蔬菜播種機(jī)的設(shè)計(jì)【含三維SolidWorks、CAD圖紙、說明書】

喜歡這個(gè)資料需要的話就充值下載吧。資源目錄里展示的全都有預(yù)覽可以查看的噢，下載就有,請(qǐng)放心下載，原稿可自行編輯修改=【QQ：11970985 可咨詢交流】=喜歡就充值下載吧。資源目錄里展示的全都有，下載后全都有,請(qǐng)放心下載，原稿可自行編輯修改=【QQ：197216396 可咨詢交流】= 畢業(yè)設(shè)計(jì)(論文)翻譯 學(xué)生姓名： 學(xué)院： 機(jī)電工程學(xué)院 專 業(yè)： 機(jī)械設(shè)計(jì)制造及自動(dòng)化 設(shè)計(jì)(論文)題目： 小型蔬菜播種機(jī)的設(shè)計(jì) 指導(dǎo)教師： 年4月10日Mathematical Modelling of Vacuum Pressure on a Precision SeederAbstractThe purpose of this research was to determine the optimum vacuum pressure of a precision vacuum seeder and to develop mathematical models by using some physical properties of seeds such as one thousand kernel mass, projected area, sphericity and kernel density. Maize, cotton, soya bean, watermelon, melon, cucumber, sugarbeet and onion seeds were used in laboratory tests. One thousand kernel mass, projected area, sphericity and kernel density of seeds varied from 4.3 to 372.5g, 577mm2, 38.485.8% and 4401310kgm3, respectively. The optimum vacuum pressure was determined as 4.0kPa for maize I and II; 3.0kPa for cotton, soya bean and watermelon I; 2.5kPa for watermelon II, melon and cucumber; 2.0kPa for sugarbeet; and 1.5kPa for onion seeds. The vacuum pressure was predicted by mathematical models. According to the results, the final model could satisfactorily describe the vacuum pressure of the precision vacuum seeder with a chi-square of 2.51103, root mean square error of 2.74102 and modelling efficiency of 0.99. Nomenclature Nomenclature a, b, c, d, e regression coefficients Em modelling efficiency Erms root mean square error kexp experimental vacuum pressure, kPa kexp, mean mean value of experimental vacuum pressure, kPa kpre predicted vacuum pressure, kPa L length, mm m1000 one thousand kernel mass, g N number of observation n number of constants in the model P projected area, mm2 Pv vacuum pressure, kPa p probability R2 coefficient of determination T thickness, mm W width, mm sphericity, % 2 chi-square k kernel density, kgm3Article OutlineNomenclature 1. Introduction 2. Literature review 3. Materials and methods 4. Results and discussion 5. Conclusions Acknowledgements References1. IntroductionPrecision sowing has been a major thrust of agricultural engineering research for many years; however, most of the research and development work has dealt with seeders for agronomic crops. The main purpose of sowing is to place the seed to a certain space and a depth in the seedbed. Precision seeders place seeds at the required spacing and provide a better growing area per seed. There are two common types of precision seeders: belt and vacuum. Precision vacuum seeders have a metering plate with metering holes on a predetermined radius. A vacuum is applied to these metering holes by means of a race machined in a backing plate. As the plate rotates, the vacuum applied to the metering holes enables them to pick up seeds from the seed hopper. Precision vacuum seeders have the following advantages over the mechanical seeders: better working quality, more precise seed rates with lower rate of seed damage, better control and adjustment of upkeep and drift of seeds, and broader spectrum of applicability (Soos et al., 1989). A seeder should place a seed in an environment in which the seed will reliably germinate and emerge. A number of factors affect the spacing of plants. The seed selection mechanism may fail to select or drop a seed resulting in large spacing between seeds. The mechanism may select and drop multiple seeds resulting in small spacings between seeds. Seed quality, soil conditions, seeder design and the skill of the operator all play a part in determining the final plant stand. The physical properties of seeds are essential for the design of equipment for handling, processing, storing and sowing the kernels. Various types of cleaning, grading, separation and sowing equipment are designed on the basis of the physical properties of seeds. However, no model has been found to describe seeder parameters such as vacuum pressure related with physical properties of seeds. The physical properties of the seeds are the most important factors in determining the optimum vacuum pressure of the precision vacuum seeder. In this study, using some of these, e.g. one thousand kernel mass, projected area, sphericity and kernel density, mathematical models were developed to predict optimum vacuum pressure. The experimental values of vacuum pressure were determined from laboratory test procedure. 