軸流式脫揚機的總體及脫粒裝置設(shè)計【脫粒清選稻、麥】【脫粒機】,脫粒清選稻、麥,脫粒機,軸流式脫揚機的總體及脫粒裝置設(shè)計【脫粒清選稻、麥】【脫粒機】,軸流,式脫揚機,總體,整體,脫粒,裝置,設(shè)計,清選稻 UNIVERSITY 設(shè) 計 題目： 軸流式脫揚機的總體及脫粒裝置畢業(yè)設(shè)計 學(xué) 院： 姓 名： 學(xué) 號： 專 業(yè)： 機械設(shè)計制造及其自動化 年 級： 指導(dǎo)教師：=== 職 稱： 二○一二 年 五 月 13 摘要 本設(shè)計是關(guān)于軸流式脫揚機的總體設(shè)計，主要對軸流式脫揚機的脫粒裝置總體進行計算和分析設(shè)計。 軸流式脫揚機脫粒部分主要由滾筒、主軸、釘齒、機架等組成，通過對進入脫粒室的谷物進行沖擊和揉搓作用，分離莖稈和谷粒，并直接將谷粒拋出。它能有效提高脫粒效率，有利于脫粒干凈，減低對稻穗的破碎率，并能很好的分離莖稈和谷粒，從而有效提高農(nóng)業(yè)生產(chǎn)率，大大減輕農(nóng)民勞動強度。 關(guān)鍵字：脫揚機；分離；滾筒 Abstract This project ,which focused on the total calculation and analysis of Axial de-Yang machine's Threshing devices ,is about the total design of Axial de-Yang machine . The Axial de-Yang machine's threshing part is constitute of roller,spindle,frame and nail tooth.When grain get into the threshing room,through to the impact of the grain and knead role,the stem and the grain will separate,and the grain will been throw away.this machine can effectively improve the efficiency of threshing,make threshing more clearer, and reduce the grain's broken rate,what's more,it can be well to separate stem and grain,then effectively improve agricultural productivity,reduce farmer's labor intensity. Key words :Axial de-Yang machine;separate;roller 目 錄 1. 緒論 1 1.1設(shè)計的目的與意義 1 1.2國內(nèi)外發(fā)展現(xiàn)狀 1 2. 軸流式脫揚機的總體方案設(shè)計及工作原理 2 2.1總體方案的選擇 2 2.2工作原理 2 3. 電動機的選擇 3 3.1電動機的類型和結(jié)構(gòu) 3 3.2電動機容量的選擇 3 3.3.電動機型號的選擇 4 4. 滾筒的設(shè)計 4 4.1.滾筒的型式 4 4.2.滾筒的直徑和轉(zhuǎn)速 4 4.3滾筒齒的形狀和排列 5 4.4滾筒長度 5 5. 滾筒釘齒的設(shè)計 5 5.1滾筒釘齒的形狀 5 5.2滾筒釘齒的排列 6 6. 凹板篩的設(shè)計 7 6.1凹板篩型式選擇 7 6.2凹板篩包角選擇 7 6.3凹板篩間隙確定 7 7. 滾筒主軸的設(shè)計與校核 7 7.1.滾筒主軸的形狀 7 7.2.選擇軸的材料 7 7.3初步確定軸的直徑 8 7.4軸的結(jié)構(gòu)設(shè)計 8 7.5軸上零件的周向定位 8 7.6滾筒主軸的強度校核 9 7.6.1.對軸進行受力分析并簡化軸的受力 9 7.6.2.計算水平面上的剪切力和彎矩，找出危險截面 9 7.6.3.計算垂直面上的剪切力和彎矩，并找出危險截面 9 7.6.4.計算轉(zhuǎn)矩 10 7.7鍵聯(lián)接的強度強度校核 10 8. 軸承的選用 11 參考文獻 12 致謝 13 1. 緒論 1.1 設(shè)計的目的與意義 隨著我國農(nóng)業(yè)的不斷發(fā)展，我國對農(nóng)村的問題越來越關(guān)注，農(nóng)業(yè)是國民經(jīng)濟的基礎(chǔ), 這是不以人們意志為轉(zhuǎn)移的客觀經(jīng)濟規(guī)律。農(nóng)業(yè)生產(chǎn)力發(fā)展的水平和農(nóng)業(yè)勞動生產(chǎn)率的高低, 決定了農(nóng)業(yè)為其他部門提供剩余產(chǎn)品和勞動力的數(shù)量, 進而制約著這些部門的發(fā)展規(guī)模和速度。只有農(nóng)業(yè)發(fā)展了, 國民經(jīng)濟其他部門才能得以進一步的發(fā)展。而農(nóng)業(yè)機械化是農(nóng)業(yè)現(xiàn)代化的中心環(huán)節(jié), 它凝聚著現(xiàn)代科學(xué)技術(shù)的最新成果, 并配合農(nóng)業(yè)生物等農(nóng)業(yè)技術(shù), 成為發(fā)揮增產(chǎn)作用的基本手段和提高勞動生產(chǎn)率、減輕繁重體力勞動的必要條件和根本途徑, 從而帶來生產(chǎn)力的質(zhì)的飛躍，面對我過十幾億的人口壓力，發(fā)展農(nóng)業(yè)的機械化顯得尤其重要，由機械化代替人力畜力作業(yè)不只是我國農(nóng)業(yè)的未來發(fā)展趨勢，也是整個世界農(nóng)業(yè)的發(fā)展趨勢。 我國是一個以農(nóng)業(yè)生產(chǎn)為主的發(fā)展中大國，20世紀后半期我國用占世界7%的耕地,卻為世界22%的人口提供了基本充足的食品。農(nóng)業(yè)的快速穩(wěn)步的發(fā)展離不開農(nóng)業(yè)機械化。中國農(nóng)業(yè)機械化經(jīng)過多年的努力發(fā)展，已經(jīng)取得了一定的成就，但是仍然存在一些不足，如何讓在日常的生產(chǎn)影響中有限的提高生產(chǎn)率，實現(xiàn)一機多用是擺在人們面前的一個棘手的問題，實現(xiàn)農(nóng)業(yè)的現(xiàn)代化、智能化是今后農(nóng)業(yè)的必然選擇。 通過采用現(xiàn)代化農(nóng)業(yè)工程和機械技術(shù)，適應(yīng)自然環(huán)境，為植物生產(chǎn)收獲提供相對更為有利條件，從而在一定程度上擺脫對自然環(huán)境的依賴而進行有效生產(chǎn)的農(nóng)業(yè)，它是在人們生活需求不斷增長的同時發(fā)展起來的，是在人為可控設(shè)施下得農(nóng)業(yè)生產(chǎn)，具有高投入、高技術(shù)含量、高品質(zhì)、高產(chǎn)量和高效益等特點，是最具活力的現(xiàn)代新農(nóng)業(yè)。 全喂入式軸流式脫揚機，要求體積的質(zhì)量小、動力足，操作舒適，符合人機工程學(xué)的設(shè)計原理，減輕作業(yè)者勞動強度，盡量減少發(fā)動機對設(shè)施環(huán)境的污染。 我國是農(nóng)業(yè)大國，農(nóng)村市場巨大，要發(fā)展農(nóng)村經(jīng)濟，就需要轉(zhuǎn)移農(nóng)村勞動力，提高勞動土地面積，可以預(yù)料，在未來的一段時間里，中國將成為世界上最大的脫揚機市場之一。 1.2國內(nèi)外發(fā)展現(xiàn)狀 1949年,全國農(nóng)業(yè)機械化裝備總動力只有8101萬千瓦, 農(nóng)用拖拉機只有117臺,一些大型農(nóng)業(yè)機械如聯(lián)合收割機、農(nóng)用載重汽車基本上是空白。經(jīng)過半個多世紀的發(fā)展, 農(nóng)業(yè)機械擁有量增長了上千倍, 有的品種甚至數(shù)萬倍，截至2003年底,全國農(nóng)業(yè)機械總動力達到6億kW以上,農(nóng)機原值達3362億美元。農(nóng)用拖拉機保有量達1494萬臺,拖拉機配套農(nóng)機具2292萬部, 聯(lián)合收獲機械36萬臺，農(nóng)田作業(yè)機械化水平顯著提高,機械耕地、播種、收獲水平分別達到46.8 %、26.7 %、19 %；2004年小麥機收比1995年提高了47%，農(nóng)業(yè)機構(gòu)服務(wù)領(lǐng)域由原來的農(nóng)田作業(yè),逐步向產(chǎn)前和產(chǎn)后延伸,向其他領(lǐng)域擴展。