通讯机构：
[Yang, L ] W;Wuhan Polytech Univ, Coll Mech Engn, Wuhan 430048, Hubei, Peoples R China.
关键词：
Vertical rice mill;Interaction mechanism;Rice;Discrete element method
摘要：
The external milling vertical rice mill (EMVRM) is one of the new rice milling process equipment. The grains moving and interaction mechanism are basic points for milling, rice blade-sand bar spacing H, sieve structure are important structural parameters need considering. The EMVRM 3D model and DEM contact working model are established. The rice blade spacing H and sieve structure effect on grains motion and interaction in is analyzed. Grains motion velocity and density in milling chamber gradually reduced along axial flow direction. The milling chamber grains average motion velocity difference decreases with spacing H increasing. The grain normal and tangential force show down parabolic relation with axial distance from inlet. The sieve direction has little effect on grain motion velocity and force. Normal and tangential force in milling chamber reduced downward along the axial. The research provides practical guidance for EMVRM mill design.
The external milling vertical rice mill (EMVRM) is one of the new rice milling process equipment. The grains moving and interaction mechanism are basic points for milling, rice blade-sand bar spacing H, sieve structure are important structural parameters need considering. The EMVRM 3D model and DEM contact working model are established. The rice blade spacing H and sieve structure effect on grains motion and interaction in is analyzed. Grains motion velocity and density in milling chamber gradually reduced along axial flow direction. The milling chamber grains average motion velocity difference decreases with spacing H increasing. The grain normal and tangential force show down parabolic relation with axial distance from inlet. The sieve direction has little effect on grain motion velocity and force. Normal and tangential force in milling chamber reduced downward along the axial. The research provides practical guidance for EMVRM mill design.
通讯机构：
[Li, JK ] W;Wuhan Polytech Univ, Sch Mech Engn, Wuhan 430023, Peoples R China.
关键词：
Rice grain pile;Heat and humidity transfer;Simulation test;Simulated granary;Water migration
摘要：
This study addresses the challenge of developing effective strategies to prevent and control the deterioration of stored grain quality caused by biological activities and heating. The unclear mechanisms of water migration and humidity distribution within rice grain piles subjected to localized high temperatures complicate this effort. Using COMSOL simulation and model granaries, the study examines temperature fields, humidity fields, and water migration in high temperature and humidity areas. Results show that within 24–48 h of high-humidity rice exposure to high temperatures, the surrounding grain's temperature rises rapidly, with relative humidity increasing significantly within 48–72 h. The temperature peaks at around 96 h. The influence of high-humidity grain on its surroundings is minimal within the first 36 h, but as temperature increases, the relative humidity of the surrounding grain pile rises faster. The micro-airflow caused by temperature differences drives moist air to migrate to cooler areas, leading to a rise in moisture content in these regions. Therefore, interventions like ventilation and grain turnover should be implemented within 24 h of detecting high-temperature grain, with continuous monitoring of moisture content in adjacent low-temperature areas to prevent further deterioration.
This study addresses the challenge of developing effective strategies to prevent and control the deterioration of stored grain quality caused by biological activities and heating. The unclear mechanisms of water migration and humidity distribution within rice grain piles subjected to localized high temperatures complicate this effort. Using COMSOL simulation and model granaries, the study examines temperature fields, humidity fields, and water migration in high temperature and humidity areas. Results show that within 24–48 h of high-humidity rice exposure to high temperatures, the surrounding grain's temperature rises rapidly, with relative humidity increasing significantly within 48–72 h. The temperature peaks at around 96 h. The influence of high-humidity grain on its surroundings is minimal within the first 36 h, but as temperature increases, the relative humidity of the surrounding grain pile rises faster. The micro-airflow caused by temperature differences drives moist air to migrate to cooler areas, leading to a rise in moisture content in these regions. Therefore, interventions like ventilation and grain turnover should be implemented within 24 h of detecting high-temperature grain, with continuous monitoring of moisture content in adjacent low-temperature areas to prevent further deterioration.
