家用多功能和面機的設計【雙葉片攪拌和面容量為10kg】

家用多功能和面機的設計【雙葉片攪拌和面容量為10kg】,雙葉片攪拌,和面容量為10kg,家用多功能和面機的設計【雙葉片攪拌,和面容量為10kg】,家用,多功能,和面,設計,葉片,攪拌,容量,10,kg 家用多功能和面機盧佳敏工學院農(nóng)業(yè)機械化及其自動化專業(yè)2014年5月中國是世界上最大的面制品生產(chǎn)國，而和面機是其生產(chǎn)加工的重要設備其性能的好壞、結構的正誤。將直接影響食品的營養(yǎng)、感官等質(zhì)量指標，進而影響著企業(yè)的經(jīng)濟效益和社會效益。隨著中國綜合實力的躍升，消費者對食品的質(zhì)量前所未有的關注，這也就對和面機市場形成一種倒逼驅(qū)動力，只有不斷研發(fā)出更好的和面機，才能立足于市場。隨著人們生活水平的不斷提高，越來越多的人喜歡自己在家里制作面點，不僅僅是為了滿足自己的食欲，更是一種精神的上的享受。但是揉面團是個費力又費時的事，因此家用和面機應運而生。目錄1234總裝圖翻缸卸料裝置部件圖攪拌傳動系統(tǒng)部件圖攪拌裝置5支架總裝圖翻缸裝置攪拌傳動系統(tǒng)攪拌裝置支架翻缸卸料裝置部件圖卸料方式主要有底部卸料和翻缸卸料，該設計和面量較小，適合采用電動翻缸卸料攪拌傳動系統(tǒng)部件圖攪拌傳動形式：選用三相異步電機，通過帶輪、兩級齒輪傳動，帶動攪拌器旋轉(zhuǎn)。攪拌裝置攪拌器也稱作攪拌槳，是和面機最重要的部件。按攪拌軸數(shù)目分，有單軸式和雙軸式兩種。臥式的與立式的也有所不同。本次設計的絞龍既適用和面粉，也可以用于打年糕，以及打雞蛋等支架機架采用角鋼，底板用10mm鋼板。尺寸依結構而定，焊接方式連接。小結本次設計，針對家用多功能和面機的結構特點，詳細設計了其攪拌裝置、攪拌傳動系統(tǒng)、卸料傳動系統(tǒng)。在不斷的發(fā)現(xiàn)問題和解決問題的過程中，收獲頗多。在此過程中，大學所學知識得到了淬煉，繁雜而無趣的知識趨于系統(tǒng)。在設計思路和方法的掌握上，開始有了個雛形，知道面對一個項目該如何去查閱材料，怎樣去把握結構、確定方案。在日后的工作中，本次設計所得經(jīng)驗與成果，經(jīng)過不斷完善后將會為我人生一個寶貴的財富。在思維上，對于專業(yè)的問題思路更加趨于嚴謹。也能體會到嚴謹?shù)乃季S對于設計人員的重要性。可能一個考慮不周就會將整個設計推翻，一切都得從頭再來。謝謝！ JIANGXI AGRICULTURAL UNIVERSITY本 科 畢 業(yè) 論 文（設 計）題目： 家用多功能和面機設計 學 院： 工 學 院 姓 名： 盧佳敏 學 號： 20100998 專 業(yè)： 農(nóng)業(yè)機械化及其自動化 年 級： 農(nóng)機101班 指導教師： 蔡金平 職稱：講 師 二 0 一 四 年 五 月摘 要 和面機的實質(zhì)作用就是進行水和面粉的攪拌混合，主要用于年糕、餃子皮、蛋糕、膨化食品、面條、餛飩皮等面食的加工，揉面團的好壞直接影響了面食的口感。本設計中和面機采用雙葉片攪拌，和面容量為10kg，適合調(diào)制各種面團，攪拌裝置選擇了雙葉片攪拌器，由主電機帶動帶輪，通過齒輪減速傳動給攪拌軸。和面完成后打開步進電機，方便取出面團。本和面機結構簡單，用途廣泛，大到農(nóng)村人家打年糕，小到三口之家包餃子。關鍵詞：絞龍；多功能；和面機 AbstractMixer is the essence of the action of water and flour, mixing is mainly used for rice cake, the wrappers, cakes, puffed food, noodles, wonton skins, such as pasta processing, knead the dough quality directly affect the taste of the pasta.In this design, including the working principle of mixer, its parts design and the technical requirements for design of mixer, and the detailed design and 10 kg/mixer, including mixing device, transmission system, feeding system and draw the mixer equipment assembly diagram, parts diagram and component diagram.Design model with double mixing blades, a powder capacity of 10 kg, suitable for all kinds of modulation dough.The reference to the design process and T - 66 type horizontal mixer, mainly characterized by mixing system chose double vane stirrer;Mixing system chooses three-phase asynchronous motor speed reducer, with belt wheel and two stage gear transmission;Choose stepper motor speed reducer, double cylinder system adopts with worm gear