一大批設(shè)施農(nóng)業(yè)設(shè)備、農(nóng)副產(chǎn)品加工機械、畜牧業(yè)機械、林業(yè)機械、植保機械、運輸機械、農(nóng)田基本建設(shè)機械等迅速增長。溫室面積達到69 億平方米, 田園管理機達到4 萬臺。[1]近年來, 國際上不少大型農(nóng)機企業(yè)看準(zhǔn)中國巨大的農(nóng)機市場, 與中國有關(guān)部門和企業(yè)合作,在中國開拓事業(yè), 取得了雙贏的佳績。國內(nèi)一些大型企業(yè), 不斷學(xué)習(xí)國外的先進經(jīng)驗和技術(shù), 加大技術(shù)改造和升級換代力度, 推進了國產(chǎn)農(nóng)業(yè)機械化產(chǎn)品質(zhì)量的提高。為鼓勵大型農(nóng)業(yè)機械的進口, 國家還制定了優(yōu)惠進口稅收政策。在中國舉辦類似于今天的展覽會, 也是農(nóng)機界加強國際交流與合作的重要形式, 我們積極支持這類活動在中國開展。 國外軸流式脫揚機的發(fā)展，基本上分為歐美和日本兩大類型，歐美國家以旱地為主，地塊大，各類作物以小麥為主；日本以水田為主，田塊小，以水稻為主。因此，前者用的脫揚機是大型的，大功率的，而后者用的機型都是小型的或中型的。 2. 軸流式脫揚機的總體方案設(shè)計及工作原理 2.1總體方案的選擇 脫粒機械的農(nóng)業(yè)技術(shù)要求是：脫的干凈、損失少、沒有碎粒和脫殼現(xiàn)象，并且盡可能避免谷粒的機械損傷，這對于作為留種的谷粒來說更為重要，因為即使受到輕微的損傷也會影響發(fā)芽率。此外由于谷物的莖桿有著很高的經(jīng)濟價值，要盡量減少其損失和損壞。脫粒機應(yīng)具有一定的生產(chǎn)率。 脫粒的本質(zhì)在于使谷粒和作物本身分離，欲達到這個目的就得借助某一形式的脫粒裝置。本設(shè)計采用的是全喂入型脫粒機構(gòu)，脫粒裝置是滾筒式的，滾筒為一高速旋轉(zhuǎn)的圓柱體，在滾筒表面上裝有脫粒元件，而在滾筒下面裝有不動的圓弧形凹板，凹板和滾筒之間保持一定的間隙，稱為脫粒間隙。脫粒時滾筒將谷物從脫粒間隙中通過進行脫粒，滾筒上采用釘齒式脫粒元件。 2.2工作原理 一、沖擊脫粒 這種脫粒方法目前應(yīng)用最廣，靠脫粒原件與谷穗的相互沖擊作用而達到脫粒目的。能增強沖擊作用，提高生產(chǎn)率和脫凈率，但沖擊過強會使谷粒破碎和損傷。沖擊強度一般是用沖擊速度來衡量的。 二、揉擦脫粒 它是靠谷穗與脫粒原件之間的揉擦以及谷物之間的相互摩擦而使谷粒脫下來的。揉擦力越大脫得越干凈，但這種方法易使谷粒損傷，用它脫水稻最不適宜。 本設(shè)計的工作原理是：將谷物連同莖稈由進料口進入脫粒室，經(jīng)滾筒的沖擊揉搓作用，莖稈伴隨滾筒旋轉(zhuǎn)到出料口，谷物直接掉落在凹板篩上，并落入螺旋輸送器，這樣莖稈自另一側(cè)出料口排出，谷粒由螺旋輸送器從出料口一側(cè)又重新到達進料口一側(cè)，并落入拋揚器，谷粒由拋揚器拋出，從而達到谷物的全喂入脫揚目的。 3. 電動機的選擇 3.1電動機的類型和結(jié)構(gòu) 電動機類型和結(jié)構(gòu)形式要根據(jù)工作條件、電源、載荷特點和轉(zhuǎn)速來確定。對于本設(shè)計的電動機沒有特殊要求，而Y系列電動機適用于不易燃易爆，無腐蝕性場合，故選用Y系列三相異步電動機。 3.2電動機容量的選擇 所選電動機的額定功率應(yīng)大于實際工作的功率以防止過載損壞電動機，更不能小于實際的功率以免造成機器無法正常工作。電動機所需功率計算公式為： （3—1） 其中一一電動機工作時的實際輸出功率 一一工作所需額定功率 一一總傳動效率 的確定： （3—2） 其中F一一工作機阻力 V一一工作機線速度 一一工作機效率 由于傳動效率大于97%，可得工作所需輸入功率為 3.3. 電動機型號的選擇 綜合電動機與傳動裝置的尺寸、重量、價格以及傳動比的特點和大小，選用1500r/min的電動機比較方便，額定功率13KW，滿載功率因素0.88。 4. 滾筒的設(shè)計 4.1.滾筒的型式 圖4—1 滾筒 4.2.滾筒的直徑和轉(zhuǎn)速 滾筒的直徑不可過大，以免工作阻力增大，但滾筒直徑過小則易于纏草。故其最小齒根圓直徑應(yīng)保證齒根圓的周長大于該地區(qū)割下最高桿的桿長。 或 （4—1） 式中： ————滾筒最小的齒根圓直徑 L ————割下作物最高桿的桿長 本設(shè)計過程中為320毫米，由公式得周長1005毫米，符合要求。 再根據(jù)釘齒高度，確定頂圓直徑 （4—2） 釘齒高度取85毫米，所以頂圓直徑為490毫米。 滾筒的速度是影響工作質(zhì)量的重要參數(shù)，當(dāng)滾筒的圓周速度太小時，鋼絲齒對稻穗的沖擊力減弱，需要增長脫粒時間而降低生產(chǎn)率。但如果圓周速度過大，脫粒效率的提高并不明顯，僅使谷粒在滾筒上的跳動加劇，增加谷粒的拋散損失。目前已有的機器上多為8.8—9.4米/秒（齒頂端線速度），電機帶動的機具為11~12.6米/秒。脫粒秈稻時由于秈稻比較容易脫粒，滾筒速度對脫粒效率影響不大顯著。 4.3滾筒齒的形狀和排列 滾筒上的齒用長度為85mm的釘齒，經(jīng)熱處理后表面硬度為HRC55，以提高耐磨性。隨著齒的增長，可以提高脫粒能力，但易帶草，并且消耗的動力多。 各種齒都采用交錯排列，每根齒板條上的齒距一般取50~60毫米。安裝時相鄰兩齒板條上的齒錯開半個齒距。 簡易脫粒機的滾筒都制成開式，即滾筒齒固定在齒板上，然后與滾筒的左右幅板連接，齒板條多為6~12根，本設(shè)計選用6根。 4.4滾筒長度 滾筒長度主要取決于滾筒的傳動動力，單人腳踏板齒輪驅(qū)動的脫粒機滾筒長1000~1100毫米，電動機帶動的脫粒滾筒，考慮到滾筒的結(jié)構(gòu)強度，一般取2000毫米以內(nèi)。本設(shè)計軸流式脫揚機的滾筒長度采用1000毫米。 5. 滾筒釘齒的設(shè)計 5.1滾筒釘齒的形狀 圖5—1 釘齒 釘齒是滾筒的主要脫粒元件，特別是對全喂入式脫粒機構(gòu)來說，谷物進入脫谷室是靠釘齒抓取，谷物進入脫谷室后又是靠釘齒打擊脫粒，莖稈在脫谷室內(nèi)做螺旋軸向轉(zhuǎn)動還是靠釘齒對它施加很高的圓周速度使它沿著蓋板上的螺旋導(dǎo)板運動，直到最后把莖稈逐出機外還是由釘齒完成。 全喂入式脫粒機的釘齒常用的有楔形釘齒、板形釘齒、指形釘齒三種。前兩種脫粒能力較強，適用于切流型脫粒機構(gòu)，指形釘齒脫粒能力弱，適用于全喂入軸流型脫粒機構(gòu)，因為軸流型脫粒流程長，為了減少碎草，采用脫粒能力較弱的指形釘齒。為了避免帶草和提高釘齒在排草口的排草能力，通常釘齒的工作面都有10°~15°的后傾角。 5.2滾筒釘齒的排列 釘齒在滾筒上的排列方式對滾筒的脫粒性能都是有一定的影響的，釘齒的排列應(yīng)考慮到充分發(fā)揮每個釘齒的作用。對于軸流式全喂入脫粒機滾筒，其釘齒排列方式按螺旋線排列比較好這樣即可以充分發(fā)揮每個釘齒的抓取能力有利于連續(xù)均勻喂入，有利于脫粒，又可以在滾筒全長上有較多的齒跡。 滾筒上的釘齒均勻的配置在數(shù)排齒桿上，齒桿的數(shù)量最好是雙數(shù)的，有利于滾筒的平衡，考慮整體的重量、經(jīng)濟性等通常采用六排排列。 相鄰兩個齒跡的距離叫做齒跡距，通常為20~40毫米，齒跡距過小雖然脫粒作用強但是碎矸顯著增多，消耗功率加大。由于本設(shè)計是全喂入式，取偏低值，可取齒跡距為28毫米。 相鄰釘齒的距離也不能過大或者過小，過大脫粒難以干凈，過小消耗功率大，容易纏草，易打碎稻穗，故本設(shè)計選擇65毫米。 6. 凹板篩的設(shè)計 6.1凹板篩型式選擇 對于比較容易脫粒的秈稻，可以不用滾筒凹板，而對難以脫粒的粳稻或脫小麥時，一般都采用滾筒篩式凹板，本設(shè)計采用柵格式凹板篩，這種凹板篩目前應(yīng)用最廣，其優(yōu)點明顯，利于脫粒，分離谷粒能力強，堅固耐用。柵格篩孔用鋼絲和扁鋼組成，篩孔常見的是20×15毫米，長為扁鋼距離寬為鋼絲的間距，本設(shè)計材料采用2~3.5鋼絲及20×3、20×4或20×5扁鋼。 6.2凹板篩包角選擇 包角的大小對谷粒分離效果影響很大，適當(dāng)增加包角可以適當(dāng)縮短滾筒長度，本設(shè)計采用202°包角，這樣可以最好的增加作物揉搓時間，使脫粒更加干凈。 6.3凹板篩間隙確定 全喂入式脫揚機考慮到較好的脫粒和分離能力，凹板篩間隙一般在10~25毫米之間，本設(shè)計選擇20毫米。 7. 滾筒主軸的設(shè)計與校核 7.1.滾筒主軸的形狀 圖7—1 滾筒主軸 7.2.