencing factors was ranked using analysis of variance (ANOVA) method. Three factors, significantly impacting the P content in deposits, hardness and corrosion current density, were identified, including bath temperature, pH values and H2PO2− concentration. The statistic relationships between process parameters and individual responses were established based on the CCC experimental data and RSM. The optimal parameters for each response were derived and the influences of interaction terms were investigated. The predicted maximum P content of 26.35 wt% was achieved at a bath temperature of 60℃, a pH of 1.2 and H2PO2− concentration of 60 g/L. The predicted highest microhardness was 534 HV0.1 under the optimal conditions of temperature of 60 ℃, pH value of 1.6 and H2PO2− concentration of 49 g/L. The lowest corrosion current density was predicted to be 1.34 μA·cm−2 under the conditions of a bath temperature of 60℃，a pH value of 1.2, and a H2PO2− concentration of 53 g/L. A desirability function was applied to explore the optimal comprehensive performance of both high hardness and low corrosion current density. The optimal parameter combination for comprehensive performance was bath temperature of 60℃, pH of 1.4, and H2PO2− concentration of 49 g/L, and the resulting hardness and corrosion current density were 527 HV0.1 and 2.40 μA·cm−2, respectively. Due to the complex electrodeposition mechanism of amorphous Fe-P alloys, the predicted P content in deposits largely deviated from the experimental result, but the hardness, corrosion current density and comprehensive performances show high prediction accuracy.

OPTIMASI MULTI PARAMETER MESIN 3D PRINTER FDM UNTUK MATERIAL THERMOPLASTIC POLYURETHANE GUNA MENGHASILKAN ERROR DIMENSI TERKECIL MENGGUNAKAN METODE 2K FACTORIAL DESIGN

Fused Deposition Modeling or FDM is a 3D printing technology that is commonly used in manufacturing processes based on Additive Manufacturing Technology. One of the materials that can be used on FDM machines is thermoplastic polyurethane (TPU). One measure of the quality of printed results is the machine's accuracy in producing workpieces that have the smallest possible dimensional error. So this research aims to obtain the printing parameters that have the most influence on print results and find out the optimal combination of printing process parameters on the machine to produce the smallest dimensional error for TPU material. The design of experiment method used is 2k factorial design using 4 process parameters, 2 levels and 3 responses. The parameters used are nozzle temperature, print speed, fill density, and enclosure box. The optimization results obtained an optimal combination of printing process parameters to produce the smallest dimensional error, namely nozzle temperature of 210°C, print speed of 30 mm/s, fill density of 100%, and not using an enclosure box.

Staging of Liver Fibrosis Based on Energy Valley Optimization Multiple Stacking (EVO-MS) Model.

Currently, staging the degree of liver fibrosis predominantly relies on liver biopsy, a method fraught with potential risks, such as bleeding and infection. With the rapid development of medical imaging devices, quantification of liver fibrosis through image processing technology has become feasible. Stacking technology is one of the effective ensemble techniques for potential usage, but precise tuning to find the optimal configuration manually is challenging. Therefore, this paper proposes a novel EVO-MS model-a multiple stacking ensemble learning model optimized by the energy valley optimization (EVO) algorithm to select most informatic features for fibrosis quantification. Liver contours are profiled from 415 biopsied proven CT cases, from which 10 shape features are calculated and inputted into a Support Vector Machine (SVM) classifier to generate the accurate predictions, then the EVO algorithm is applied to find the optimal parameter combination to fuse six base models: K-Nearest Neighbors (KNNs), Decision Tree (DT), Naive Bayes (NB), Extreme Gradient Boosting (XGB), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF), to create a well-performing ensemble model. Experimental results indicate that selecting 3-5 feature parameters yields satisfactory results in classification, with features such as the contour roundness non-uniformity (Rmax), maximum peak height of contour (Rp), and maximum valley depth of contour (Rm) significantly influencing classification accuracy. The improved EVO algorithm, combined with a multiple stacking model, achieves an accuracy of 0.864, a precision of 0.813, a sensitivity of 0.912, a specificity of 0.824, and an F1-score of 0.860, which demonstrates the effectiveness of our EVO-MS model in staging the degree of liver fibrosis.