and worm drive and gear transmission.Mixer has simple structure, wide range of USES, big to the somebody else dozen rice cakes in the countryside, small to a family of three pack dumpling.Key words: heave the dragon;multi-function;doughII目 錄摘 要IIAbstractIII目 錄V1 緒論11.1 本課題的研究目的11.2 我國的和面機市場11.3 本課題應達到的要求12 和面機初步設計12.1 設計準備12.1.1 和面的基本過程12.1.2 和面機各部件設計12.2 家用多功能和面機設計22.2.1 確定和面機容量22.2.2 總方案的設計32.2.3 攪拌裝置的設計42.2.4 攪拌軸傳動系統(tǒng)的設計62.2.5 取料裝置的設計212.2.6 支架的設計282.2.7 家用和面機裝配圖283 小結30參考文獻31致謝32家用多功能和面機1 緒論1.1 本課題的研究目的和面機的實質(zhì)作用就是進行水和面粉的攪拌混合，主要用于年糕、餃子皮、蛋糕、膨化食品、面條、餛飩皮等面食的加工，揉面團的好壞直接影響了面食的口感。和面機在實際使用過程中，常常根據(jù)需要人為控制，選擇不同的攪拌速度、攪拌裝置形狀，才能讓面團的質(zhì)量得到保證。本課題的任務是設計一臺具有多功能的家用和面機，主要適用于加工小面團。1.2 我國的和面機市場現(xiàn)如今我國的和面機種類繁多，有大型食品工廠用的，有小型家庭用的。面食自古就是我們中國人比較喜愛的食物。但是揉面不是每個中國人都能掌握的一項技術，所以對于現(xiàn)在的我們吃一口家常面點變的越來越難了。究其原因，大部分人都是被和面這第一步難倒了。我們的家用多功能和面機就應運而生。1.3 本課題應達到的要求 本課題的任務是設計一臺具有多功能的小型和面機，為小規(guī)模食品加工帶來便利和效益。2 和面機初步設計2.1 設計準備2.1.1 和面的基本過程和面機調(diào)制面團的基本過程是我們先添加一定量的面粉（以和面為例），然后加入適量水（根據(jù)我們需要的面團用途），啟動和面機。攪拌軸旋轉(zhuǎn)進行攪拌混合，然后面粉和水分慢慢的混合，直到兩者混合均勻，面團圓滑。步進電機反轉(zhuǎn)，翻缸，取出面團，和面完成。2.1.2 和面機各部件設計 和面機功能介紹：功能多樣，用途廣泛，可以用來和面、打年糕、打雞蛋等。1、攪拌器 攪拌器也叫攪拌槳，是和面機最重要的部件。按攪拌軸數(shù)目分，有單軸式和雙軸式兩種。臥式的與立式的也有所不同。本次設計的絞龍既適用和面粉，也可以用于打年糕，以及打雞蛋等。2、攪拌容器容器多由不銹鋼焊接而成。容器的容積由和面機的適用的面團大小決定。為了防止軸承處物料或潤滑油泄漏，污染食品，容器與攪拌軸之間必須密封。因為和面機軸的轉(zhuǎn)速普遍低，工作載荷變化又大，密封處間隙變化無常，本設計的密封裝置采用了應J型無滑架橡膠密封圈。當然也可以采用空氣端面密封裝置，密封效果更完美。本設計采用機動翻轉(zhuǎn)容器，由電動機，減速器及容器翻轉(zhuǎn)齒輪組成。和面完成后，打開步進電機，通過蝸輪蝸桿、減速器帶動與容器固接的齒輪轉(zhuǎn)動，使容器翻轉(zhuǎn)一定的角度，方便使用者取出面團。當攪拌軸攪拌時通過蝸輪蝸桿的自鎖原理將其鎖死。3、機架小型和面機攪拌軸轉(zhuǎn)速低，工作阻力大，振動不會很劇烈，噪音也不會很大。本設計的機架結構采用焊接結構。4、傳動裝置本和面機的傳動是由電動機，皮帶輪，減速器及聯(lián)軸器等組成。和面機工作轉(zhuǎn)速低，多為25-35r/min，所以應選用較大的減速比。本設計采用行星輪減速器，其傳動效率高，結構緊湊。表2-1 和面機容量與所配電機額定功率的關系生產(chǎn)力（/次）12255075100主電機 （千瓦）1.12.23.04.05.52.2 家用多功能和面機設計2.2.1 確定和面機容量一般選5/次、10/次、15/次。設計10kg/次，和面時間為10min。2.2.2 總方案的設計1、攪拌容器的總體尺寸攪拌時,攪拌容器的裝料系數(shù)可取0.5-0.6查有關資料可得我國面粉的吸水系數(shù)為15%一般面粉的密度為0.5g/ml, 和面過程中加40%的水（以和面為例），水的密度為1g/ml。面粉的質(zhì)量：10kg 水的質(zhì)量：4kg計算得總體積為：10000/0.5+4000/170000mm3所以取攪拌容器容積為200000mm3（1）寬度（B） B = 2 ( R+) 式中 R = 攪拌漿半徑 R的大小取決于面團性質(zhì)及生產(chǎn)（2.1） = 漿葉與容器的間隙 取為5mm =5 mm B=2（R5）（2） 高度（ H ）H = h +B 式中 h=(0.5-1)R 取h=0.5R （2.2） （3）長度（ L ） L = (2-2.5)R （2.3） 攪拌容器的體積為V= （2.4） 經(jīng)計算可取R=25cm 取 B=50cm H=66.5cm L=75cm 選擇本設計的攪拌容器材料為1Ge18Ni9Ti 2、攪拌漿的選擇本設計選擇的攪拌器,攪拌軸上設置2個攪拌葉片，攪拌葉片是兩片對稱固定在攪拌軸上的弧形葉片，葉片與攪拌軸旋轉(zhuǎn)1/4圓連接，主要適用于加工小型面團。這種攪拌裝置適合多種作業(yè)，比如和面團，打雞蛋以及做蛋糕等。攪拌均勻，攪拌效率高。與容器壁間距1-2cm,選擇軸半徑為d=23cm。 3、攪拌傳動的選擇 選用三相異步電機，通過帶輪、兩級齒輪傳動，帶動攪拌裝置旋轉(zhuǎn)。 