選擇軸的材料 軸的材料主要是碳素鋼和合金鋼，根據(jù)傳動的功率和一些參數(shù)選擇材料，最常用45#鋼，經(jīng)調(diào)質(zhì)處理得到的組織具有良好的綜合力學(xué)性能，有較好的強度，同時兼具較好的塑性和韌性，查表得毛坯直徑≤200毫米，硬度217-255HBS，抗拉強度極限為640MPa,屈服強度極限為355MPa,彎曲疲勞極限為275MPa,剪切疲勞極限為155MPa。 7.3初步確定軸的直徑 軸是機械傳動的重要零件，必須達到足夠的強度，保持良好的穩(wěn)定性，并具有良好的工藝性，根據(jù)軸上零件的定位和固定要求以及加工裝配要求，合理的定出軸的結(jié)構(gòu)外形和全部尺寸。對于軸流式脫揚機的主軸主要是承受扭矩作用，故只需按軸所受的轉(zhuǎn)矩來進行計算。 扭矩強度條件： （7—1） 式中： ————軸的扭轉(zhuǎn)切應(yīng)力 T ————軸所受的扭矩 n ————軸的轉(zhuǎn)速 ————軸的許用扭轉(zhuǎn)切應(yīng)力 ————軸的抗扭截面系數(shù) 由于本設(shè)計的軸采用的是實心軸，由 （7—2） 可得軸的直徑： （7—3） C取決于的大小，C取較小值時取較大值，反之取較小值。根據(jù)軸的材料以及估算結(jié)果，軸直徑取48毫米。 7.4軸的結(jié)構(gòu)設(shè)計 軸主要有軸頸、軸頭、軸身三部分組成，考慮到滾筒主軸主要承受扭矩，故采用圓截面的實心傳動軸，以保持良好的強度，軸長設(shè)計為1400毫米。 7.5軸上零件的周向定位 本設(shè)計主要使用皮帶輪傳動，軸上皮帶輪的周向定位采用平鍵連接，由手冊并根據(jù)輪轂寬度選用14×9×70的平鍵，軸肩高度為0.08×48=3.84毫米。 7.6滾筒主軸的強度校核 7.6.1.對軸進行受力分析并簡化軸的受力 根據(jù)對滾筒主軸上零件的作用力分析，將其上各受力簡化為集中力，其所受力主要為電機的轉(zhuǎn)矩、軸的徑向力以及滾筒的作用力。其受力如圖所示： 圖7—2 滾筒主軸的受力分析圖 7.6.2.計算水平面上的剪切力和彎矩，找出危險截面 剪切力： 彎矩： 剪切圖彎矩圖分別為： 圖7—3 滾筒主軸水平面上的剪切與彎矩圖 7.6.3.計算垂直面上的剪切力和彎矩，并找出危險截面 剪切力： 彎矩： 剪切圖彎矩圖分別為： 圖7—4 滾筒主軸垂直面上的剪切與彎矩圖 7.6.4.計算轉(zhuǎn)矩 由,運用第四強度理論校核滾筒主軸強度，則有： （7—4） 校核結(jié)果： （7—5） 故所受最大力截面安全，該軸符合強度的安全要求。 7.7鍵聯(lián)接的強度強度校核 對于平鍵聯(lián)接，如忽略摩擦，則當(dāng)聯(lián)接傳遞扭矩時鍵軸一體受力較大，可能的失效有：軟弱零件的工作面被壓潰或磨損和鍵的剪斷等。對于實際采用的材料組合和標(biāo)準(zhǔn)尺寸來說，壓潰或磨損常是主要失效形式。因此，通常只作聯(lián)接的擠壓強度或耐磨性計算，但在重要的場合也要驗算鍵的強度。 根據(jù)軸徑查表得b×h=14×9,取L=70，聯(lián)接所能傳遞轉(zhuǎn)矩 （7—6） 其中：———————許用擠壓應(yīng)力 T———————傳遞的扭矩 h———————鍵的高度 l———————鍵的接觸長度 d———————軸的直徑 （7—7） 其中： ———————鍵的許用剪切應(yīng)力 ———————剪切應(yīng)力 經(jīng)校核=4.532MPa;=14.08MPa,故滿足強度要求。 8. 軸承的選用 軸承的作用是支撐軸及軸上的零件，保持軸的旋轉(zhuǎn)精度，減少軸與支撐之間的摩擦和磨損。 本設(shè)計因為軸主要是受軸向和徑向載荷，故選用滾動軸承，相比于滑動軸承，采用滾動軸承的機器起動力矩小，有利于在負載下起動；徑向游隙比較小，運轉(zhuǎn)精度高；可使機器軸向結(jié)構(gòu)緊湊；對于大多數(shù)滾動軸承，軸承組合結(jié)構(gòu)較為簡單；消耗潤滑劑少，便于密封和維護；不需要要用有色金屬；標(biāo)準(zhǔn)化程度高，能成批生產(chǎn)，使用成本低。因此本設(shè)計直接選擇深溝球軸承6000系列。查手冊可得，選軸承尺寸如下：，極限轉(zhuǎn)速12000，軸承代號61908. 參考文獻 [1]宋宜清.我國農(nóng)業(yè)機械化現(xiàn)狀及發(fā)展趨勢[J].農(nóng)業(yè)與技術(shù).2007.2.25~28 [2]李陸俊.中國農(nóng)業(yè)機械化的現(xiàn)狀與發(fā)展趨勢[M]．北京：西北農(nóng)林科技大學(xué)，2001． [3]倫冠德.我國農(nóng)業(yè)機械化現(xiàn)狀及發(fā)展趨勢[J]. 農(nóng)機化研究,2006. 6.17~19 [4]萬鶴群.農(nóng)業(yè)經(jīng)濟結(jié)構(gòu)調(diào)整與農(nóng)業(yè)機械化優(yōu)化[M].北京:科學(xué)技術(shù)出版社. [5]邱宣懷.機械設(shè)計[M].高等教育出版社. [6]劉鴻文.材料力學(xué)[M].高等教育出版社. [7]實用機械設(shè)計手冊[M].中國農(nóng)業(yè)機械出版社. [8]何銘新,錢可強.機械制圖[M].高等教育出版社. [9]于永泗,齊民,等.機械工程材料[M].大連理工大學(xué)出版社. [10]牛淑卿.我國農(nóng)業(yè)機械化發(fā)展的研究[J].農(nóng)機化研究.2006. [11]劉曉娟.我國農(nóng)業(yè)機械化發(fā)展的現(xiàn)狀與對策[J]. 農(nóng)業(yè)科技與裝備.2008. 8.116~118 致謝 本設(shè)計是在嚴霖元教授、吳彥紅教授的悉心指導(dǎo)下，與周鵬坤同學(xué)共同完成的，經(jīng)過接近兩個月的準(zhǔn)備及對實際脫揚機的測繪分析，順利完成了本次設(shè)計。 通過此次畢業(yè)設(shè)計，對大學(xué)四年的課程做了一個系統(tǒng)全面的回顧和深化，并使我更加熟練了對AutoCAD的運用，為以后的工作打下更好的基礎(chǔ)。 本設(shè)計總體上還是合理的，但是由于自身的知識、實踐等有限，還有一些地方有疏漏不足之處，請指導(dǎo)老師不吝指正。 在這次設(shè)計的過程中，我們有幸得到嚴霖元教授和吳彥紅教授的耐心幫助和支持，為我們豐富了設(shè)計的方法，在此對兩位老師表示衷心的感謝，同時在設(shè)計的過程中班里同學(xué)的幫助讓我們能更快更好的完成設(shè)計，在此也一并表示感謝！ 工學(xué)院 王晨 2012年5月13日 systems. assessing the example of three tractors of the same category, which are exploited in climatic and soil conditions 1. Introduction for agricultural agricultural recognized careful technical, predicting ofcropproduction.Nowadays,theexistingmathematicaloptimiza- tion methods, supported by the high-performance computers, can efficiently resolve the optimization problems (Dette Duffy et al., 1994; Mileusnic, 2007; etc.). The formation of an optimal technical system in order to produce cheaper food, highly impacted reliability of tractors, its maintainability, and the functionality of the system. rounding conditions. Although in the same spirit, some authors have defined effectiveness somewhat differently. In (Ebramhimipour maintainabilityascapacityofthe systemforpreventionandfindingfailuresanddamages,forrenewing operating ability and functionality through technical attending and repairs; and functionality as the degree of fulfilling