Parameters Optimization and Physicochemical Properties Characterization of Nanobiochar Prepared from Apple Branches by Ball Milling

Nanobiochar exhibits promising applications across a spectrum of fields due to its exceptional physicochemical properties. Ball milling, a prominent nanobiochar synthesis method, is influenced by process factors and biochar pretreatments, yet dominant controls still remain uncertain. In this study, the apple branch bulk biochar (pyrolyzed under 350[Formula: see text] C) was ball milled under different milling speeds, ball-to-powder mass ratios, and milling durations to investigate the optimal process parameter combinations for ball milling method. Then, we evaluated the impact of pyrolysis temperature and pretreatment temperature of bulk biochar on ball milling effect using optimized parameter combinations. Subsequently, the physicochemical properties of the bulk biochar and nanobiochar were measured. The results showed that high speed with low ball-to-powder mass ratio, low speed with high ball-to-powder mass ratio, and high pyrolysis temperature could significantly decrease the particle sizes of nanobiochar. Ball milling duration and pretreatment temperature had no significant effect on the particle sizes of biochar. The optimal combination of the rotational speed, ball-to-powder mass ratio and milling duration was 400 RPM, 100:1, 2 h and 700 RPM, 20:1, 2 h. The bulk biochar pyrolyzed at 550[Formula: see text] C could be milled into biochar nanoparticles with an average particle size smaller than 100 nm via above parameters. Nanobiochar shows improved properties such as specific surface area, cation exchange capacity, and surface functional groups over bulk biochar. This study provides a scientific basis for the widespread application of ball milling in the field of preparing nanobiochar.

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Wire arc additive manufacturing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposition rate. One of the basic tasks in WAAM is to determine appropriate process parameters, which will directly affect the morphology and quality of the weld bead. However, the selection of process parameters relies heavily on empirical data from trial-and-error experiments, which results in significant time and cost expenditures. This paper employed different machine learning models, including SVR, BPNN, and XGBoost, to predict process parameters for WAAM. Furthermore, the SVR model was optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. A 3D laser scanner was employed to obtain the weld bead’s point cloud, and the weld bead size was extracted using the point cloud processing algorithm as the training data. The K-fold cross-validation strategy was applied to train and validate machine learning models. The comparison results showed that PSO–SVR predicted process parameters with the highest precision, with the RMSE, R2, and MAE being 1.1670, 0.9879, and 0.8310, respectively. Based on the process parameters produced by PSO–SVR, an optimal process parameter combination was chosen by taking into comprehensive consideration the impacts of power consumption and efficiency. The effectiveness of the process parameter optimization method was proved through three groups of validation experiments, with the energy consumption of the first two groups decreasing by 10.68% and 11.47%, respectively.

Optimization of Mechanical Properties and Evaluation of Fatigue Behavior of Selective Laser Sintered Polyamide-12 Components.

In this paper, a comprehensive study of the mechanical properties of selective laser sintered polyamide components is presented, for various different process parameters as well as environmental testing conditions. For the optimization of the static and dynamic mechanical load behavior, different process parameters, e.g., laser power, scan speed, and build temperature, were varied, defining an optimal parameter combination. First, the influence of the different process parameters was tested, leading to a constant energy density for different combinations. Due to similarities in mechanical load behavior, the energy density was identified as a decisive factor, mostly independent of the input parameters. Thus, secondly, the energy density was varied by the different parameters, exhibiting large differences for all levels of fatigue behavior. An optimal parameter combination of 18 W for the laser power and a scan speed of 2666 mm/s was determined, as a higher energy density led to the best results in static and dynamic testing. According to this, the variation in build temperature was investigated, leading to improvements in tensile strength and fatigue strength at higher build temperatures. Furthermore, different ambient temperatures during testing were evaluated, as the temperature-dependent behavior of polymers is of high importance for industrial applications. An increased ambient temperature as well as active cooling during testing was examined, having a significant impact on the high cycle fatigue regime and on the endurance limit.