期刊：
Results in Engineering,2025年27:106131 ISSN：2590-1230
通讯作者：
Youjun He
作者机构：
[Yin, Qiang; He, Youjun; Liu, Shuai; Liu, Xiaopeng; Zhang, Yonglin; Song, Shaoyun; Li, Hui] School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan 430023, China
通讯机构：
[Youjun He] S;School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan 430023, China
关键词：
paddy grain pile;Heat and moisture coupling;COMSOL;Mechanical Ventilation
摘要：
Temperature and humidity within grain bulks are two critical factors affecting the safe storage duration of grains. Investigating the heat and moisture transfer mechanisms during storage, along with the coupled interactions among various in-silo factors, enables better prediction of grain storage conditions and supports high-quality grain preservation. In this study, a paddy grain bulk exhibiting humidity stratification inside a grain silo is selected as the research subjet. Based on multi-physics coupling theory, a comprehensive study is conducted on the heat and moisture transfer processes in the paddy grain bulk. A coupled heat and moisture transfer model is established under mechanical ventilation conditions according to the principles of mass, energy, and momentum conservation. Experimental validation is conducted using a custom-built grain silo test chamber, in which temperature and humidity data are collected during ventilation and compared with simulation results to assess model accuracy. The results show that the maximum temperature deviation between simulated and measured values at various monitoring points is 0.94 °C, while the relative humidity error remains within 2%, demonstrating the model’s high accuracy and adaptability. Building upon existing heat and moisture modeling efforts, this study further deepens the coupling mechanisms and enables dynamic simulation and quantitative analysis of humidity stratification within the grain bulk. These findings provide theoretical support for understanding the mechanisms of humidity stratification and optimizing ventilation-drying strategies.
Temperature and humidity within grain bulks are two critical factors affecting the safe storage duration of grains. Investigating the heat and moisture transfer mechanisms during storage, along with the coupled interactions among various in-silo factors, enables better prediction of grain storage conditions and supports high-quality grain preservation. In this study, a paddy grain bulk exhibiting humidity stratification inside a grain silo is selected as the research subjet. Based on multi-physics coupling theory, a comprehensive study is conducted on the heat and moisture transfer processes in the paddy grain bulk. A coupled heat and moisture transfer model is established under mechanical ventilation conditions according to the principles of mass, energy, and momentum conservation. Experimental validation is conducted using a custom-built grain silo test chamber, in which temperature and humidity data are collected during ventilation and compared with simulation results to assess model accuracy. The results show that the maximum temperature deviation between simulated and measured values at various monitoring points is 0.94 °C, while the relative humidity error remains within 2%, demonstrating the model’s high accuracy and adaptability. Building upon existing heat and moisture modeling efforts, this study further deepens the coupling mechanisms and enables dynamic simulation and quantitative analysis of humidity stratification within the grain bulk. These findings provide theoretical support for understanding the mechanisms of humidity stratification and optimizing ventilation-drying strategies.
摘要：
Purpose In order to replace the traditional grain condition measurement and control system to complete the grain condition detection operation inside the grain pile, design a robot that can drill into the inside of the grain pile and realize the grain condition detection operation inside the pile.
In order to replace the traditional grain condition measurement and control system to complete the grain condition detection operation inside the grain pile, design a robot that can drill into the inside of the grain pile and realize the grain condition detection operation inside the pile.
Methods According to the actual operation scenario, the overall structure and drive form of the grain detection robot are determined, and the torque balance equation of the spiral drive wheel in the grain pile is established based on the principle of ground science. Optimize the structural parameters of the spiral drive wheel using RecurDyn-EDEM coupled simulation, take the lift angle, height, number of spiral blades and the taper angle of the outer contour of the spiral drive wheel as the optimization target parameters, and take the speed, axial resistance magnitude, torsional resistance moment and propulsive efficiency as the evaluation indexes, and analyze the influence of different parameters on the motion performance of the spiral drive wheel. A test platform is built to test the torque and displacement of the helical drive wheel, and the difference between the simulated and experimental values under the same experimental conditions is compared to prove the high reliability of the simulation model, and to test the kinematic performance of the food situation detection robot.
