Virtual Simulation Test Research Articles

Performance evaluation of 3D printed acoustic metamaterial using soft computing approach

This paper introduces a novel methodology for predicting the acoustic absorption coefficient of DENORMS cell based acoustic metamaterial. The samples were printed from resin using Digital Light Processing based 3D printing technique. The manufactured samples were tested in an Impedance tube using the two Microphone method. A virtual simulation test rig was used to generate data sets for geometrically distinct DENORMS cell based metamaterial. Four distinct soft computing techniques specifically the “Neural Networks (NN), Random Forests (RF), Decision Trees (Rpart) and Generalized Linear Model (GLM)”, were employed and compared to develop an accurate prediction model for forecasting the absorption coefficient of the developed metamaterial. The machine learning techniques were used due to their higher speed and lower computational power requirement compared to numerical simulations to determine the absorption coefficient. The input variables consist of the Spherical Diameter, Cylindrical Diameter, Cylinder Length of the DENORMS cell and Frequency of Incident noise. The performance of the four prediction models was evaluated based on criteria such as Root mean square error, Coefficient of determination, Correlation and Accuracy. Ten-Fold cross validation is performed to test the robustness of the model.

Communicating robots’ intent through visual cues enhances human anticipatory behavior in human–dual robot collaboration

The present study aims at exploring the effect of communicating robots’ intent through visual cues, to the human on a complex human-robot collaborative task. Specifically, it aims to investigate (i) whether the use of such “anticipatory cues” will have a positive effect on task efficiency, human safety and collaborating fluency, (ii) the degree of this effect with varying robots’ speed and (iii) whether a retention effect will be observed after the removal of the cues. For exploring these issues, a human - dual robot industrial assembly task was designed in a Virtual Reality simulation environment and testing was carried out by 64 volunteer participants. Results showed that communicating robots’ intent through visual cues enhanced human anticipatory behavior, resulting in a significant improvement in human safety, team efficiency and collaborative fluency, in conjunction with a favorable subjective tendency towards the robots. However, the positive effect of the anticipatory cues was not found to increase with higher robot speed. Finally, the findings suggest that prior exposure to the cues made participants more confident in coordinating with the robots, even when the cues were removed from them, thus retaining their prior efficiency but with a negative effect on safety. In summary, the study provides evidence that use of anticipatory visual cues accelerates the legibility of robot movement and fosters human confidence and familiarization. The use of anticipatory cues seems promising for high-pace, non-repetitive interactions with collaborative robots or as a training aid in more repetitive human-robot collaborative tasks.

Design and Test of Potato Seedling Killing and Residual Film Recycling Integrated Machine

Plastic film mulching technology can effectively enhance crop yield and quality, and the use of mulch has been increasing in recent years; however, the problem of mulch residue is worsening due to the large amount of recycling work and slow natural degradation. In this study, a potato seedling killing and residual film recycling machine is designed to provide good working conditions for potato harvesters before harvesting in response to the problems of difficult separation of film tangles, the low net rate of recycling due to the mixing of residual film with soil, and the high soil content in residual film recycling operations in northwest China. The machine is based on the potato monoculture and double row planting mode in Gansu area. This paper puts forward the overall design scheme and carries out the theoretical analysis and parameter determination of the key components, such as the seedling killing device, the film surface cleaning device, the film unloading device, and so on. Using EDEM software to carry out the virtual simulation test and Design-Expert13 to analyze the test results, we determined the optimal working scheme for the machine, with a forward speed of 0.8 m/s, a film gap of 125 mm, and a spiral stirrer speed of 600 r/min. Based on a field test for verification, the test results show that the machine’s residual film recovery rate was 83.3%, the impurity rate was 3.8%, and the rate of injury to the potatoes was 1.4%. The machine meets the requirements of national and industry standards, and it can simultaneously realize straw crushing, film surface cleaning, residual film recycling, and hydraulic film unloading operations, with better operating results and while reaching the expected results. It can also provide a reference for the design and testing of a seeding and residual film recycling machine.