Multi-Objective Optimization of Tribological Characteristics for Aluminum Composite Using Taguchi Grey and TOPSIS Approaches

In this study, a multi-objective optimization regarding the tribological characteristics of the hybrid composite with a base material of aluminum alloy A356 as a constituent, reinforced with a 10 wt.% of silicon carbide (SiC), size 39 µm, and 1, 3, and 5 wt.% graphite (Gr), size 35 µm, was performed using the Taguchi method, gray relational analysis (GRA), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making methods. Tribological tests were carried out on a “block on disc” type tribometer with lubrication. Load, sliding speed, and graphite mass concentration were analyzed as input parameters. As output parameters, wear rate and coefficient of friction were calculated. An analysis of variance (ANOVA) was conducted to identify all parameters that have a significant influence on the output multi-response. It was found that the normal load has the highest influence of 41.86%, followed by sliding speed at 32.48% and graphite addition at 18.47%, on the tribological characteristics of composites. Multi-objective optimization determined that the minimal wear rate and coefficient of friction are obtained when the load is 40 N, the sliding speed is 1 m/s, and the composite contains 3 wt.% Gr. The optimal combination of parameters achieved by GRA was also confirmed by the TOPSIS method, which indicates that both methods can be used with high reliability to optimize the tribological characteristics. The analysis of worn surfaces using scanning electron microscopy revealed adhesive and delamination wear as dominant mechanisms.

Calibration and Verification of Discrete Element Parameters of Surface Soil in Camellia Oleifera Forest

To analyze the interaction between the surface soil and the soil-contacting component (65 Mn) in the camellia oleifera forest planting area in Changsha City, Hunan, China, in this study, we conducted discrete element calibration using physical and simulation tests. The chosen contact model was Hertz–Mindlin with JKR cohesion, with the soil repose angle as the response variable. The repose angle of the soil was determined to be 36.03° based on the physical tests. The significant influencing factors of the repose angle determined based on the Plackett–Burman test were the soil–soil recovery coefficient, soil–soil rolling friction coefficient, soil-65 Mn static friction coefficient, and surface energy of soil for the JKR model. A regression model for the repose angle was developed using the Box–Behnken response surface optimization method to identify the best parameter combination. The optimal parameter combination for the JKR model was determined as follows: surface energy of soil: 0.400, soil–soil rolling friction coefficient: 0.040, soil-65 Mn static friction coefficient: 0.404, and soil–soil recovery coefficient: 0.522. The calibrated discrete element parameters were validated through experiments on the repose angle and steel rod insertion. The results indicated that the relative errors obtained from the two verification methods were 2.44% and 1.71%, respectively. This research offers fundamental insights for understanding the interaction between soil and soil-contacting components and optimizing their design.

Design and Parameter Optimization of Rotary Double-Insertion Device for Small Arched Insertion Machine

China’s small arched shed-building machinery suffers from a low degree of mechanization, building efficiency, and qualification rate for frame insertion. Therefore, we designed a rotary double-insertion device and established the equation for its motion trajectory. The analysis shows that in the rotary insertion process, a better point of entry into the soil exists. A simulation model was constructed in ADAMS, and the static and dynamic trajectories were analyzed. Additionally, the optimal planting and insertion speed ratios were determined. Considering the qualified rate of the insertion frame as the evaluation index to establish a regression model, we adopted a three-factor three-level experimental design and established the planting speed ratio, center distance of the planting arm, and length of the pressing rod arm as the main influencing factors. We used Design-Expert 13 to perform the analysis of variance and determined the optimal parameter combinations. The experimental results show that the planting speed ratio was 0.7, the center distance of the planting arm group was 554 mm, the length of the pressing rod arm was 923 mm, and the qualification rate of trellis planting at this time was 98.05%. The bench was adjusted and tested based on the optimal parameter combination. The average value of the measured trellis qualification rate was 96.73%, and the relative error between the test value and the theoretical optimization value was 1.32%, thereby verifying the reliability of the optimal parameter combination. Field verification test results show that the rotary double-insertion device had a planting speed ratio of 0.7 and a trellis qualified rate of 95.74% compared with the theoretical optimization value of 2.31%. Conforming to the design requirements of small arch shed-building machinery, the prototype operation performance was stable.