According to the actual operation scenario, the overall structure and drive form of the grain detection robot are determined, and the torque balance equation of the spiral drive wheel in the grain pile is established based on the principle of ground science. Optimize the structural parameters of the spiral drive wheel using RecurDyn-EDEM coupled simulation, take the lift angle, height, number of spiral blades and the taper angle of the outer contour of the spiral drive wheel as the optimization target parameters, and take the speed, axial resistance magnitude, torsional resistance moment and propulsive efficiency as the evaluation indexes, and analyze the influence of different parameters on the motion performance of the spiral drive wheel. A test platform is built to test the torque and displacement of the helical drive wheel, and the difference between the simulated and experimental values under the same experimental conditions is compared to prove the high reliability of the simulation model, and to test the kinematic performance of the food situation detection robot.
RESULTS The simulation results show that the kinematic performance of the helical drive wheel is best when the helical lift angle of the helical drive wheel is 35°, the maximum height of the helical blades is 35mm, the taper angle of the outer contour of the helical drive wheel is 20°, and the number of the helical blades is 4 pieces. The kinematic performance test experiment shows that when the rotational speed of the spiral driving wheel is 30r/min, the prototype dives to about 460mm below the grain surface after 32s, with an average dive speed of about 14mm/s and an average slip rate of 78.21%.
The simulation results show that the kinematic performance of the helical drive wheel is best when the helical lift angle of the helical drive wheel is 35°, the maximum height of the helical blades is 35mm, the taper angle of the outer contour of the helical drive wheel is 20°, and the number of the helical blades is 4 pieces. The kinematic performance test experiment shows that when the rotational speed of the spiral driving wheel is 30r/min, the prototype dives to about 460mm below the grain surface after 32s, with an average dive speed of about 14mm/s and an average slip rate of 78.21%.
CONCLUSION The comparison experiment proves the high reliability of the simulation model. The motion performance test experiment shows that the grain detection robot can drill into the internal operation of the grain pile, and subsequently can be equipped with a variety of sensors to complete such operations as temperature and humidity detection, cuttings, turning grain and so on.
The comparison experiment proves the high reliability of the simulation model. The motion performance test experiment shows that the grain detection robot can drill into the internal operation of the grain pile, and subsequently can be equipped with a variety of sensors to complete such operations as temperature and humidity detection, cuttings, turning grain and so on.
摘要：
Olive oil is widely used for its easy availability, biodegradability, and environmental friendliness. It is a good potential candidate for green lubrication performance. However, lack of research on the tribological behavior of sliding contact under high-temperature conditions limits its use in high-temperature lubrication. To gain a deeper comprehension of how olive oil lubricates under high-temperature settings, an equipped friction testing apparatus with temperature regulation capabilities was utilized for a thorough investigation. Testing results show that high-temperature olive oil lubrication can be divided into four stages: start-up lubrication stage, steady lubrication stage, transition lubrication stage, and lubrication failure stage (dry friction). The material wear mechanisms under olive oil lubrication at high temperatures mainly include abrasive wear, adhesive wear, fatigue wear, oxidation wear. The effects of olive oil under high-temperature conditions, including load, speed, and temperature, were further discussed.Its reveals the lubrication mechanism of olive oil under high-temperature sliding contact conditions.
Olive oil is widely used for its easy availability, biodegradability, and environmental friendliness. It is a good potential candidate for green lubrication performance. However, lack of research on the tribological behavior of sliding contact under high-temperature conditions limits its use in high-temperature lubrication. To gain a deeper comprehension of how olive oil lubricates under high-temperature settings, an equipped friction testing apparatus with temperature regulation capabilities was utilized for a thorough investigation. Testing results show that high-temperature olive oil lubrication can be divided into four stages: start-up lubrication stage, steady lubrication stage, transition lubrication stage, and lubrication failure stage (dry friction). The material wear mechanisms under olive oil lubrication at high temperatures mainly include abrasive wear, adhesive wear, fatigue wear, oxidation wear. The effects of olive oil under high-temperature conditions, including load, speed, and temperature, were further discussed.Its reveals the lubrication mechanism of olive oil under high-temperature sliding contact conditions.