4、卸料形式本設計和面量較小，因此選擇電動翻缸的方式。系統(tǒng)副電動機安裝在機座上，通過蝸桿、蝸輪、齒輪傳動帶動筒體傳動，和面過程中利用蝸輪蝸桿自鎖效應，控制旋轉(zhuǎn)角度。2.2.3 攪拌裝置設計1、攪拌容器設計容器壁設計為3mm，兩側連接軸承支承處設置加強圈，厚度為2mm，每處兩個，采用焊接結構，選擇本設計的攪拌容器材料為1Ge18Ni9Ti。結構如圖2.1所示。圖2.1攪拌容器具體尺寸及參數(shù)要求見設計圖紙2、攪拌器設計設計攪拌葉片寬度為50mm，厚度為4mm，徑向旋轉(zhuǎn)角度為1/4圓，軸向長度為450mm。攪拌葉片厚度設計為10mm,根部寬度為50mm，頂部寬度為20mm。為輪轂內(nèi)徑85mm，外徑為100mm，寬度為40mm。攪拌葉片、輪轂采用焊接方式連接，材料選用1Ge18Ni9Ti。結構簡圖如圖2.2。具體參數(shù)見設計圖紙。 圖2.2 攪拌器結構簡圖3、攪拌裝置支座設計材料取為鑄鐵HT150，基本結構如圖2.3圖2.3攪拌裝置支座具體尺寸、參數(shù)見設計圖紙4、其它零件設計材料取為45鋼，攪拌軸左、右支撐，如圖2.4圖2.4攪拌軸支撐左、右蓋裝配關系見裝配圖5、 攪拌系統(tǒng)部件圖圖2.14攪拌裝置部件圖2.2.4 攪拌軸傳動系統(tǒng)的設計1、主電機選擇表2-2 和面機容量與所配電機額定功率的關系生產(chǎn)力（/次）12255075100主電機 （千瓦）1.12.23.04.05.5根據(jù)表格2-2中和面機容量與所配電機額定功率的關系，選用3kw的電機，查設計手冊選用通用異步電機Y132s-6型號，轉(zhuǎn)速960r/s。2、傳動系統(tǒng)中傳動鏈的設計及各傳動比的分配設計 (1)攪拌漿轉(zhuǎn)速 n=1435 rpm 各種面團所需的轉(zhuǎn)速不同 現(xiàn)取n20rpm。(2)電機轉(zhuǎn)速 = 960rpm 同步轉(zhuǎn)速 (3)傳動鏈總傳動比 =48 （2.5） (4)傳動比分配：皮帶傳動 3 取=3 齒輪傳動 =2，=8 3、皮帶傳動設計 (1)電機功率p=3kw, =1.1 ,計算功率=p=3.3kw （2.6）(2)選擇v帶型號 根據(jù)=3.3kw，n1=960r/min，查圖得選用A型v帶(3)求大、小帶輪基準直徑、 取=100mm 驗算帶速v=5.02 （2.7） 55.0230,符合要求 =300 （2.8） 由i=3 得n2 =320r/min 圓整后得=315mm 求v帶基準長度Ld和中心距a 由0.7（+ ）2（+ ） 取=450mm 帶長=2+（+ ）/2 + （）2/（4）=1577.2mm （2.9） 由長度系列表查得選用帶長=1600mm 計算實際中心距 a=+=4500+（1600-1577.2）/2=461 （2.10） 驗算小帶輪包角 （2.11） =1800-（）/a57.30 =153.80900 合適 求V帶根數(shù)z 計算單根v帶的額定功率 查表8-4a,=0.97kw 查表8-4b,=0.12kw 查表8-5,=0.93 查表8-2,=0.99 =（+）=1.00kw (2.12) 由此可得 Z=3.3 (2.13) 取4根 材料的選擇 帶輪材料取為鑄鐵HT150 ，V帶由復合材料抗拉體，頂膠，底膠和包布做成。 結構 小帶輪采用實心式結構，如圖2.5所示。圖2.5小帶輪 大帶輪采用橢圓輪輻式結構，如圖2.6所示。圖2.6 大帶輪 具體結構參數(shù)、尺寸見設計圖紙4、齒輪傳動設計 采用兩級齒輪傳動 =2，=8(1) 一級齒輪傳動設計 p=3kw =320r/min =21) 選精度等級、齒輪類型、材料、齒數(shù) 直齒輪傳動 一般工作機器，速度不高，故選用7級精度材料 大齒輪硬度為240HBS的45鋼 小齒輪硬度為280HBS的40cr鋼選小齒輪齒數(shù)=24 大齒輪齒數(shù)=48 2) （2.14）選載荷系數(shù)=1.3小齒輪傳遞轉(zhuǎn)矩 =8.952N （2.15）由表10-7，取=1 由表10-6，材料彈性影響系數(shù)=189.8 由圖10-21，按硬度查得7 小齒輪接觸疲勞強度極限=600MPa 大齒輪接觸疲勞強度極限=550MPa應力循環(huán)次數(shù) =1.382 （2.16） =0.6912由圖10-19，接觸疲勞壽命系數(shù) =0.9 =0.95計算接觸疲取勞許用應力 取失效概率為1% 安全系數(shù)為s=1 =0.9600=540MPa （2.17） 0.95550=522.5MPa 3) =66.01mm圓周速度v=1.105m/s （2.18）齒寬b=66.01mm （2.19）齒寬與齒高之比 （2.20） 模數(shù)=2.75 （2.21） 齒高h=2.25=6.19 =10.66載荷系數(shù) =0.675 =1 =1 =1.315 =1.24 k=0.8870 （2.22）分度圓直徑=58.113mm計算模 =2.414) 根據(jù)齒根彎曲強度計算設計 確定參數(shù) 小齒輪彎曲強度極限=500MPa 大齒輪彎曲強度極限=380MPa 彎曲疲勞壽命系數(shù)=0.85 =0.88 取彎曲疲勞安全系數(shù)s=1.4 彎曲許用應力 （2.23） k=10.67511.24=0.837 齒形系數(shù) =2.65 =2.33 應力校正系數(shù) =1.58 =1.69計算比較 =0.01379 =0.1649 大齒輪值較大5) 設計計算 =1.625mm （2.24） 圓整后為2.0mm =58.113mm =29 =229=586) 幾何尺寸計算 分度圓直徑 中心距a=87mm 齒寬 =58mm 取 （2.25）7) 