Combined Shewhart–EWMA and Shewhart–CUSUM monitoring schemes for time between events

AbstractTo improve the detecting abilities of upward and downward parameter changes in high‐quality process, two combined schemes by integrating memory‐type, that is, exponentially weighted moving average (EWMA) or cumulative sum (CUSUM), and memoryless, that is, Shewhart, are proposed for monitoring the time between events (TBE), which is modeled as an exponential distributed variable. A Monte Carlo simulation method is employed to obtain the Run Length () properties, that is average run length (), of the proposed schemes for different parameter settings. The nearly optimal monitoring parameter combinations under different change size are obtained by minimizing the out‐of‐control with the satisfied in‐control . Using the designed parameters, the performance of the proposed monitoring schemes is compared with the existing EWMA and CUSUM TBE. The results show that the proposed combined Shewhart–EWMA or Shewhart–CUSUM TBE generally perform better than the corresponding EWMA or CUSUM TBE for large changes and they also show better performance than the Shewhart TBE for small changes. Finally, a real dataset of organic light‐emitting diode (OLED) failure time from Sumsung company is employed to indicate the usage and implementation of combined TBE schemes.

Simulation of the mixing component of a horizontal air-assisted centralised wheat metering device

A horizontal air-assisted centralised metering device involves the use of conveying airflow for mixing and conveying during the seeding process, making it suitable for high-speed precision seeding. This study identified key structural parameters for the mixing component. Computational fluid dynamics (CFD) simulations were used to analyse the influence of the key structural parameters on airflow distribution and airflow velocity. The results suggested selecting a contraction section conical angle of 40° with maximal conveying airflow velocity and minimal airflow pressure loss. A mixing chamber length of 25 mm prevented the retention and blockage. An expansion section conical angle of 20° achieved higher airflow velocity within the expansion section. CFD-DEM (Discrete Element Modelling) coupled simulation were used to analyse the influence of mixing chamber height, seeding rate, and conveying airflow velocity on seed conveying performance. The results indicated that a mixing chamber height of 16 mm ensured stable seed acceleration, reduced the probability of seed-wall collisions. Seed collisions within the mixing chamber and expansion section increased noticeably along with the rising seeding rates. While airflow velocity in the range of 16–25 m s−1 facilitated timely seed conveyance and reduced seed-wall collisions. Verification experiments for the optimal parameter combination of the mixing component indicated that a conveying airflow velocity of 22 m s−1 resulted in the stability coefficient of variation of total seeding mass not exceeding 1.04 %, the uniformity coefficient of variation of seeding mass in each row not exceeding 3.61 %. This research offers valuable insights for structural improvements in the mixing component of the horizontal air-assisted metering device.

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.

Parameter calibration of discrete element model for gluten densification molding.

The densified powder material is convenient for storage and transportation, with broad market application prospects. In this study, the discrete element model parameters required for simulating gluten densification were calibrated using the Hertz-Mindlin with JKR contact model. Initially, physical testing techniques were utilized to assess the size distribution, density, and angle of repose (AoR) of gluten particles. Following this, the Plackett-Burman test, the steepest ascent test, and the Box-Behnken test were conducted, and the significant factors were obtained: The coefficient of rolling friction (P-P) was 1.038, the coefficient of static friction (P-P) was 0.071, and the surface energy (P-P) was 0.047. Finally, the AoR and densification simulations were performed under the optimal parameter combination, along with validation tests. The results showed that the relative error between the simulated and tested AoR was 0.52%. The compression ratio and compression force curves of simulated and actual were similar.