摘要：
Grain is the root of the people, and grain storage is a top priority. It is necessary to improve the efficiency of grain storage operations and reduce work intensity, so it is important to develop an automated or even intelligent grain leveling robot for the precise operation of grain silos. In this paper, we propose an area classification method based on target leveling height for the special working mode of truss-type grain leveling robot, simplify the 3D map to a 2D map, reduce the difficulty of path planning, and improve the working efficiency. Based on multi-level path planning and genetic algorithm to achieve the planning of the working path of the grain-leveling robot, it solves the problem of a large number of useless trips under the adoption of full-coverage operation. It saves a lot of time, improves grain-leveling efficiency, reduces energy consumption, optimizes the effect of grain-leveling, and helps realize the precise storage operation of grain silos. The experiment of rough leveling operation was carried out through the grain leveling robot prototype and the simulated experimental warehouse, and the results show that the height difference between the peak of the grain surface and the target leveling height is within 5 cm, which verifies that the path planning method in this paper is feasible, and shows that the grain leveling robot can complete the task of grain leveling and the effect is good.
通讯机构：
[Li, JK ] W;Wuhan Polytech Univ, Sch Mech Engn, Wuhan 430023, Peoples R China.
关键词：
Multi-source information fusion;Grain situation analysis;Rice pile characteristics;Space-time law;Evaluation model
摘要：
Grain storage is a complex process, affected by factors such as mold, temperature, humidity, and moisture. The use of multiple sensors to detect changes in rice pile parameters has gained prominence as a means to ensure the accuracy and timeliness of grain condition monitoring. However, the current technology does not effectively utilize data. The assessment criteria primarily rely on grain temperature, and the analysis of grain condition is simplistic. Additionally, it fails to adequately integrate information on temperature, humidity, moisture, gas concentration, and other parameters of the grain pile to form a unified assessment result. To address the isolated and one-sided reaction of various parameters in the grain pile, this thesis conducts research on the storage characteristics of heating, condensation, and mold condition. It combines the information fusion of temperature, humidity, moisture, and CO2 with normal grain conditions, constructs an assessment model based on the classification and identification of grain conditions under gray correlation, and achieves real-time dynamic assessment of the state of the grain pile. The experimental results show that the assessment model based on gray correlation can accurately discriminate between normal and mold conditions, but the accuracy in distinguishing heating and condensation still requires improvement. The overall recognition rate of the four types of grain conditions is 79%, which demonstrates the effectiveness of the model in identifying abnormal grain states.
Grain storage is a complex process, affected by factors such as mold, temperature, humidity, and moisture. The use of multiple sensors to detect changes in rice pile parameters has gained prominence as a means to ensure the accuracy and timeliness of grain condition monitoring. However, the current technology does not effectively utilize data. The assessment criteria primarily rely on grain temperature, and the analysis of grain condition is simplistic. Additionally, it fails to adequately integrate information on temperature, humidity, moisture, gas concentration, and other parameters of the grain pile to form a unified assessment result. To address the isolated and one-sided reaction of various parameters in the grain pile, this thesis conducts research on the storage characteristics of heating, condensation, and mold condition. It combines the information fusion of temperature, humidity, moisture, and CO2 with normal grain conditions, constructs an assessment model based on the classification and identification of grain conditions under gray correlation, and achieves real-time dynamic assessment of the state of the grain pile. The experimental results show that the assessment model based on gray correlation can accurately discriminate between normal and mold conditions, but the accuracy in distinguishing heating and condensation still requires improvement. The overall recognition rate of the four types of grain conditions is 79%, which demonstrates the effectiveness of the model in identifying abnormal grain states.