結構設計 小齒輪因直徑較小，齒根圓到鍵槽底部的距離e2m,故采用齒輪軸形勢，如圖2.7所示圖2.7 一級齒輪軸 大齒輪采用實心結構，如圖2.8所示圖2.8一級大齒輪 具體尺寸、參數(shù)見設計圖紙 5、二級齒輪傳動設計6 使用最大功率3kw計算 p=3kw =160r/min =8 1) 選精度等級、齒輪類型、材料、齒數(shù)直齒輪傳動一般工作機器，速度不高，故選用7級精度材料 大齒輪硬度為240HBS的45鋼 小齒輪硬度為280HBS的40cr鋼選小齒輪齒數(shù)=20 大齒輪齒數(shù)=1602) 選載荷系數(shù)=1.3小齒輪傳遞轉(zhuǎn)矩 =1.79N由表10-7，取=0.5由表10-6，材料彈性影響系數(shù)=189.8由圖10-21，按硬度查得 小齒輪接觸疲勞強度極限=600MPa 大齒輪接觸疲勞強度極限=550MPa應力循環(huán)次數(shù) =6.912 =0.864由圖10-19，接觸疲勞壽命系數(shù) =0.9 =0.93計算接觸疲取勞許用應力 取失效概率為1% 安全系數(shù)為s=1 =0.9600=540MPa 0.95550=522.5MPa3) =75.5mm 圓周速度v=0.6322m/s齒寬b=75.5mm齒寬與齒高之比 模數(shù)=3.775mm 齒高h=2.25=8.494mm =8.89載荷系數(shù) =1.02 =1 =1 =1.425 =1.375 k=1.4535分度圓直徑=79.83mm計算模 =4mm4) 根據(jù)齒根彎曲強度計算設計 確定參數(shù)小齒輪彎曲強度極限=500MPa 大齒輪彎曲強度極限=380MPa彎曲疲勞壽命系數(shù)=0.85 =0.88取彎曲疲勞安全系數(shù)s=1.4 彎曲許用應力 k=1.4025齒形系數(shù) =2.8 =2.138 應力校正系數(shù) =1.55 =1.835計算比較 =0.01430 =0.01642 大齒輪值較大5) 設計計算 =2.7418mm 圓整后為3.2mm =79.83mm =25 =825=2006) 分度圓直徑 中心距a=(80+640)/2=360mm 齒寬 =158=80mm 取 7) 結構設計9 小齒輪采用實心結構，如圖2.8所示圖2.8二級小齒輪 大齒輪400mm1000,采用輪輻式結構，如圖2.9所示圖2.9 二級大齒輪 具體結構參數(shù)、尺寸見設計圖紙 6、傳動軸設計1) 一級傳動軸 V型皮帶傳動的效率為0.96，電機功率為3kw，轉(zhuǎn)速320r/s， 確定軸的最小直徑 =21mm 取最小處直徑為24mm。 軸的結構設計，如圖2.10所示圖2.10 一級傳動軸 未注倒角為C1，未注圓角為R1.5軸材料選用45鋼按扭矩校核軸的強度=31MPa=45MPa, 因此,故安全2) 二級傳動軸 V型皮帶傳動的效率為0.96，齒輪傳動效率為0.97轉(zhuǎn)速160r/s，電機功率為3kw 確定軸的最小直徑 =26mm 取最小處直徑為30mm。 軸的結構設計，如圖2.11所示圖2.11 二級傳動軸 未注倒角為C1.5，未注圓角為R1.5 軸材料選用45鋼 按扭矩校核軸的強度=30.87MPa=45MPa, 因此,故安全3) 攪拌軸 V型皮帶傳動的效率為0.96，齒輪傳動效率為0.97轉(zhuǎn)速20r/s，電機功率為3kw 確定軸的最小直徑，取=0.75 =58.3mm 取最小處直徑為80mm。 軸的結構設計，如圖2.12所示圖2.12 攪拌軸未注倒角為C2，未注圓角為R2軸材料選用1Cr18Ni9Ti按扭矩校核軸的強度=955000030.960.97/(1600.68360.2303030)=23.1MPa=35MPa, 因此,故安全7、軸承座設計6 材料取為鑄鐵HT150，基本結構如圖2.13所示圖2.13 軸承座具體尺寸、參數(shù)見設計圖紙8、攪拌傳動部件圖2.14所示圖2.14 攪拌傳動部件圖2.2.5 取料裝置設計一級減速采用蝸桿傳動二級減速采用齒輪傳動，大齒輪與筒體固定在一起。電動機轉(zhuǎn)速為n1=800r/min 取容器轉(zhuǎn)速n310r/min 取=32 =2.5 則=800/2.5/32=10r/min1、電動機的選擇 選擇步進電動機 步進電機最大速度一般在600-1000r/min之間。 取BF系列反應式步進電動機 型號為110BF02 3相數(shù) 電壓為80V 最大靜轉(zhuǎn)距為80kgf.cm=509.810=7840Nmm 負載轉(zhuǎn)距一般為靜轉(zhuǎn)距的30%-50%，取個中間值40% 則負載轉(zhuǎn)距為78400.4=3136Nmm2、蝸桿傳動設計(1)選擇蝸桿頭數(shù)z1與蝸輪齒數(shù)z2 查表得傳動比i在14-30之間時6 z1取2 29z282 取z1=1 由i= n1/ n2=32= z2/ z1 得 Z2 =32 合適 (2)求蝸桿、蝸輪直徑d1 、d2、 取m=2.5 則查表得d1=28mm q= d1/ m=11.2 d2=m z2=80mm D1= d1+ m=30.5mm D2= d2+m=82.5mm 蝸桿長度L（11+0.06 z2）m=57.375mm 取為60mm 由表11-4，設計渦輪寬度B=25mm,=87.5mm(3)蝸桿導程角 tg= z1/q=2/10=0.087 則=5.5250 蝸桿與蝸輪的交錯角取為900 蝸輪螺旋角=5.5250 (4)傳動的中心距 a=54mm(5)選擇蝸輪蝸桿材料 蝸桿材料采用40Cr 淬火到4555HRC 并磨削 齒圈用青銅制造 蝸輪輪芯材料采用45鋼，齒圈用青銅制造 齒圈和輪芯間采用過盈配合(6)結構設計 蝸桿、渦輪直徑較小，采用蝸桿軸的結構如圖2.15所示。