Modeling
A. Shahraki; M. Khojastehpour; M. R. Golzarian; E. Azarpazhooh
Abstract
IntroductionDrying is one of the oldest methods of food preservation. To increase the efficiency of heat and mass transfer while maintaining product quality, the study of the drying process is crucial scientifically and meticulously. It is possible to conduct experimental tests, trial and error, in the ...
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IntroductionDrying is one of the oldest methods of food preservation. To increase the efficiency of heat and mass transfer while maintaining product quality, the study of the drying process is crucial scientifically and meticulously. It is possible to conduct experimental tests, trial and error, in the drying process. However, this approach consumes time and cost, with a significant amount of energy resources. By harnessing available software and leveraging technological advancement to develop a general model for drying food under varying initial conditions, the drying process can be significantly optimized.Materials and MethodsThis study was conducted with the aim of simulating heat and mass transfer during Refractance window drying for aloe vera gel. Comsol Multiphysics version 5.6 is a three-dimensional model used to solve heat and mass transfer equations. For this purpose, the differential equations of heat and mass transfer were solved simultaneously and interdependently. The above model considered various initial conditions: water temperature of 60, 70, 80, and 90℃, and aloe vera gel thickness of 5 and 10 mm. The initial humidity and temperature of the aloe vera is uniform. The initial temperature is 4℃ and the initial humidity of the fresh aloe vera sample is 110 gwater/gdry matter. Heat is supplied only by hot water from the bottom surface of the product.Results and DiscussionThe drying time was needed to reduce the moisture content of aloe vera gel from 110 to 0.1 gwater/gdry matter during Refractance window drying. Aloe vera gel with a thickness of 5 mm dried in 120, 100, 70, and 50 minutes at water temperatures of 60, 70, 80, and 90℃, respectively. For a 10 mm thick layer of aloe vera gel, the drying time was 240, 190, 150, and 120 minutes, for water temperatures of 60 to 90℃, respectively. These results demonstrate the importance of both the water temperature and thickness on the drying time. Furthermore, the drying rate of aloe vera gel increased as the water temperature increased from 60 to 90℃, the drying rates were 0.915, 1.099, 1.57, and 2.198 gwater/min for 5 mm thickness and 0.457, 0.578, 0.732, and 0.915 gwater/min for 10 mm thick layer of aloe vera gel, respectively.ConclusionBased on the simulation results, the optimal model is with a water temperature of 90℃ and an aloe vera gel thickness of 5 mm. Overall, the modeling results are consistent with the results of experimental data.
S. I. Shariati; M. H. Aghkhani; M. R. Golzarian; A. A. Akbari
Abstract
IntroductionRobots have been used for material handling for many years, and their applications have greatly expanded with the integration of intelligent technologies. While numerous researchers have proposed various robots for this field, it is crucial to design customized configurations that are suitable ...
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IntroductionRobots have been used for material handling for many years, and their applications have greatly expanded with the integration of intelligent technologies. While numerous researchers have proposed various robots for this field, it is crucial to design customized configurations that are suitable for agricultural farms. However, research in our country has been limited to a few mobile agricultural robots. The main focus of this paper is to design and model workspaces and analyze the kinematics of manipulators in agricultural settings.Materials and MethodsThis article investigates the workspace and kinematics of a robot manipulator to design and manufacture a four-DOF manipulator for farming. This manipulator will be capable of performing a variety of tasks, but the goal of this project is to enable it to load and unload materials and products on the farm as an auxiliary force for the farmer.When designing and analyzing a manipulator, the first step is to determine the specific task that the robotic arm will perform. For example, consider a scenario where the task involves loading or unloading forage packages from a trailer at a designated location. This task specification forms the basis for further design and analysis, ensuring that the manipulator is appropriately designed to meet the requirements of the task.An intelligent robotic arm that is attached to a tractor can perform this operation in the shortest possible time without the intervention of human workers. Otherwise, a large number of laborers would be required to move boxes weighing 10 kg over distances of 3 to 4 meters and heights of 1 to 2 meters, which would require a great deal of torque.At this stage, the design of the arm kinematics model, direct kinematic equations, velocity kinematics, and Jacobian matrix solving were performed. The calculations were carried out using two methods: manual calculation and kinematic modeling in MATLAB software for three arm configurations in two simulation tests. The results