摘要：
Castor oil has been widely used in various fields due to its properties, leading to large attention for its extraction mechanism. To research the castor oil extraction mechanism during pressing, a self-developed uniaxial compression device combined with an in situ observation is established. The effects of pressure, loading speed, and creep time are investigated, and a finite element model coupling with multi-physics is established for castor oil pressing extraction, verified by the seed cake experimental compression strain matching with numerical simulation under the same condition. Simulation results indicated that the pressing oil extraction process can be divided into two stages, Darcy's speed shows the first sharp decreasing stage and the second gradual increasing stage during porosity and pressure interaction. In the first stage, porosity is dominant on Darcy's speed. With porosity decreasing, the pressure effect on Darcy's speed exceeds porosity in the second stage. With seed thickness increasing, Darcy's speed first increases and then decreases. With loading speed increasing, Darcy's speed increases. Darcy's speed decreases constantly with creep time increasing. This study can provide basic theoretical and practical guidance for oil extraction.
通讯机构：
[Yang, L ] W;Wuhan Polytech Univ, Coll Mech Engn, Wuhan 430048, Hubei, Peoples R China.
关键词：
brown rice;DOM prediction;features extraction;machine learning;SHAP interpretation
摘要：
Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moderate processing. In this study, a self-established grain image acquisition platform was built. Degree of bran layer remaining (DOR) datasets is established with image capturing and processing (grain color, texture, and shape features extraction). The mapping relationship between DOR and the DOM is in-depth analyzed. Rice grain DOR typical machine learning and deep learning prediction models are established. The results indicate that the optimized Catboost model can be established with cross-validation and grid search method, with the best accuracy improving from 84.28% to 91.24%, achieving precision 91.31%, recall 90.89%, and F1-score 91.07%. Shapley additive explanations analysis indicates that color, texture, and shape feature affect Catboost prediction accuracy, the feature importance: color>texture>shape. The YCbCr-Cb_ske and GLCM-Contrast features make the most significant contribution to rice milling quality prediction. The feature importance provides theoretical and practical guidancefor grain DOM prediction model. PRACTICAL APPLICATION: Rice milling degree prediction and detection are valuable for rice milling process in practical application. In this paper, image processing and machine learning methods provide an automated, nondestructive, and cost-effective way to predict the quality of rice. The study may serve as a valuable reference for improving rice milling methods, retaining rice nutrition, and reducing broken rice yield.
通讯机构：
[Yang, L ] W;Wuhan Polytech Univ, Coll Mech Engn, Wuhan 430048, Hubei, Peoples R China.
关键词：
Brown rice;Bran layer;Micro-structure;Moderate processing
摘要：
Moderate processing can improve the edible rice grain quality and nutrition. The grain structural-removal behavior was researched based on the macro and micro-structure analysis. Grain bran layer 3D profiling combined with iodine solution staining is applied for geometrical-structural analysis. Results show that rice grain can be divided into five structural region based on bran layer removal capacity, lateral > ventral > ventral groove > dorsal > dorsal groove. The relationship between grain geometric parameters and collision possibility is in-depth analyzed, results indicated large grain surface curvature leads to grain surface layer hard removal capacity. The region's bran layer thickness distribution affects its removal behavior and obey its removal capacity. The regional bran layer thickness order: dorsal(59.56 μm) > dorsal groove(50.62 μm) > ventral(43.91 μm) > ventral groove(42.83 μm) > lateral(37.08 μm). The bran layer micro-morphology impacts removal behavior, dorsal region with the groove structure, the bottom surface layer in grain groove structure can be removed when groove structure is damaged. The ventral groove depth is less than dorsal groove, showing better removal ability. The bran layer color in L*A*B*space shows strong correlation with remaining layer thickness, reflecting grain milling degree. Combined effects of grain geometry-parameters, bran layer thickness and micro-structure lead the final bran layer removal behavior. This study provides theoretical and practical basis for grain moderate processing.