圖2.15 蝸桿軸 渦輪結構如圖2.16所示圖2.16 蝸輪軸3、齒輪計算設計聯(lián)軸器的傳動效率取為0.99，雙頭蝸桿傳動的機械效率為0.75-0.82 取為0.80，=0.263kw 則齒輪傳遞的功率為：P=0.263kw0.800.99=0.2kwn=25r/s i=2.51) 初步確定齒輪幾何尺寸及參數(shù) 模數(shù)m=4mm 齒數(shù) =40 =100 分度圓直徑：=160mm =400mm 齒寬 =30mm =28mm2) 材料 大齒輪硬度為240HBS的45鋼。 小齒輪硬度為280HBS的40cr鋼。3) 結構設計 齒輪直徑較大，設計成腹板式結構，如圖2.17和2.18所示。圖2.17 小齒輪結構圖圖2.18 大齒輪結構圖4) 校核 按齒面接觸強度校核：選載荷系數(shù)=1.3小齒輪傳遞轉(zhuǎn)矩 =5.74N取=0.1875查得材料彈性影響系數(shù)=189.8由材料硬度查得 小齒輪接觸疲勞強度極限=600MPa 大齒輪接觸疲勞強度極限=550MPa應力循環(huán)次數(shù) =0.216 =0.108由圖10-19，接觸疲勞壽命系數(shù) =1.03 =1.08 計算接觸疲取勞許用應力取失效概率為1% 安全系數(shù)為s=1 =1.03600=618MPa =1.08550=594MPa 計算=49.74mm所設計齒輪=160mm遠大于，所以合格4、軸的計算設計1) 蝸桿軸設計聯(lián)軸器傳動的效率為0.99，電機功率為0.243kw，轉(zhuǎn)速800r/s，確定軸的最小直徑 =7.72mm取最小處直徑為16mm。軸的結構設計，如圖2.19所示圖2.19 蝸桿軸軸材料選用40cr按扭矩校核軸的強度=3.79MPa =45MPa, 因此,故安全 2) 蝸輪軸計算設計 聯(lián)軸器傳動的效率為0.99，蝸桿傳動效率為0.80電機功率為0.243kw，轉(zhuǎn)速 800r/s，i=32 確定軸的最小直徑 =18.7m 取最小處直徑為25mm。軸的結構設計，如圖2.20所示圖2.20 蝸輪軸軸材料選用45鋼按扭矩校核軸的強度 =25.46MPa =45MPa, 因此,故安全5、其它零件設計 蝸桿軸承座設計，材料選用鑄鐵，結構如圖2.21所示。 圖2.21 蝸桿軸承座 具體見設計圖紙2) 蝸輪軸承座設計 材料選用鑄鐵，結構如圖2.22圖2.22 蝸輪軸承座3) 其它步進電機座設計，根據(jù)電機安裝要求確定機座高度及各定位尺寸，材料選用鑄鐵。各標準件如鍵、螺栓等見裝配圖6、卸料系統(tǒng)部件圖圖2.23 卸料傳動系統(tǒng)部件圖2.2.6 支架設計 機架采用角鋼，底板用10mm鋼板。 尺寸依結構而定，焊接方式連接。結構簡圖如圖2.24所示。 圖2.24 支架2.2.7 絞龍式家用和面機裝配圖 如圖2.25所示。圖2.25 和面機總裝圖3 小結 本次畢業(yè)設計，針對家用多功能和面機的設計要求，詳細設計了其攪拌裝置、攪拌傳動系統(tǒng)、取料傳動系統(tǒng)。在整個設計過程中受益良多，大學四年的知識融會貫通，才有了此次設計。也重新深入學習了之前掌握的不太好的課程，完善了大學的知識結構。各課程之間是互相聯(lián)系，相輔相成的。以前做事不夠嚴謹，本次設計過程中深切體會了嚴謹?shù)闹螌W態(tài)度對于設計者的重要性。 參考文獻1 盧頌峰. 機械零件課程設計手冊. 北京：中央廣播電視大學出版社，20012 王大康 盧頌峰.機械設計課程設計. 北京工業(yè)大學出版社，20003 劉混舉 機械可靠性設計 科學出版社 20124 濮良貴 紀名剛 機械設計 高等教育出版社20125 余桂英 郭紀林 AutoCAD 2008基礎教程 大連理工大學出版社20086 邵立新 夏素民 孫江宏 Pro/ENGINEER Wildfire 3.0中文版標準教程 清華大學出版社 2007 致謝 本次畢業(yè)設計進入尾聲了，由于部分課程基礎不好，本次設計我遇到了無數(shù)的困難，值得慶幸的是都在老師和同學的幫助下一一解決了。我一直認同這句話，別人幫你是你的福分，別人不幫你是別人的本分。所以，謝謝你們，謝謝。最應該感謝的人就是我的指導老師蔡金平老師，由于基礎差，自然困難也就多。但他始終不厭其煩對我的疑問一一指導?？梢哉f從開始的確定方案到后面排版，蔡老師真是手把手指點迷津。回憶那段時光，不經(jīng)有點恍惚，人生有個良師諍友遠勝百萬雄兵。還有我要感謝在大學里教過我的老師們，是他們把知識傾囊相授，更教會了我像個成年人樣去思考，讓我從大一的懵懂漸漸變得成熟。 雖然本次畢業(yè)設計結束了，我們也要畢業(yè)了，但是人生才剛剛開始。最后祝福老師們，同學們，朋友們享受人生，享受生活。31Robot companion localization at home and in the office Arnoud Visser Jurgen Sturm Frans Groen Intelligent Autonomous Systems, Universiteit van Amsterdam http:/www.science.uva.nl/research/ias/Abstract The abilities of mobile robots depend greatly on the performance of basic skills such asvision and localization. Although great progress has been made to explore and map extensivepublic areas with large holonomic robots on wheels, less attention