of both methods were compared.The workspace analysis of the selected manipulator configurations, as well as the use of arm kinematic performance evaluation indices, were illustrated in graphs.Results and DiscussionThe issue of moving forage packages on the farm is described below. If a farmer were to move 48 packages of fodder weighing about 10 kg manually (using human workers) in the workspace modeled in Figure 10, each package would take an average of 30 seconds to be moved reciprocally along an unobstructed path. Hence, it would take approximately 24 minutes to move all the packages. However, the linear speed of the final operator of the robot arm during the first test was found to be 1 meter per second, which is 3.7 times faster than the manual work scenario, and the total movement of the packages can be completed in about 6.5 minutes.Upon analyzing the velocity diagrams of the final performer in both tests, it becomes evident that there is not much variation in speed and acceleration due to the change in configurations. The evaluation of robot workspace indicators was conducted using two methods: workspace index and structural length index. These indicators were calculated for all three configurations, and the results indicated that Configuration Type 1 was the most suitable option. Furthermore, the manipulability index of the robot arm was assessed based on the obtained diagrams for all three configurations in the two tests. It was observed that Configuration Type 1 outperformed the other two types in terms of score, indicating its superior performance. This aligns with the suggestion made by Yoshigawa for the first three joints of the Puma robot.Overall, the results suggest that Configuration Type 1 is one of the most favorable options, ensuring better performance for the final performer.ConclusionOne of the main considerations when using robots in agriculture is the appropriate kinematic design of joints and links for work operations. Using the example of robots assisting with moving products on the ground, it can be seen that using robots significantly reduces the time required compared to manual labor. Furthermore, in terms of energy consumption and cost within a certain period, the use of robots has economic justification.Based on the studies conducted, Configuration Type 1 passed the kinematic path in both tests with a higher manipulability index and a more suitable workspace index based on both calculated criteria. Therefore, this configuration is recommended for the design of robots for the operation of moving products on the ground.
A. Jafari Malekabadi; M. Khojastehpour; B. Emadi; M. R. Golzarian
Abstract
Introduction: Poisson ratio and modulus of elasticity are two fundamental properties of elastic and viscoelastic solids that use in solving all contact problems, including the calculation of stress, the contact surfaces and elastic deformation (Mohsenin, 1986; Gentle and Halsall, 1982).
There are many ...
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Introduction: Poisson ratio and modulus of elasticity are two fundamental properties of elastic and viscoelastic solids that use in solving all contact problems, including the calculation of stress, the contact surfaces and elastic deformation (Mohsenin, 1986; Gentle and Halsall, 1982).
There are many published literature on Poisson ratio and elasticity modulus of fruit and vegetables. Shitanda et al. (2002) calculated Poisson ratio of rice by considering Boussinesq’s theory. They showed that the Poisson ratio is greater for shorter varieties. In another study, researchers used the instrumented bending beam to measure the lateral expansion of red beans. They were considered Poisson ratio as the ratio of transverse strain to the longitudinal strain (regardless of the geometry of the sample) and were calculated modulus of elasticity with Hertz theory for convex bodies (Kiani Deh Kiani et al., 2009). Cakir et al. (2002) was determined the Poisson ratio and elastic modulus of some onion varieties. They used a simple formula to determine the transverse strain that developed by Sitkei (1986) for prism-shaped rod, regardless of the geometry of the product.
Reviewed scientific literature shows that these parameters have not been studied according to the geometric shape of onions and was not used by a more accurate method, such as image processing to determine these parameters. The objective of this study was to evaluate the mechanical properties of two varieties of onions. Poisson ratio was determined with image processing. Considering shape of the onions and deformation value, and using Hertz’s theory with Poisson ratio, modulus of elasticity was calculated. The effects of loading directions (polar or equatorial), deformation value (5, 10 and 15 mm), loading speed (15 or 25 mm min-1) and onion varieties (Red and Yellow) on the modulus of elasticity and apparent Poisson’s ratio were examined.
Materials and Methods: The onions harvested in autumn, 20 days before conducting the tests. Onion samples kept at room temperature (21oC). Onions of each cultivar were randomly selected. Diameters of onion were measured with a digital vernier caliper. In each run, eight onions were randomly selected and the loading test and photography were done together and the average values reported.