Moderate processing can improve the edible rice grain quality and nutrition. The grain structural-removal behavior was researched based on the macro and micro-structure analysis. Grain bran layer 3D profiling combined with iodine solution staining is applied for geometrical-structural analysis. Results show that rice grain can be divided into five structural region based on bran layer removal capacity, lateral > ventral > ventral groove > dorsal > dorsal groove. The relationship between grain geometric parameters and collision possibility is in-depth analyzed, results indicated large grain surface curvature leads to grain surface layer hard removal capacity. The region's bran layer thickness distribution affects its removal behavior and obey its removal capacity. The regional bran layer thickness order: dorsal(59.56 μm) > dorsal groove(50.62 μm) > ventral(43.91 μm) > ventral groove(42.83 μm) > lateral(37.08 μm). The bran layer micro-morphology impacts removal behavior, dorsal region with the groove structure, the bottom surface layer in grain groove structure can be removed when groove structure is damaged. The ventral groove depth is less than dorsal groove, showing better removal ability. The bran layer color in L*A*B*space shows strong correlation with remaining layer thickness, reflecting grain milling degree. Combined effects of grain geometry-parameters, bran layer thickness and micro-structure lead the final bran layer removal behavior. This study provides theoretical and practical basis for grain moderate processing.
摘要：
During the rice milling process, single and continuous compression occurs between brown rice and the processing parts. When the external load exceeds the yield limit of brown rice, brown rice kernels are damaged; with an increase in compression deformation or the extent of compression, the amount of damage to the kernels expands and accumulates, ultimately leading to the fracture and breakage of kernels. In order to investigate the mechanical compression damage characteristics of brown rice kernels under real-world working conditions, this study constructs an elastic–plastic compression model and a continuous damage model of brown rice kernels based on Hertz theory and continuous damage theory; the accuracy of this model is verified through experiments, and the relevant processing critical parameters are calculated. In this study, three varieties of brown rice kernels are taken as the research object, and mechanical compression tests are carried out using a texture apparatus; finally, the test data are analysed and calculated by combining them with the theoretical model to obtain the relevant critical parameters of damage. The results of the single compression crushing test of brown rice kernels showed that the maximum destructive forces Fc in the single compression of Hunan Early indica 45, Hunan Glutinous 28, and Southern Japonica 518 kernels were 134.77 ± 11.20 N, 115.64 ± 4.35 N, and 115.84 ± 5.89 N, respectively; the maximum crushing deformations αc in the single compression crushing test were 0.51 ± 0.04 mm, 0.43 ± 0.01 mm, and 0.48 ± 0.17 mm, respectively; and the critical average deformations αs of elasticity–plasticity deformation were 0.224 mm, 0.267 mm, and 0.280 mm, respectively. The results of the continuous compression crushing test of brown rice kernels showed that the critical deformations αd of successive compression damage formation were 0.224 mm, 0.267 mm, and 0.280 mm, and the deformation ratios δ of compression damage were 12.24%, 14.35%, and 12.84%. From the test results, it can be seen that the continuous application of compression load does not result in the crushing of kernels if the compression deformation is less than αd during mechanical compression. The continuous application of compressive loads can lead to fragmentation of the kernels if the compressive deformation exceeds αd; the larger the compression variant, the less compression is required for crushing. If the compression deformation exceeds αc, then a single compressive load can directly fragment the kernels. Therefore, the load employed during rice milling should be based on the variety of brown rice used in order to prevent brown rice deformation, which should be less than αd, and the maximum load should not exceed Fc. The results of this study provide a theoretical reference for the structure and parameter optimisation of a rice milling machine.