is paid on the localizationof a small robot companion in a confined environment as a room in office or at home. Inthis article, a localization algorithm for the popular Sony entertainment robot Aibo inside aroom is worked out. This algorithm can provide localization information based on the naturalappearance of the walls of the room. The algorithm starts making a scan of the surroundings byturning the head and the body of the robot on a certain spot. The robot learns the appearanceof the surroundings at that spot by storing color transitions at different angles in a panoramicindex. The stored panoramic appearance is used to determine the orientation (including aconfidence value) relative to the learned spot for other points in the room. When multiplespots are learned, an absolute position estimate can be made. The applicability of this kind oflocalization is demonstrated in two environments: at home and in an office.1 Introduction1.1 Context Humans orientate easily in their natural environments. To be able to interact with humans, mobilerobots also need to know where they are. Robot localization is therefore an important basic skillof a mobile robot, as a robot companion like the Aibo. Yet, the Sony entertainment softwarecontained no localization software until the latest release1. Still, many other applications for arobot companion - like collecting a news paper from the front door - strongly depend on fast,accurate and robust position estimates. As long as the localization of a walking robot, like theAibo, is based on odometry after sparse observations, no robust and accurate position estimatescan be expected. Most of the localization research with the Aibo has concentrated on the RoboCup. At theRoboCup2 artificial landmarks as colored flags, goals and field lines can be used to achieve localizationaccuracies below six centimeters 6, 8.The price that these RoboCup approaches pay is their total dependency on artificial landmarksof known shape, positions and color. Most algorithms even require manual calibration of the actualcolors and lighting conditions used on a field and still are quite susceptible for disturbances aroundthe field, as for instance produced by brightly colored clothes in the audience. The interest of the RoboCup community in more general solutions has been (and still is) growingover the past few years. The almost-SLAM challenge3 of the 4-Legged league is a good example ofthe state-of-the-art in this