2. Materials and methodsThe laboratory test procedure involved testing the metering uniformity of the seeder at the different vacuum pressure with the different seeds: two different maize varieties (maize I and maize II), cotton, soya bean, two different watermelon varieties (watermelon I and watermelon II), melon, cucumber, sugarbeet and onion. These seeds represent several seed shapes varying from spherical (soya bean, maize II) to flat and elongated (maize I, melon, watermelon, cucumber). Two different varieties of maize and watermelon seeds were selected, because of the more diverse range of one thousand kernel mass, projected area, sphericity or kernel density than other seeds. All seeds used in this research were uncoated seed. The main dimensions of the seeds are given in Table 1. The seeder was set to space the seeds as closely to the recommended spacing as possible. Table 1. Means and standard errors of the seed dimensions A grease belt test stand was used to determine sowing uniformity of each seed at the different vacuum pressures. This particular test stand had a 150mm wide belt with a 75m long horizontal viewing surface. A seeder row unit was mounted on a greased belt test stand which utilised an adjustable speed drive mechanism to operate the seed metering devices at a known constant speed. Sufficient oil was added to the top surface of belt to capture the seed as it was released from seeder unit without rolling or bouncing of seed on the belt surface. A wide variety of measures were used to qualify seeder performance with regard to plant spacing (Brooks & Church, 1987; Karayel & zmerzi, 2001; Jasa & Dickey, 1982). Some tests used performance measures involving distance between plants in the field. Other tests used performance measures involving distance between seeds on grease belt test stand or by opto-electronic sensor system ( Bracy et al., 1998; Smith et al., 1991; Lan et al., 1999). A few tests used performance measures involving distance between seeds sown into soil ( Panning, 1997). A precision vacuum seeder unit was operated in all treatments (Fig. 1). The seeder unit was a general purpose seeder designed for row crops such as maize and soya beans. Three different vacuum plates with different hole diameters were used in the metering mechanism. The diameter of vacuum plates were 230mm. The holes were drilled along a 200mm diameter pitch circle. The holes of the vacuum plate were 35mm in diameter for maize I, II, soya bean and cotton; 25mm in diameter for watermelon I, II, melon, and cucumber and 15mm in diameter for sugarbeet and onion. The seed plate operated in a vertical plane. Air suction from the holes of the seed plate caused the seed to stick to the holes. The stuck seed was released from the rotating plate by temporarily preventing airflow. The absence of suction allowed the seed to be dropped into soil. It had no seed tube and the seed fall height (12mm) of the seeder was kept low in order to reduce the chance of non-uniform spacing which can occur due to the bouncing of seed, if dropped from high plane. The vacuum level was regulated by adjusting the size of an opening in the vacuum line of seeder and measured with a manometer. Fig. 1. The metering mechanism of the precision vacuum seeder: 1, vacuum plate; 2, seed; 3, seed box; 4, air suction canal; 5, air cut; 6, furrow opener The seeder was operated over the greased belt at a ground speed of 1ms1 and adjusted