Multi-objective optimization design for anti-fatigue lightweight of dump truck carriage combined with machine learning

As urbanization continues to accelerate, dump trucks assume an increasingly important role in the transportation and construction of infrastructure. The carriage represents a critical structural assembly of dump trucks. One of the primary failure modes of the carriage is weld fatigue failure, which frequently gives rise to the problem of weld fatigue cracking during transportation. To increase the fatigue life of welds and enhance the degree of structural lightweight of a heavy dump truck carriage, a method for anti-fatigue lightweight design based on machine learning and multi-objective optimization is proposed. A high-fidelity finite element model of the carriage is established for static simulation analysis of the typical conditions. Based on the virtual reliability simulation test of the dump truck and the equivalent structural stress method, the fatigue life of the critical welds in the carriage is calculated. The important part thicknesses are selected as design variables through the comprehensive contribution analysis method. The maximum displacement and maximum stress under the dangerous condition are considered as constraints. The mass of the carriage and the minimum fatigue life of the critical welds are considered as optimization objectives. The GA-XGBoost machine learning approximation models (GA-XGBoost-MLAM) and NSGA-II algorithm are employed for multi-objective optimization design of the carriage. The entropy weighted TOPSIS method is utilized for multi-objective decision-making of Pareto solutions. The design after optimization and decision-making shows that, while satisfying the requirements of static structural performance, the minimum fatigue life mileage of the critical welds of the carriage is increased by 157,570 km, representing an increase of 36.58%. Additionally, the mass of the carriage is reduced by 295.69 kg, representing a decrease of 9.47%. Therefore, the proposed design method achieves a good effect in the anti-fatigue lightweight of dump truck carriage.

Multi-scale closed-loop coupled real-time water quantity optimization scheduling of cascade pumping station in water supply canal systems

Closed-loop control is an effective control method to deal with uncertain disturbance in water supply canal system (WSCS). However, previous researches mainly focus on closed-loop control with a single time scale, and it is difficult to solve the uncertainty problem with a long time dimension. In this paper, a multi-scale closed-loop coupled real-time scheduling framework is established and solved by considering both large and small scale closed-loop scheduling models, so as to improve the performance of real-time scheduling in a longer time dimension under uncertain perturbations. The framework is applied to the virtual simulation test of the Xuhong River WSCS of the east route of the South-to-North Water Diversion project under different water distribution disturbances. The results show that the closed-loop control method can update the system status regularly through rolling optimization, and effectively reduce the prediction error of water level and energy consumption. In the two water distribution disturbance scenarios, the proposed method reduces the average relative deviation of water supply plan execution by 3.4 % and 5.9 %, and the unit energy consumption of cascade pumping station operation by 1.7 kWh/104m3 and 1.5 kWh/104m3, respectively, compared with the single-scale closed-loop scheduling method. In addition, the proposed method can effectively deal with the uncertainty of water distribution disturbance in limited computing time, and can meet the requirements of real-time scheduling in a long time dimension under uncertain disturbance.

Optimising Learning Outcomes: A Comprehensive Approach to Virtual Simulation Experiment Teaching in Higher Education

The development of online virtual simulation test teaching programs has recently been in increasing demand due to the lack of traditional test teaching resources in colleges. This research aims to optimize the delivery of experimental teaching in higher education by harnessing the potential of virtual simulation technology. However, several challenges and limitations must be addressed to attain optimal learning outcomes. Data collection methods encompassed questionnaire surveys, multi-subject interviews, and regression analysis to examine underlying issues and trends in this teaching approach. This study underscores three vital dimensions for enhancing virtual simulation experiment teaching in colleges. Firstly, the integration of online and offline teaching modes is essential to broaden the spectrum of test-teaching scenarios. Secondly, technological advancements are pivotal in bolstering the reliability and authenticity of virtual examinations. The incorporation of virtual reality and artificial intelligence significantly enhances the realism and accuracy of virtual experiments, making them more effective in simulating real-life scenarios. Lastly, adapting the teaching model to accommodate diverse levels and facets is crucial for optimizing student learning experiences and outcomes. Tailoring teaching strategies and methods to individual student needs fosters a more personalized learning experience. Additionally, the paper highlights the significance of evaluating the effectiveness of virtual simulation experiment teaching through various assessment methods, including formative and summative assessments. This evaluation process enables teachers to identify areas for improvement and refine teaching practices to achieve better learning outcomes for students. As a result, this research provides valuable insights into the potential of virtual simulation technology in higher education and offers a roadmap for its successful implementation.