community. For this challenge additional landmarks with bright colorsare placed around the borders on a RoboCup field. The robots get one minute to walk around andexplore the field. Then, the normal beacons and goals are covered up or removed, and the robotmust then move to a series of five points on the field, using the information learnt during the first1Aibo Mind 3 remembers the direction of its station and toys relative to its current orientation2RoboCup Four Legged League homepage, last accessed in May 2006, http:/www.tzi.de/4legged3Details about the Simultaneous Localization and Mapping challenge can be found at http:/www.tzi.de/4legged/pub/Website/Downloads/Challenges2005.pdf1 minute. The winner of this challenge 6 reached the five points by using mainly the information ofthe field lines. The additional landmarks were only used to break the symmetry on the soccer field.A more ambitious challenge is formulated in the newly founded RoboCup Home league4. Inthis challenge the robot has to safely navigate toward objects in the living room environment. Therobot gets 5 minutes to learn the environment. After the learning phase, the robot has to visit 4distinct places/objects in the scenario, at least 4 meters away from each other, within 5 minutes.1.2 Related Work Many researchers have worked on the SLAM problem in general, for instance on panoramic images1, 2, 4, 5. These approaches are inspiring, but only partially transferable to the 4-Legged league.The Aibo is not equipped with an omni-directional high-quality camera. The camera in the nosehas only a horizontal opening angle of 56.9 degrees and a resolution of 416 x 320 pixels. Further,the horizon in the images is not a constant, but depends on the movements of the head and legs ofthe walking robot. So each image is taken from a slightly different perspective, and the path of thecamera center is only in first approximation a circle. Further, the images are taken while the headis moving. When moving at full speed, this can give a difference of 5.4 degrees between the top andthe bottom of the image. So the image seems to be tilted as a function of the turning speed of thehead. Still, the location of the horizon can be calculated by solving the kinematic equations of therobot. To process the images, a 576 Mhz processor is available in the Aibo, which means that onlysimple image processing algorithms are applicable. In practice, the image is analyzed by followingscan-lines with a direction