摘要：
Apples have been constantly damaged in collecting, transporting, and processing, leading research focus on apples' mechanical-structural damage behavior. To research apples' mechanical-structural damage behavior during collision, a dropping collision damage testing device was self-established, with PLC control, data acquisition-processing, in situ high-speed observation. The effect of impact material, drop height, impact orientation on apple deformation and bruise area was investigated with self-established device, considering three typical kinds of apples. The results indicated that apple dropping collision can be divided into two stages: dropping down contact deformation stage and recovering contact deformation stage. Three kinds of apples demonstrate the largest deformation and bruise area when the impact material is steel and acrylic plate. The deformation is similar when apples collide with soil and foam, apples have no bruise area when the impact material is foam. The correlation between apple deformation, bruising area, and drop height was established, reflecting the relationship between drop height and apples' mechanical-structural damage behavior. Yellow Marshal apple deformation is the largest compared to other two kinds of apples under the same collision condition. Red Fuji apple bruise area is the largest compared to other two kinds of apples. The largest bruise area of Yellow Marshal apple and Guoguang apple are in apple transverse, and Red Fuji apple is in apple top. The study can provide basic theoretical and practical guidance for apples postharvest work.
摘要：
Camellia oleifera shell (COS) mechanical-structural property and cracking behavior are crucial for hulling, hulling work can improve the extraction oil quality. A developed texture analyzer with in situ observation is built for COS mechanical-structural and crack testing. Influencing factors are studied under working effects, considering fruit size, drying temperature, compression distance, speed, directions. The shell compression damage is analyzed with compression damage model. Results show that COS compression damage can be divided into three stages: shell elastic deformation, initial cracking, crack propagation. Compression force decreases rapidly after initial cracking, increases with cracking propagation. Compression direction leads different cracking, cracking load F-x > F-y. Shell cracking force increases with increasing compression speed, distance, fruit diameter, leading intensified crack. Cracking force decreased with drying temperature increasing in 20-110 degrees C, obvious at 80 degrees C. FEM simulation matches COS cracking behavior, stress concentration increases at apical. The research provides theoretical basis for hulling design. Practical applications Camellia oleifera shell (COS) mechanical-structural property and cracking behavior were crucial for shelling, hulling work improves the extraction oil quality. COS cracking behavior were studied under various working effects such as fruit size, drying temperature, compression distance, speed, and direction, and providing a theoretical basis for improving hulling methods. Based on the actual hulling process, research on temperature factors has been added, and the conclusion that the optimal drying temperature is 80 degrees C has been proposed. Considering energy conservation and tea oil quality, hulling efficiency has been improved. Meanwhile, cracking behavior and mechanical properties were studied from cracking in three stages, preventing excessive extrusion caused to seeds damage in hulling. The research provides theoretical basis for hulling design and optimization.
摘要：
This article studies the use of inspection robots to construct environmental maps of domestic bungalow granaries, employing mobile robots to build different granary maps under varying conditions. The 2D maps of the granary are provided to the robots for inspection tasks, helping them reach designated locations and replacing manual inspection methods. During the construction of 2D maps, when new obstacles are discovered or the robot’s pose significantly deviates, the construction switches to 3D maps for more detailed perception and analysis of the environment, ensuring the safety of grain storage. High-precision 2D maps are crucial for the robots to navigate effectively to specified locations during inspections. To address the issues of particle degradation and loss of particle diversity in the traditional FastSLAM algorithm, which lead to reduced accuracy in robot localization and map construction, an improved FastSLAM algorithm based on the hunter-prey optimization (HPO) is proposed. The HPO algorithm, improved with chaotic strategy and Levy flight strategy, optimizes FastSLAM by enhancing particle prediction accuracy. Simulation studies conducted on the MATLAB platform show that the IHPO-FastSLAM algorithm has higher pose accuracy and landmark estimation accuracy compared to the traditional FastSLAM algorithm. Finally, the improved algorithm was tested in a constructed granary environment, and comparison results of the constructed maps demonstrate that the improved algorithm has higher mapping accuracy. This research contributes to the application of mobile robots in granaries, advancing automation and intelligence in the grain storage industry.