Investigation of forming process and deformation mechanisms of 3D warp interlock fabric

This article introduces a modeling method aimed at predicting the internal geometric structure of 3D warp interlock fabric. Inspired by the digital element method, the method employs truss elements to model the yarns as fiber bundles, and considers their material properties. This technique allows for predicting the microstructure of 3D woven fabric based on simple input characteristics. By varying the elastic modulus of virtual fibers, the friction coefficient between fibers, and the tension load at the ends of yarns, and designing compression molding molds to compress the fabric into shape, the virtual fiber fabric predicted the influence of variables on the fabric. The study found that the structure of the samples obtained through CT scanning is generally consistent with the structure of the virtual fiber fabric. The elastic modulus has a relatively minor impact on the fabric structure, whereas the friction coefficient and tension load significantly affect the cross-sectional shape of the yarns. During compression molding, deformation within the mold leads to interlayer fabric slippage and changes in the cross-sectional shape of the fabric. This suggests the feasibility and applicability of virtual simulation testing methods for analyzing the behavior of various types of 3D woven fabrics.

Optimal Design and Dynamic Characteristic Analysis of Double-Link Trapezoidal Suspension for 3WPYZ High Gap Self-Propelled Sprayer

A fast spraying speed, wide working area, and easy operation are the operational advantages of high-clearance boom sprayers. To address the issue of spray boom mechanical vibration affecting the spraying effect, a double-link trapezoidal boom suspension is designed for the 3WPYZ sprayer. This suspension can achieve passive vibration reduction, active balance, and ground profiling. The kinematic model of the boom suspension is established based on D’Alembert’s principle and the principle of multi-body dynamics, and the design factors affecting the stability of the boom are determined. Through orthogonal experimental design and virtual kinematics simulation, the influence of the boom length and orifice diameter of each part on the swing angle and the natural frequency of the boom suspension is investigated. Design-Expert 8.0.6 software is used to analyze and optimize the test results. The optimization results show that, when the connecting boom length LAB is 265 mm, the inner boom suspension boom length LAD is 840 mm, the outer boom suspension boom length LBC is 1250 mm, and the throttle hole diameter d is 4 mm; the maximum swing angle of the boom suspension is reduced by 53.02%. In addition, the natural frequency of the boom is reduced from 1.3143 rad/s to 1.1826 rad/s, and the dynamic characteristic optimization effect is remarkable. The modal analysis results show that the first sixth-order vibration test frequency of the boom sprayer suspension designed in this paper meets the requirements and avoids the influence of external factors. Field tests show that, when the sprayer is excited by the environment at 3.5° to 4°, the boom suspension can reduce the vibration transmitted by the body to a reasonable range. The static analysis shows that the structural design of this study reduces the stress at the connection of the end boom suspension, the maximum displacement, and the maximum stress of the inner boom suspension. The test results of the dynamic characteristics of the implement are basically consistent with the virtual model simulation test results, thus achieving the optimization objectives.

A dynamic-static combination risk analysis framework for berthing/unberthing operations of maritime autonomous surface ships considering temporal correlation

The autonomy of the ship is bound to bring new risk challenges to its berthing/unberthing operations process. Potential damages resulting from these risks can range from minor hull deformations to substantial losses of assets and environmental harm. To address these risks in the process of berthing/unberthing operations of MASSs or mitigate them to an acceptable level, this paper aims to propose a risk analysis framework combining dynamic and static Bayesian network models, and which can consider the temporal correlation of the values of risk factors, to help ships avoid the occurrence of operational accidents. In this framework, a set of probabilistic model is included to calculate the real-time risk value of autonomous berthing operation, so as to analyze and solve the time-dependent problem of risk change with time during berthing/unberthing of autonomous ships. In addition, the developed model can determine the key influencing factors and possible key situations during the berthing/unberthing of autonomous ships, which can support the risk prevention and mitigation measures in the process of autonomous ship berthing/unberthing. Finally, the applicability of the proposed risk analysis framework is analyzed through a virtual simulation test case of autonomous berthing/unberthing.

Calibration of Simulation Parameters for Fresh Tea Leaves Based on the Discrete Element Method

To address the problem of a lack of accurate parameters in the discrete element simulation study of the machine-picked fresh tea leaf mechanized-sorting process, this study used machine-picked fresh tea leaves as the research object, established discrete element models of different fresh tea leaf components in EDEM software version 7.0.0. based on the bonded particle model using three-dimensional scanning inverse-modeling technology, and calibrated the simulation parameters through physical tests and virtual simulation tests. Firstly, the intrinsic parameters of machine-picked tea leaves were measured using physical tests; the physical-stacking tea leaf test was conducted using the cylinder lifting method, the tea leaf repose angle being 32.62° as measured from the stacking images using CAD. With the physical repose angle as the target value, the Plackeet–Burman test, the steepest-ascent test and the Box–Behnken optimization test were conducted in turn, and the results showed that the static friction coefficient between tea leaves, the rolling friction coefficient between tea leaves and the static friction coefficient between tea leaves and PVC have a major effect on the repose angle, and the optimal combination of the three significant parameters was determined. Finally, five simulations were conducted using the optimal combination of parameters, the relative error between the repose angle measured by the simulation test and the physical repose angle being just 0.28%. Moreover, the t-test obtained p > 0.05, indicating that there was no significant difference between the simulation test results and the physical test results. The results showed that the calibrated discrete element simulation parameters obtained could provide a reference for the discrete element simulation study of fresh tea leaves.