to four vacuum pressures 2.0, 3.0, 4.0 and 5.0kPa for maize I, II, soya bean and cotton; 2.0, 2.5， 3.0 and 3.5kPa for melon, watermelon I, II and cucumber; 1.0, 1.5, 2.0 and 2.5kPa for sugarbeet and onion seeds. Seed spacings were measured over a distance of 7m. The seeder was adjusted to deliver a nominal seed spacing of 230mm for maize I and II, 170mm for cotton, 105mm for soya bean, 550mm for watermelon I, II, melon and cucumber, 150mm for sugarbeet and 85mm for onion. The sowing uniformity was analysed using the methods as described by Kachman and Smith (1995). The multiple index is the percentage of spacings that are less than or equal to half of the theoretical spacing and indicates the percentage of multiple seed drops. The miss index is the percentage of spacings greater than 1.5 times the theoretical spacing and indicates the percentage of missed seed locations or skips. Quality of feed index is the percentage of spacings that are more than half but no more than 1.5 times the theoretical spacing. Quality of feed index is 100% minus miss and multiple index and indicates the percentages of single seed drops. Preciseness is the coefficient of variation of the spacings that are classified as singles after omitting the outliers consisting of misses and multiples. Kachman and Smith (1995) recommended using miss index, multiple index, quality of feed index and preciseness for summarising the uniformity of seeder metering rather than mean or sample coefficient of variation. They concluded that several measures were needed to give a true picture of seeder uniformity. For this study, miss index, multiple index, quality of feed index and preciseness are reported. Various physical properties of seeds including kernel density, projected area, sphericity and one thousand kernel mass are the most important factors in determining the optimum vacuum pressure of the precision vacuum seeder (Barut, 1996). The physical properties of the seeds were determined by the following methods: Linear dimensions, i.e. length, thickness and width were measured by using a vernier caliper with a sensitivity of 0.01mm. Sphericity were calculated by using the following equation (Mohsenin, 1970):(1)where: L is the length; W is the width; and T is the thickness in mm. One thousand kernel mass was measured by an electronic balance with a sensitivity of 0.001g. Kernel density was measured by the liquid displacement method. Toluene (C7H8) was used rather than water because it was not absorbed by fruits (Mohsenin, 1970; gt, 1998). Projected area was determined by using a digital camera (Kodak DC 5000) and Sigma Scan Pro 5 program. For the estimation of the vacuum pressure, in relation to kernel density, projected area, sphericity and one thousand kernel mass, mathematical models were developed. The suitability of the final model was compared and evaluated using chi-square, root mean square error and modelling efficiency. Chi-square 2, root mean square error Erms and modelling efficiency Em were calculated as follows:(2)(3)(4)where: kexp is the experimental vacuum pressure in kPa; kexp,mean is the mean value of experimental vacuum pressure in kPa; kpre is the predicted vacuum pressure in kPa; N is the number of observations; and n is the number of constants in the model. Reduced chi-square is the mean square of the deviations between the experimental and calculated values for the models and, is used to determine the goodness of the fit. The lower values of the reduced chi-square, the better the goodness of the fit. The root mean square error shows the deviations between the calculated and experimental values and it requires to reach zero. The modelling efficiency also shows the ability of the model and its highest value is 1 (Yaldiz et al., 2001; Ertekin & Yaldiz, 2004). Each experiment was arranged as a randomised complete block (Neter et al., 1990) and replicated five times. An analysis of variance method was applied to analyse data sets using a statistical software package SAS. Duncans multiple-range tests were used to identify significantly different means within dependent variables. 