All mechanical tests were performed using a Universal Testing Machine (UTM) (Model H5KS, Tinius Olsen Company) between two rigid plates. The loading was made with two constant speeds of 15 and 25 mm min-1. Deformation values were 5, 10 and 15 mm. The onions were loaded either axially or laterally until rupture point and photography were done together.
The initial and current onion diameters along the y and x axes obtained by using image processing and the strains were calculated. Having axially and laterally strains of the onions, the apparent Poisson's ratio was calculated using equation presented by Figura and Teixeira 2007; Kiani Deh Kiani et al., 2009; Pallottino et al., 2011; Kabas and Ozmerzi 2008; Gladyszewska and Ciupak 2009.
A factorial experiment based on a completely randomized design with 8 replications was applied. The significant differences of means were compared by using the Duncan’s multiple range test at 5% significant level. SPSS 20.0 software was used for data analysis.
Results and Discussion: According to the analysis of variance (Table 2), the effects of speed and displacement of loading was significant in 5% probability levels. In addition, interaction effect varieties × directions × speed along Y, varieties × directions, varieties × speed and directions × speed along X was significant in 1, 1, 5 and 5% probability levels, respectively. The average of the apparent Poisson ratio for Yellow onion was less than that obtained for the Red onion, because Red onions have softer texture than Yellow onions. Apparent Poisson ratio was obtained as 0.2623 to 0.4485 and 0.2423 to 0.4179 for Yellow and Red onions, respectively. With increasing deformation, apparent Poisson ratio increased.
Modulus of elasticity along X and Y
According to the analysis of variance (Table 2), the effects of speed and displacement of loading and directions × speed was significant in 1% probability levels. The average of the modulus of elasticity for Red onion was less than that obtained for the Yellow onion because Yellow onion has tougher and more powerful texture than Red onion. Modulus of elasticity were obtained as 2.032 to 5.449 and 1.829 to 5.311 MPa for Yellow and Red onions, respectively. The modulus of elasticity for lateral loading was less than that obtained for the axial loading. With increasing deformation, the modulus of elasticity decreased. The modulus of elasticity for lateral loading in loading speed 25 mm min-1 was less than that obtained for loading speed 15 mm min-1.
Conclusions: The results were summarized as below:
Loading speed, deformation value and their interaction effect were significant in different confidence levels for apparent Poisson's ratio and modulus of elasticity.
The compression force of Yellow onion was more than Red onion. Thus, it can be concluded that Yellow onions have more strength against the forces and loading.
The modulus of elasticity for lateral loading was less than that obtained for the axial loading. It is better to be considered for packaging of onions.
The modulus of elasticity for lateral loading in loading speed 25 mm min-1 was less than that obtained for loading speed 15 mm min-1.
With increasing deformation, the modulus of elasticity and apparent Poisson’s ratio decreased and increased, respectively.
A. Moghimi; M. H. Aghkhani; M. R. Golzarian
Abstract
In recent years, automation in agricultural field has attracted more attention of researchers and greenhouse producers. The main reasons are to reduce the cost including labor cost and to reduce the hard working conditions in greenhouse. In present research, a vision system of harvesting robot was developed ...
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In recent years, automation in agricultural field has attracted more attention of researchers and greenhouse producers. The main reasons are to reduce the cost including labor cost and to reduce the hard working conditions in greenhouse. In present research, a vision system of harvesting robot was developed for recognition of green sweet pepper on plant under natural light. The major challenge of this study was noticeable color similarity between sweet pepper and plant leaves. To overcome this challenge, a new texture index based on edge density approximation (EDA) has been defined and utilized in combination with color indices such as Hue, Saturation and excessive green index (EGI). Fifty images were captured from fifty sweet pepper plants to evaluate the algorithm. The algorithm could recognize 92 out of 107 (i. e., the detection accuracy of 86%) sweet peppers located within the workspace of robot. The error of system in recognition of background, mostly leaves, as a green sweet pepper, decreased 92.98% by using the new defined texture index in comparison with color analysis. This showed the importance of integration of texture with color features when used for recognizing sweet peppers. The main reasons of errors, besides color similarity, were waxy and rough surface of sweet pepper that cause higher reflectance and non-uniform lighting on surface, respectively.