Aircraft Upset Recovery Strategy and Pilot Assistance System Based on Reinforcement Learning

The upset state is an unexpected flight state, which is characterized by an unintentional deviation from normal operating parameters. It is difficult for the pilot to recover the aircraft from the upset state accurately and quickly. In this paper, an upset recovery strategy and pilot assistance system (PAS) based on reinforcement learning is proposed. The man–machine closed-loop system was established and the upset state, such as a high angle of attack and large attitude angle, was induced. The upset recovery problem was transformed into a sequential decision problem, and the Markov decision model of upset recovery was established by taking the deflection change of the control surface as the action. The proximal policy optimization (PPO) algorithm was selected for the strategy training. The adaptive pilot model and the reinforcement learning method proposed in this paper were used to make the aircraft recover from the upset state. Based on the correspondence between the flight state, the recovery method, and the recovery result, the aircraft upset recovery safety envelopes were formed, and the four-level upset recovery PAS with alarm warning, coordinated control, and autonomous recovery modes was constructed. The results of the digital virtual flight simulation and ground flight test show that compared with a traditional single pilot, the aircraft upset recovery strategy, the upset recovery safety envelopes, and the PAS established in this study could reduce the handling burden of the pilot and improve the success rate and effect of upset recovery. This research has certain theoretical reference values for flight safety and pilot training.

Study on Temperature Field in Common-rail Diesel Engine Combustion Chamber at Different Altitudes

The plateau covers 26% of China’s land area, the higher altitude, the lower atmospheric pressure, the smaller air density, the lower average temperature. Therefore diesel engine power decreases significantly, fuel consumption increases, starting performance deteriorates, and soot emissions increase. In this paper, virtual simulation and simulation test methods are used to study the temperature field change in the combustion chamber of CA6DL2-35E3R common-rail diesel engine at different altitudes and atmospheric pressures. The results show that the altitude has a great influence on the temperature field in the combustion chamber. The maximum combustion temperature gradually increases with increase of altitude, post-combustion is serious and the exhaust temperature increases.

Autonomous Vehicles Testing Considering Utility-Based Operable Tasks

Virtual simulation testing of Autonomous Vehicles (AVs) is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs. Mainstream methods focus on improving testing efficiency by extracting critical scenarios from naturalistic driving datasets. However, the criticalities defined in their testing tasks are based on fixed assumptions, the obtained scenarios cannot pose a challenge to AVs with different strategies. To fill this gap, we propose an intelligent testing method based on operable testing tasks. We found that the driving behavior of Surrounding Vehicles (SVs) has a critical impact on AV, which can be used to adjust the testing task difficulty to find more challenging scenarios. To model different driving behaviors, we utilize behavioral utility functions with binary driving strategies. Further, we construct a vehicle interaction model, based on which we theoretically analyze the impact of changing the driving behaviors on the testing task difficulty. Finally, by adjusting SV's strategies, we can generate more corner cases when testing different AVs in a finite number of simulations.

An adaptive on-board real-time model with residual online learning for gas turbine engines using adaptive memory online sequential extreme learning machine

The on-board real-time model (ORM) of gas turbine engines (GTEs) is widely used in various applications of control systems, such as sensor fault-tolerant control and model-based control. However, the accuracy of the ORM in the primary stage is affected by changes in engine performance, including degradation and sudden changes, which inevitably occur during engine service. To address this issue, we propose an adaptive on-board real-time model (AORM) modelling framework for GTEs based on a component-level model (CLM) and a residual online learning model (ROLM). The CLM is constructed using classical component-level modelling theory, while the ROLM is built using the proposed adaptive memory online sequence extreme learning machine (AMOSELM) algorithm. The AMOSELM algorithm can effectively learn the residuals between the engine and the CLM during engine service, improving the accuracy of the model. Specifically, we compensate for the CLM by using the predicted residuals based on the AMOSELM to achieve enhanced accuracy in the AORM. The memory coefficient of the AMOSELM is adjusted using a bell-type membership function in the online learning stage. The shape parameters of the membership function are updated using an off-line optimization strategy based on particle swarm optimization when the online learning is finished. To validate the effectiveness of the AORM framework and the AMOSELM algorithm, we conducted both virtual engine flight data simulation and actual ground test verification. The verification results show that the AMOSELM algorithm can effectively realize residual online learning during engine service, and performs better than other traditional online learning algorithms. Moreover, using the predicted residuals to compensate for the CLM can significantly reduce the modelling error of the AORM, achieving better modelling accuracy.