the functional requirements, namely the adjustment to environment, or more pre- cisely to the conditions in which the system operates. In the case of monitoring reliability and maintainability it is common to monitor the time picture of state (Fig. 1) according to their working conditions is obtained. The model can be used as cri- teria for decision making related to any procedure in purchasing, operation or maintenance of the system, for prediction of repair and maintenance costs. Quality and functionality of the proposed model is shown in effectiveness determination of agricultural machinery, precisely tractors. R. Miodragovic et al./Expert Systems with Applications 39 (2012) 89408946 8941 which the functions of reliability and maintainability can be deter- mined, as well as the mean time in operation and the mean time in failure. The main problem that occurs in forming the time picture of state is data monitoring and recording. In real conditions the ma- chines should be connected to information system which would precisely record each failure, duration and procedure of repair. This is usually expensive and improvised monitoring of the machine performance, namely of its shut downs, is imprecise. Moreover, statistical data processing provided by the time picture of the state requires that all machines work under equal conditions, which is difficult to achieve. As for the functionality of the technical system, there is no common way for its measuring and quantification. This is the reason why in this paper, in order to assess the effectiveness, expertise judgments of the employed in the working process of the analyzed machines will be used. Application of expertise judgments has been largely used in literature, primarily for data processing and the assessment of the technical systems in terms of: risk (Li Wang, Yang, Tanasijevic, Ivezic, Ignjatovic, Zadeh, 1996). Application of fuzzy sets today represents one of the most frequently used tools for solving the problems in various areas of optimization (Huang, Gu, Liebowitz, 1988) in general is also used for solving the optimizations problems from area of agro machinery. In article (Rohani, Abbaspour-Fard, and fuzzy composition of men- tioned indicators into one synthesized. Fuzzy proposition is pro- cedure for representing the statement that includes linguistic variables based on available information about considered techni- cal system. In that sense it is necessary to define the names of lin- guistic variables that represent different grades of effectiveness of considered technical system and define the fuzzy sets that describe the mentioned variables. Composition is a model that provides structure of indicators influences to the effectiveness performance. 2.1. Fuzzy model of problem solving The first step in the creation of fuzzy model for effectiveness (E) assessment is defining linguistic variables related to itself and to reliability (R), maintainability (M) and functionality (F). Regarding number of linguistic variables, it can be found that seven is the maximal number of rationally recognizable expressions that hu- man can simultaneously identify (Wang et al., 1995). However, for identification of considered characteristics even the smaller number of variables can be useful because flexibility of fuzzy sets to include transition phenomena as experts judgments commonly is (Ivezic et al., 2008). According to the above, five linguistic vari- ables for representing effectiveness performances are included: poor, adequate, average, good and excellent. Form of these linguis- tic variables is given as appropriate triangular fuzzy sets (Klir .;l 5 R ; l M l 1 M ; .;l 5 M ; l F l 1 F ; .;l 5 F 1 In the next step, maxmin composition is performed on them. Max min composition, also