3. Results and discussionThe effect of vacuum pressure on sowing uniformity of the vacuum seeder was analysed relating to the multiple index, miss index, quality of feed index and preciseness. Multiple index, miss index and quality of feed index were combined for analysis of variance to determine the significant difference in the variability among the parameters. The results of this analysis are given in Table 2, Table 3 and Table 4. All measurement of sowing uniformity of the vacuum seeder were affected by vacuum pressure. Table 2. The sowing uniformity of the vacuum seeder with maize I and II, cotton and soya bean seeds for different vacuum pressure Note: Means within a group followed by same letter are not significantly different at probability p=005, by Duncans multiple range test.Table 3. The sowing uniformity of the vacuum seeder with watermelon I and II, melon and cucumber seeds for different vacuum pressure Note: Means within a group followed by same letter are not significantly different at probability p=005, by Duncans multiple range test.Table 4. The sowing uniformity of the vacuum seeder with sugarbeet and onion seeds for different vacuum pressure Note: Means within a group followed by same letter are not significantly different at probability p=0.05, by Duncans multiple range test.The optimum vacuum pressure was determined for each seed according to quality of feed index and preciseness. As can be seen from laboratory study results in Table 2, Table 3 and Table 4, the highest seed spacing uniformities (quality of feed index) and the lowest preciseness values were obtained at the vacuum pressure of 4.0kPa for maize I and II; 3.0kPa for cotton, soya bean and watermelon I; 2.5kPa for watermelon II, melon and cucumber; 2.0kPa for sugarbeet and 1.5kPa for onion seeds. The most uniform sowing uniformity was obtained with soya bean seeds at any vacuum pressures. Uniform, spherical seeds such as soya bean and maize II were easy to meter with the vacuum metering system. The miss index decreased and the multiple index increased with increasing vacuum pressure for all seeds. Multiple seed drops were more common than misses for watermelon I and II, melon, cucumber, onion and sugarbeet seeds. Few skips or multiple drops occur at any vacuum pressure for maize I and II, cotton and soya bean seeds. Loss of uniformity of the vacuum seeder was probably a combination of several factors. The results support reports from Barut (1996) who found that the pattern efficiency of the vacuum plate differed most at lower or higher vacuum pressures and faster wheel speeds. In this research, preciseness and quality of feed index of the vacuum seeder were poorer at the lower and higher vacuum pressures than optimum vacuum pressure. One thousand kernel mass, projected area, sphericity and kernel density of seeds are given in Table 5. One thousand kernel mass, projected area, sphericity and kernel density of seeds varied from 4.3 to 372.5g, 577mm2, 38.485.8% and 4401310kgm3, respectively. Table 5. Means and standard errors of the seed dimensions The relationship between one thousand kernel mass, projected area, sphericity and kernel density with vacuum pressure presented in Fig. 2, Fig. 3, Fig. 4 and Fig. 5. For the determination of the relationship between the one thousand kernel mass and the projected area with vacuum pressure, the power model was used. For the determination of relationship between the sphericity and the kernel density with the vacuum pressure, the linear model was used. The diagrammatic representation of the models results in a curve that fits well for the description of the vacuum pressure. The relationship between one thousand kernel mass with vacuum pressure is better than the others with the highest coefficient of determination of 0.92. Fig. 