Parameter optimization of column-comb harvesting of litchi based on the EDEM

Litchi is one of the most important commercial subtropical fruit with high nutritional and medicinal value widely planted in South China.With the problems of time-consuming, laborious and low mechanization in litchi harvesting continues to increase, growers need highly effective way to harvest litchi. To solve the problem mentioned above, this paper presented a flexible harvesting column-comb litchi harvester, a flexible column-comb device with an adjustable column-comb angle harvesting method and a collection device using a lifting and lowering adjustable folding collection method was proposed, which can complete the harvesting and collection of litchi fruits in the orchard at one time. A litchi harvester model was established, and a virtual simulation test was carried out in EDEM software by taking the distance of the column-comb rods d (A), the column-comb rod length l (B) and the column-comb axle angle amplitude a (C) as the test factors and the maximum compression force F of the litchi as the evaluation index. Through optimization analysis of the response surface, the optimal combination of parameters affecting the working performance of the litchi harvester was obtained. The simulation results showed that the factors d, l, and a affected the evaluation index F during the harvesting process; the optimal parameter combination after optimization was d = 66.69 mm, l = 673.80 mm, and a = 69.84, yielding F = 64.77 N, which was within the limit load of litchi damage. Under the optimal parameter combination conditions, the litchi harvesting test was conducted in a litchi orchard in Guangdong, China, the results of the orchard test showed that the breakage rate of the optimized column-comb harvester was 6.10%, the efficiency of mechanical harvesting was 2.2 times that of manual harvesting, which was basically meeting mechanized litchi harvesting requirements. To a certain extent, it was confirmed that the established EDEM model and the virtual test analysis were accurate and effective, providing a reference for the design and research of mechanized litchi harvesting equipment.

Batch human-like trajectory generation for multi-motion-state NPC-vehicles in autonomous driving virtual simulation testing

Non-player character vehicles (NPC-Vs) denote crucial components of autonomous driving systems (ADSs) and autonomous driving assistance algorithms (ADAAs) when conducting virtual simulation testing (VST). Human-like behaviors and trajectories of NPC-Vs could provide information on complex background traffic flow to the tested ADS and ADAA, thus ensuring rigorous tests on the reliability and stability of the ADS and ADAA. However, a VST based on data injection faces the problems of a small amount of data and difficulty in extracting critical scenarios and methods’ transplantation and reuse. To address these problems, this study takes intersection as a research scenario and proposes a probability-limited parameter combination method and a learning-based batch human-like trajectory generation model to generate different human-like trajectories according to different motion states of vehicles. First, an effective IM-sampling algorithm, which samples trajectory data and obtains equal-number-coordinate trajectories as trajectory generation model labels, is proposed. Second, dependency probabilities between different vehicle kinematic parameters (VKPs) are calculated to form a probability-limited generation tree, which generates different VKP combinations representing different vehicle motion states that are used as trajectory generation model inputs. Finally, a learning-based batch trajectory generation model is developed. After model training and testing, the generated VKP combinations are used for trajectory generation, and the generated trajectories are subjected to the human-like degree analysis considering multiple metrics. The experimental results show that the proposed model is capable of generating more complex human-like trajectories and behaviors than real trajectories in batch. The proposed model could be used to generate complex and human-like NPC-V trajectories for the autonomous driving VST and thus accelerate the autonomous driving VST.

Optimal Design and Analysis of Cavitating Law for Well-Cellar Cavitating Mechanism Based on MBD-DEM Bidirectional Coupling Model

A variable velocity parallel four-bar cavitating mechanism for well-cellar can form the well-cellar cavitation which suits for well-cellar transplanting under a continuous operation. In order to improve the cavitating quality, this paper analyzed the structural composition and working principle of the cavitating mechanism and established the bidirectional coupling model of multi-body dynamics and the discrete element between the cavitating mechanism and soil through Recurdyn and EDEM software. Based on the model, a three-factor, five-level quadratic orthogonal rotational combination design test was conducted with the parameters of the cavitating mechanism as the experimental factors and the parameters of the cavitation as the response index to obtain the optimal parameter combination, and a virtual simulation test was conducted for the optimal parameter combination in order to study the cavitating law of the cavitating mechanism and soil. The test results showed that the depth of the cavitation was 188.6 mm, the vertical angle of the cavitation was 90.4°, the maximum diameter of the cavitation was 76.1 mm, the minimum diameter of the cavitation was 68.5 mm, and the variance in the diameters for the cavitation was 5.42 mm2. The cavitating mechanism with optimal parameters based on the Recurdyn–EDEM bidirectional coupling mode could further improve the cavitating quality.