called pessimistic, is often used in fuzzy alge- bra as a synthesis model (Ivezic et al., 2008; Tanasijevic et al., 2011; Wang et al., 1995; Wang 2000). The idea is to make overall assess- ment (E) equal to the partial virtual representative assessment. This assessment is identified as the best possible one between the worst partial grades expected (R, M or F). It can be concluded that all elements of (R, M and F) that make the E have equal influence on E, so that maxmin composition will be used, which in parallel way treats the partial ones onto the h time of planned shut down due to preventive maintenance. 1995) and OR R M F If we tions that is (according to Fig. 2): with 39 (2012) 89408946 Further, for each outcome its values are calculated (X c ). The outcome which would suit the combination c, it would be calcu- lated following the equations: X c P R;M;E j hi c 3 3 Finally, all of these outcomes are treated with maxmin composi- tion, as follows: (i) For each outcome search for the MINimum value of l R,M,F in vector E c (2). The minimum which would suit the combina- tion o, it would be calculated following the equations: MIN 0 minfl j1;.;5 R ;l j1;.;5 M .;l j1;.;5 F g;for all o 1toO 4 (ii) Outcomes are grouped according to their values X c (3), namely the size of j. (iii) Find the MAXimum between previously identified mini- mums (i) for each group (ii) of outcomes. The maximum which would suit value of j, would be calculated following the equations: MAX j maxfMIN o g; for every j 5 E assessment of technical system is obtained in the form: l E This expression (Fig. 2 tion of to fuzzy cedure (d) between the E which d i E j ;H take into account only values if l j1;.;5 R;M;F 0, we get combina- are named outcomes (o =1toO, where O # C). in the process of synthesis, are also used. Precisely, if we look at three partial indicators, namely their membership function (1), it is possible to make C = j 3 =5 3 combina- tions of their membership functions. Each of these combinations represents one possible synthesis effectiveness assessment (E). E l j1;.;5 ;l j1;.;5 ; .;l j1;2;.5 hi ; for all c 1toC 2 maxmin compositions which by using operators AND provide an advantage to certain elements over the others synthetic indicator. In literature (Ivezic et al., 2008; Wang et al., Fig. 2. Effectiveness fuzzy sets. 8942 R. Miodragovic et al./Expert Systems MAX j1 ; .;MAX j5 l 1 E ; .;l 5 E 6 (6) is necessary to map back to the E fuzzy sets ). Best-fit (Wang et al., 1995), method is used for transforma- E description (6) to form that defines grade of membership sets: poor, adequate, average, good and excellent. This pro- is recognized as identification. Best-fit method uses distance E obtained by maxmin composition (6) and each of expressions (according to Fig. 2), to represent the degree to E is confirmed to each of fuzzy sets of effectiveness (Fig. 2). i X 5 j1 l j E C0l j H j 2 v u u t ; j 1; .;5;H i fexcellent;goodaverage;adequate;poorg7 E i fb i1 ;poor;b i2 ;adequate;b i3 ;good; b i4 ;average;b i5 ;excellentg 10 3. An illustrative example As an illustrative example of evaluation of agriculture machin- ery effectiveness, the comparative analysis of three tractors A 1 B 2 , and C 2 is given in this article. In tractor A a 7.146 l engine LO4V TCD 2013 is installed. Thanks to the reserves of torque from 35%, the tractor is able to meet all the requirements expected in the worst performing farming oper- ations in agriculture. Total tractor mass is 16,000 kg. According to OECD (CODE II) report maximum power measured at the PTO shaft is 243 kW at 2200 rpm with specific fuel consumption of 198 g/kW h (ECE-R24). Maximum engine torque is 1482 Nm at en- gine regime of 1450 rpm. Transmission gear is vario continious transmision. Linkage mechanism