2. Vacuum pressure of precision vacuum seeder as a function of one thousand kernel mass; R2, coefficient of determination Fig. 3. Vacuum pressure of vacuum seeder as a function of projected area; R2, coefficient of determination Fig. 4. Vacuum pressure of vacuum seeder as a function of sphericity; R2, coefficient of determination Fig. 5. Vacuum pressure of vacuum seeder as a function of kernel density; R2, coefficient of determination All possible combinations of the different variables were tested and included in the regression analysis. The multiple combinations of one thousand kernel mass, projected area, sphericity and kernel density that gave the lowest root mean square error and chi-square and the highest modelling efficiency were finally included in the final model. Based on the multiple regression analysis the accepted model constants, coefficients, chi-square 2, root mean square error Erms and modelling efficiency Em were as follows:Pv=a+bm1000027+cP002d+ekwhere: Pv is the vacuum pressure in kPa; m1000 is one thousand kernel mass in g; P is the projected area in mm2; is the sphericity in %; k is the kernel density in kgm3. The optimum values of the coefficient a, b, c, d, and e, namely 1.00, 0.72, 2.09103, 0.01 and 0.37103, respectively, gave values for 2 of 2.51103, for Erms of 2.74102, and for Em of 0.99. Validation of the established final model was evaluated by comparing the computed vacuum pressures with the observed vacuum pressures. The performance of the model was illustrated in Fig. 6. The predicted data generally banded around the straight line which showed the suitability of the final model in describing vacuum pressure of the seeder. Fig. 6. Experimental versus predicted vacuum pressure values by final model; R2, coefficient of determination 4. ConclusionsIn laboratory tests, the optimum vacuum pressure of a precision vacuum seeder was determined as 4.0kPa for maize I and II; 3.0kPa for cotton, soya bean and watermelon I; 2.5kPa for watermelon II, melon and cucumber; 2.0kPa for sugarbeet and 1.5kPa for onion seeds. In order to predict vacuum pressure in relation to one thousand kernel mass, projected area, sphericity and kernel density of seeds, mathematical models were developed. The relationship between one thousand kernel mass with vacuum pressure was better than the others with the highest coefficient of determination. The final model could satisfactorily describe the vacuum pressure of the precision vacuum seeder with a chi-square of 2.51103, root mean square error of 2.74102 and modelling efficiency of 0.99. Acknowledgements The corresponding author acknowledge the help of Dr. Can ERTEKIN in developing the mathematical models. 真空壓力播種機(jī)的數(shù)學(xué)建模引言 這項(xiàng)研究的目的是確定最佳的精密真空壓力播種機(jī)。通過運(yùn)用種子的一些物理性質(zhì)如每1000粒種子的質(zhì)量,表面積、圓度和種子密度來建立數(shù)學(xué)模型.。分別取玉米、棉花、大豆、西瓜、甜瓜、黃瓜、甜菜、洋蔥的種子作為實(shí)驗(yàn)對(duì)象。結(jié)果，每1000粒種子質(zhì)量、表面積、圓度和種子密度分別為4.3-372.5g、 5-77m2、38.485.8%、440-1310千克/m3。最佳的真空壓力：玉米種子（I、II）為4kPa；棉花、黃豆和西瓜（I）種子為3kPa；西瓜（II）、甜瓜和黃瓜種子為2.5kPa；甜菜種子為2kPa；洋蔥種子為1.5kPa。最終，數(shù)學(xué)模型能準(zhǔn)確模擬出真空壓力。研究結(jié)果顯示：模型能準(zhǔn)確地模擬出精密真空壓力播種機(jī)的真空度為2.5110-3； 均方根誤差為2.7410-2。模擬效率率高達(dá)99%。各參數(shù)含義回歸系數(shù): a,b,c,d,e模型效率：Em均方根誤差：Erme試驗(yàn)真空壓力 (kPa)：Kexp試驗(yàn)真空壓力平均值(kPa): Kexp.mean真空預(yù)壓(kPa):Kpre長(zhǎng)度(mm)：L每千粒種子質(zhì)量(g)：m1000種子數(shù)目：N模型種子數(shù)常量：n表面積(mm2)：P真空壓力(kPa)：Pv概率：p確定系數(shù)：R2厚度(mm)：T寬度(mm)：W圓度(%)：方差：x2種子密度(kg/m3)：k文章概要標(biāo)題1.引言2.材料和方法3.結(jié)果與討論4.結(jié)論5.致謝6.參考資料1.引言精密播種作為主要農(nóng)業(yè)工程研究已經(jīng)多年。所以,大部分的研究和開發(fā)成果已經(jīng)運(yùn)用到了農(nóng)業(yè)播種?，F(xiàn)在，研究的主要目的是把種子播到一定深度的苗床上。精密播種機(jī)必須讓種子之間有一定間隔，以適應(yīng)種子生長(zhǎng)。現(xiàn)在，有兩種類型的精密播種機(jī):皮帶播種機(jī)和真空壓力播種機(jī)。真空壓力播種機(jī)有一帶有固定半徑計(jì)量孔的真空計(jì)量板。計(jì)量板應(yīng)用這些計(jì)量手段洞競(jìng)賽通過一回收裝置. 由于平板旋轉(zhuǎn)、真空所產(chǎn)生的壓力使種子從這些孔中漏出. 精密真空壓力播種機(jī)具有以下優(yōu)點(diǎn)：更好的工作質(zhì)量、較低種子損害率、更好地控制和保護(hù)種子、還有廣泛的適用性。 精密播種機(jī)需要把種子播種在一個(gè)可靠的環(huán)境,使種子發(fā)芽并生長(zhǎng)。很多因素包括種子間距都能影響種子生長(zhǎng)