摘要：
Peeling wheat yields higher-quality flour. During processing in a flaking machine, wheat kernels undergo continuous compression within the machine’s chamber. As this compression persists, damage to the kernels intensifies and accumulates, eventually leading to kernel breakage. To study the damage characteristics of wheat kernels during peeling, this study established a continuous damage model based on Hertzian contact theory and continuous damage theory. The model’s accuracy was validated through experiments, culminating in the calculation of critical parameters for wheat peeling. This study focused on different wheat varieties (Ningmai 22 and Jichun 1) and kernel sizes (the thicknesses of the small, medium, and large kernels were standardized as follows: Ningmai 22—2.67 ± 0.07 mm, 2.81 ± 0.07 mm, and 2.95 ± 0.07 mm; Jichun 1—2.98 ± 0.11 mm, 3.20 ± 0.11 mm, and 3.42 ± 0.11 mm). Continuous compression tests were conducted using a mass spectrometer, and critical damage parameters were analyzed and calculated by integrating the theoretical model with experimental data. The test results showed that the average maximum crushing force (Fc) for small, medium, and large-sized kernels of Ningmai 22 was 96.71 ± 2.27 N, 110.17 ± 2.68 N, and 128.41 ± 2.85 N, respectively. The average maximum crushing deformation (αc) was 0.65 ± 0.08 mm, 0.68 ± 0.13 mm, and 0.77 ± 0.17 mm, respectively. The average elastic–plastic critical pressure (Fs) was 50.21 N, 60.13 N, and 59.08 N, respectively, and the average critical values of elastic–plastic deformation (αs) were 0.37 mm, 0.38 mm, and 0.39 mm, respectively. For Jichun 1, the average maximum crushing force (Fc) for small-, medium-, and large-sized kernels was 113.34 ± 3.15 N, 125.28 ± 3.64 N, and 136.15 ± 3.29 N, respectively. The average maximum crushing deformation (αc) was 0.75 ± 0.11 mm, 0.83 ± 0.15 mm, and 0.88 ± 0.18 mm, respectively. The average elastic–plastic critical pressure (Fs) was 58.11 N, 64.17 N, and 85.05 N, respectively, and the average critical values of elastic–plastic deformation (αs) were 0.45 mm, 0.47 mm, and 0.52 mm, respectively. The test results indicated that during mechanical compression, if the deformation is less than αs, the continued application of the compression load will not result in kernel crushing. However, if the deformation exceeds αs, continued compression will lead to kernel crushing, with the required number of compressions decreasing as the deformation increases. If the deformation surpasses αc, a single compression load is sufficient to cause kernel crushing. Since smaller wheat kernels are more susceptible to breakage during processing, the peeling pressure (F) within the chamber should be controlled to remain below the average elastic–plastic critical pressure (Fs) of small-sized wheat kernels. Additionally, the kernel deformation (α) induced by the flow rate and loading in the chamber should be kept below the average elastic–plastic critical deformation (αs) of small-sized wheat kernels. This paper provides a theoretical foundation for the structural design and optimization of processing parameters for wheat peeling machines.
摘要：
<jats:p>With the development of machine vision technology, deep learning and image recognition technology has become a research focus for agricultural product non-destructive inspection. During the ripening process, banana appearance and nutrients clearly change, causing damage and unjustified economic loss. A high-efficiency banana ripeness recognition model was proposed based on a convolutional neural network and transfer learning. Banana photos at different ripening stages were collected as a dataset, and data augmentation was applied. Then, weights and parameters of four models trained on the original ImageNet dataset were loaded and fine-tuned to fit our banana dataset. To investigate the learning rate’s effect on model performance, fixed and updating learning rate strategies are analyzed. In addition, four CNN models, ResNet 34, ResNet 101, VGG 16, and VGG 19, are trained based on transfer learning. Results show that a slower learning rate causes the model to converge slowly, and the training loss function oscillates drastically. With different learning rate updating strategies, MultiStepLR performs the best and achieves a better accuracy of 98.8%. Among the four models, ResNet 101 performs the best with the highest accuracy of 99.2%. This research provides a direct effective model and reference for intelligent fruit classification.</jats:p>