Integrated Analysis System for Dynamic Sealing Performance of Civil Aircraft Boarding Door

This paper introduces an integrated analysis system for the dynamic sealing performance of civil aircraft boarding doors. The analysis system mainly carries out virtual verification for the sealing performance of civil aircraft doors in various service environments, including the simulation module of fluid pressure temperature on the surface of aircraft doors. There are three subsystems: hatch fluid-structure coupling interpolation module and hatch dynamic sealing performance finite element simulation module. The system is used to simulate the sealing performance of civil aircraft boarding doors in complex dynamic environments such as under high and low temperatures and high and low pressures. The system can improve the evaluation mechanism of aircraft hatch dynamic sealing performance, and help to build a virtual simulation analysis and test verification system of hatch performance under the interaction of multiple environmental factors, so as to help overcome the bottleneck in the development of civil aircraft in China.

Artificial intelligence in the digital twins: State of the art, challenges, and future research topics

Advanced computer technologies such as big data, Artificial Intelligence (AI), cloud computing, digital twins, and edge computing have been applied in various fields as digitalization has progressed. To study the status of the application of digital twins in the combination with AI, this paper classifies the applications and prospects of AI in digital twins by studying the research results of the current published literature. We discuss the application status of digital twins in the four areas of aerospace, intelligent manufacturing in production workshops, unmanned vehicles, and smart city transportation, and we review the current challenges and topics that need to be looked forward to in the future. It was found that the integration of digital twins and AI has significant effects in aerospace flight detection simulation, failure warning, aircraft assembly, and even unmanned flight. In the virtual simulation test of automobile autonomous driving, it can save 80% of the time and cost, and the same road conditions reduce the parameter scale of the actual vehicle dynamics model and greatly improve the test accuracy. In the intelligent manufacturing of production workshops, the establishment of a virtual workplace environment can provide timely fault warning, extend the service life of the equipment, and ensure the overall workshop operational safety. In smart city traffic, the real road environment is simulated, and traffic accidents are restored, so that the traffic situation is clear and efficient, and urban traffic management can be carried out quickly and accurately. Finally, we looked forward to the future of digital twins and AI, hoping to provide a reference for future research in related fields.

Design and simulation for seeding performance of high-speed inclined corn metering device based on discrete element method (DEM)

Mechanical precision corn seed-metering planter has a compact structure, missed and repeated seeding advantages during high-speed operation. In this regard, the current research study focuses on the development of a corn seed planter that features an inclined seed-metering device. The spatial layout of the seed-metering device is optimized to change the seed-filling mode to meet the needs of high-speed operation. Firstly, the mechanical characteristics and seeds in the metering device chamber were analyzed, and then the seed-filling stress model was established. Secondly, a mechanical model for corn seed particles was developed for virtual simulation tests and numerical analysis using the discrete element method (DEM) and EDEM software. Moreover, a quadratic rotating orthogonal center combination test was implemented by setting the inclination angle of seed-metering device θ(A), machine ground speed v(B), and rotation speed of metering disc n(C) as the influence factors, with the missed seeding rate M and the seed-filling stress S as the evaluation indices. The results indicated that the most significant factors affecting the missed seeding rate, seed-filling stress, S, were the rotation speed of the metering disc (n) > machine ground speed (v) > inclination angle of the metering disc (θ) and inclination angle of the metering device (θ) > rotation speed of the metering disc (n) > machine ground speed (v), respectively. However, the field verification test shows that the optimized corn seed-metering planter achieved mean values of M = 4.33, Q(qualified seeding rate) = 92.83%, and R(repeated seeding rate) = 2.84%, with average relative errors of 1.17% compared to the simulation tests and the accuracy and effectiveness of the DEM simulation model was verified. Therefore, the developed corn seed-metering device meets the industry standards and operation requirements for precise corn sowing, and technical support can be given for future studies of similar precision seeding equipment.