is a Category II/III with lifting force of 11,800 daN. In tractors B 2 and C 2 8.134 l engine 6081HRW37 JD is installed, with reserve torque of 40%, and this tractor was able to meet all the requirements expected in the worst performance of the farming operations in agriculture. Total tractor weight is 14,000 kg. Accord- ing to OECD (CODE II) report maximum power measured at the PTO shaft is 217 kW at 2002 rpm with specific fuel consumption of 193 g/kW h (ECE-R24). Maximum torque is 1320 Nm at engine revs of 1400 rpm. Transmission is AutoPower. Linkage mechanism is a Category II/III with lifting force of 10,790 daN. Both models have electronically controlled tractor engine and fuel supply system that meets the regulations on emissions. From the submitted technical characteristics of the tractor A, B and C it is seen that all three tractors are fully functional for l exc. = (0,0,0,0.25,1); l good = (0,0,0.25,1,0.25); l aver. = (0,0.25,1,0.25,0); l adeq. = (0.25,1,0.25,0,0); l poor = (1,0.25,0,0,0). The closer l E (6) is to the ith linguistic variable, the smaller d i is. Distance d i is equal to zero, if l E (6) is just the same as the ith expression in terms of the membership functions. In such a case, E should not be evaluated to other expressions at all, due to the exclusiveness of these expressions. Suppose d imin (i =1,.,5) is the smallest among the obtained distances for E j and leta 1 ,.,a 5 represent the reciprocals of the rel- ative distances (which is calculated as the ratio between corres- ponding distance d i (7) and the mentioned values d imin ). Then, a i can be defined as follows: a i 1 d i =d imin ; i 1; .;5 8 If d i = 0 it follows that a i = 1 and the others are equal to zero. Then, a i can be normalized by: b i a j P 5 m1 a im ; i 1; .;5 X 5 i1 b i 1 9 Each b i represents the extent to which E belongs to the ith defined E expressions. It can be noted that if E i completely belongs to the ith expression then b i is equal to 1 and the others are equal to 0. Thus b j could be viewed as a degree of confidence that E i belongs to the ith E expressions. Final expression for E performance at the level of tech- nical system, have been obtained in the form (10) where Applications 1 Tractor Fendt Vario 936. 2 Tractor John Deere 8520. performing difficult operations for different technologies of agri- cultural production. Tractors B and C have the same technical char- acteristics, and practice is the same type and model, except that the tractor B entered into operation in May 2007, a tractor C in June 2007. A tractor on the experimental farm, which is the technical documentation for the base model, comes into operation in July 2009. The main task of maintaining agricultural techniques is to provide functionality and reliability of machines. Maintenance of all three tractors is done by machine shop owned by the user up- grade option. Ten engineers (analysts) working on maintenance and opera- tion of tractors were interviewed. Their evaluation of R, D and F are given in Table 1. First, the effectiveness of tractor A is calculated. It can be seen that the reliability was assessed as excellent by six out of ten ana- lysts (6/10 = 0.6), as average by three (0.3) and as good by one (0.1). In this way the assessment R is obtained in the form (11): R 0:6=exc; 0:3=good; 0:1=aver; 0=adeq; 0=poor11 In the same way the assessments for M and F are obtained: M 0:4=exc; 0:4=good; 0:2=aver; 0=adeq; 0=poor F 0:5=exc; 0:5=good; 0=aver; 0=adeq; 0=poor In the next step, these assessments are mapped on fuzzy sets (Fig. 1) in order to obtain assessment in the form (1). For example, Reliabil- ity in this example is determined as (11), where it is to linguistic variable excellent joined weight 0.6. Thereby, fuzzy set excellent is defined as: R exc = (1/0, 2/0, 3/0, 4/0.25, 5/1.0) (according to Fig. 1). In this way the specific values of fuzzy set excellent R exc0.6 = (1/(0 C2 0.6), 2/(0 C2 0.6), 3/(0 C2 0.6), 4/(0.25 C2 0.6), 5/(1.0 C2 0.6) are obtained. The remaining four linguistic variables are treated in the same way. In the end for each j =1,.,5 specific membership functions (last row, Table 2) are added into the final fuzzy form (1) of tractor A reliability: l RA 0;0:025;0:175;0:475;0:675 In the same way, based on the questionnaire (Table 1) values for maintainability and functionality are obtained: l MA 0;0:05;0:3;0:55;0:5; l FA 0;0;0:125;0:625;0:62512 These fuzzificated assessments (11) and (12) are necessary to syn- thesize into assessment of effectiveness, using maxmin logics. In this case it is possible to make C =5 3 = 125 combinations, out of which the 48 outcomes. First outcome would be for combination 2-2-3: E 2-2-3 = 0.025,0.05,0.125, where is X 2-2-3 = (2 + 2 + 3)/3 = 2 (rounded as integer). Smallest value among the membership func- tions of this outcome is 0.025. Other outcomes and corresponding values of X c are shown in Table 3. All these outcomes can be grouped around sizes X = 2, 3, 4 and 5. For example, for outcome X = 5 it can be written: E 4C05C05 0:475;0:5;0:625C138;E 5C04C05 0:675;0:55;0:625C138;E 5C05C04 0:675;0:5;0:625C138;E 5C05C05 0:675;0:5;0:625C138 Further, for each of them, minimum between membership function is sought: Table 1 Results of questionnaire. Average x x xx x xx x R. Miodragovic et al./Expert Systems with Applications 39 (2012) 89408946 8943 Analyst Linguistic variables Tractor A Tractor B Excellent Good Average Adequate Poor Excellent Good 1R x x Mx x Fxxx 2R x Mx x Fx 3R x x Mx Fx 4R x x Mx Fx x 5R x x Mx Fxxx 6R x x Mx Fx x 7R x Mx Fx 8R x x Mx x Fx x 9R x x Mx x Fx x 10 R x x Mx x Fx x Tractor C Adequate Poor Excellent Good Average Adequate Poor x x x x x x x x x x x xx x x x x x x x x x with Table 2 Calculation of specific values of fuzzy sets. 12345 0.6/exc. 0 C2 0.6 0 C2 0.6 0 C2 0.6 0.25 C2 0.6 1.0 C2 0.6 0.3/good 0 C2 0.3 0 C2 0.3 0.25 C2 0.3 1.0 C2 0.3 0.25 C2 0.3 8944 R. Miodragovic et al./Expert Systems MINE 4C05C05 minf0:475;0:5;0:625g0:475;MINE 5C04C05 0:55;MINE 5C05C04 0:5;MINE 5C05C05 0:5 Between these minimums, in the end it seeks maximum: MAXX d5 maxf0:475;0:55;0:5;0:5g0:55 Also for other values: X: MAX X =2 = 0.025; MAX X =3 = 0.175; MAX X =4 = 0.55 (Table 1.) 0.1/aver. 0 C2 0.1 0.25 C2 0.1 1.0 C2 0.1 0.25 C2 0.1 0 C2 0.1 0/adeq. 0.25 C2 0 1.0 C2 0 0.25 C2 00C2 00C2 0 0/poor 1.0 C2 0 0.25 C2 00C2 C2 C2 0 P R 0 0.025 0.175 0.475 0.675 Table 3 Structure of MAXMIN composition. Comb. X l MIN 2345 2-2-3 2 0.025,0.05,0.125 0.025 2-2-4 3 0.025,0.05,0.625 0.025 2-2-5 3 0.025,0.05,0.625 0.025 2-3-3 3 0.025,0.3,0.125 0.025 2-3-4 3 0.025,0.3,0.625 0.025 2-3-5 3 0.025,0.3,0.625 0.025 2-4-3 3 0.025,0.55,0.125 0.025 2-4-4 3 0.025,0.55,0.625 0.025 2-4-5 4 0.025,0.55,0.625 0.025 2-5-3 3 0.025,0.5,0.125 0.025 2-5-4 4 0.025,0.5,0.625 0.025 2-5-5 4 0.025,0.5,0.625 0.025 3-2-3 3 0.175,0.05,0.125 0.05 3-2-4 3 0.175,0.05,0.625 0.05 3-2-5 3 0.175,0.05,0.625 0.05 3-3-3 3 0.175,0.3,0.125 0.125 3-3-4 3 0.175,0.3,0.625 0.175 3-3-5 4 0.175,0.3,0.625 0 0.175 3-4-3 3 0.175,0.55,0.125 0.125 3-4-4 4 0.175,0.55,0.625 0.175 3-4-5 4 0.175,0.55,0.625 0.175 3-5-3 4 0.175,0.5,0.125 0.125 3-5-4 4 0.175,0.5,0.625 0.175 3-5-5 4 0.175,0.5,0.625 0.175 4-2-3 3 0.475,0.05,0.125 0.05 4-2-4 3 0.475,0.05,0.625 0.05 4-2-5 4 0.475,0.05,0.625 0.05 4-3-3 3 0.475,0.3,0.125 0.125 4-3-4 4 0.475,0.3,0.625 0.3 4-3-5 4 0.475,0.3,0.625 0.3 4-4-3 4 0.475,0.55,0.125 0.125 4-4-4 4 0.475,0.55,0.625 0.475 4-4-5 4 0.475,0.55,0.625 0.475 4-5-3 4 0.475,0.5,0.125 0.125 4-5-4 4 0.475,0.5,0.625 0.475 4-5-5 5 0.475,0.5,0.625 0.475 5-2-3 3 0.675,0.05,0.125 0.05 5-2-4 4 0.675,0.05,0.625 0.05 5-2-5 4 0.675,0.05,0.625 0.05 5-3-3 4 0.675,0.3,0.125 0.125 5-3-4 4 0.675,0.3,0.625 0.3 5-3-5 4 0.675,0.3,0.625 0.3 5-4-3 4 0.675,0.55,0.125 0.125 5-4-4 4 0.675,0.55,0.625 0.55 5-4-5 5 0.675,0.55,0.625 0.55 5-5-3 4 0.675,0.5,0.125 0.125 5-5-4 5 0.675,0.5,0.625 0.5 5-5-5 5 0.675,0.5,0.625 0.5 MAX 0.025 0.175 0.55 0.55 Finally, we get expression for membership function of effective- ness of tractor A: l EA 0;0:025;0:175;0:55;0:55 Best-fit method (79) and proposed E fuzzy set (Fig. 1) give the final effectiveness assessment for the tractor A: d 1 E;exc X 5 j1 l j E C0l j exc 2 v u u t 0C00 2 0:025C00 2 0:175C00 2 0:55C00:25 2 0:55C01 2 q 0:56899 where is : l E 0;0:025;0:175;0:55;0:55 l exc 0;0;0;0:25;1 For other fuzzy sets: d 2 (E, good) = 0.54658, d 3 (E, aver) = 1.06007, d 4 (E, adeq) = 1.27426, d 5 (E, poor) = 1.29856. for d min d 2 : a 1 1 d 1 =d 2 1 0:56899=0:54658 0:96061; a 2 1:00000;a 3 0:51561;a 4 0:42894;a 5 0:42091: b 1 a 1 P 5 i1 a i 0:96901 0:96901 1 0:51561 0:42894 0:42091 0:28881; b 2 0:30065;b 3 0:15502;b 4 0:12896;b 5 0:12655: Finally, we get the assessment of effectiveness of tractor A, in form (10): E A =(b 1 , excellent), (b 2 , good), (b 3 , average), (b 4 , ade- quate), (b 5 , poor) = (0.28881, excellent), (0.30065, good), (0.15502, average), (0.12896, adequate), (0.12655, poor) In the same way, we get the assessments for other two tractors B and C: E B = (0.23793, excellent), (0.27538, good), (0.20635, aver- age), (0.14693, adequate), (0.13342, poor) E C = (0.17507, excellent), (0.25092, good), (0.25468, aver- age), (0.17633, adequate), (0.14300, poor). Tractor A is in great extent of 0.30065 (in relation to 30 %) as- sessed as good, tractor B in great extent of 0.27538 (27.5%) as- Applications 39 (2012) 89408946 sessed as good, while tractor C is in great extent of 0.25468 (25.5%) assessed as average. It can be concluded that C is the worst, while tractor A is only somewhat better than B, especially if we see with that A is assessed as excellent in the extent of 28.8% while B in the extent of 23.8%. Effectiveness of analyzed tractors can be presented as in Fig. 3., where it can be more clearly seen that tractor A has the biggest effectiveness. If this assessment (E A , E B , E C ) is defuzzificated by center of mass point calculation Z (Bowles if calculated on 10,000 moto-hours, Fig. 3. Relationship of effectiveness of observed tractors. R. Miodragovic et al./Expert Systems it would spend in work 9244 moto-hours. As of the tractor B, out of 10,004 available moto-hours, it spent 9069 moto-hours in work, and tractor C out of 9981 available moto-hours spent 9045 in work. The experiment showed that the more reliable and efficient tractors are the less frequent are delays. In part, this initial advan- tage wiped out worse logistics of delivery of spare parts when it comes to tractor A. in 1100 moto-hours work of the tractor, due to poor logistics in maintaining hoped to eight working days, and it greatly influenced the decline in benefits of maintainability of a given tractor and thus the decline in total exploitation of the same efficiency (Internal technical documentation PKB). 4. Conclusion This paper presents a model for effectiveness assessment of technical systems, precisely agricultural machinery, based on fuzzy sets theory. Effectiveness performance has been adopted as overall indicator of systems quality of service, i.e. as entire measure of technical system availability. Reliability, maintainability and func- tionality performances have been recognized as effectiveness parameters or indicators. Linguistic form can be appointed as the References Bowles, J. B., & Pelaez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering and System Safety, 50(2), 203213. Cai, K. Y. (1996).