relative the calculated horizon. In our approach, multiple sectors abovethe horizon are analyzed, with in each sector multiple scan-lines in the vertical direction. One ofthe general approaches 3 divides the image in multiple sectors, but this image is omni-directionaland the sector is analyzed on the average color of the sector. Our method analysis each sector ona different characteristic feature: the frequency of colortransitions.2 Approach The main idea is quite intuitive: we would like the robot to generate and store a 360o circularpanorama image of its environment while it is in the learning phase. After that, it should aligneach new image with the stored panorama, and from that the robot should be able to derive itsrelative orientation (in the localization phase). This alignment is not trivial because the new imagecan be translated, rotated, stretched and perspectively distorted when the robot does not stand atthe point where the panorama was originally learned 11. Of course, the Aibo is not able (at least not in real-time) to compute this alignment on fullresolutionimages. Therefore a reduced feature space is designed so that the computations becometractable5 on an Aibo. So, a reduced circular 360o panorama model of the environment is learned.Figure 1 gives a quick overview of the algorithms main components. The Aibo performs a calibration phase before the actual learning can start. In this phase theAibo first decides on a suitable camera setting (i.e. camera gain and the shutter setting) basedon the dynamic range of brightness in the autoshutter step. Then it collects color pixels byturning its head for a while and finally clusters these into 10 most important color classes in thecolor clustering step using a standard implementation of the Expectation-Maximization algorithmassuming a Gaussian mixture model 9. The result of the calibration phase is an automaticallygenerated lookup-table that maps every YCbCr color onto one of the 10 color classes and cantherefore be used to segment incoming images into its characteristic color patches (see figure 2(a).These initialization steps are worked out in more detail in 10.4RoboCup Home League homepage, last accessed in May 2006, http:/www.ai.rug.nl/robocupathome/5Our algorithm consumes per image frame approximately 16 milliseconds, therefore we can easily process imagesat the full Aibo frame rate (30fps).Figure 1: Architecture of our algorithm(a) Unsupervised learned color segmentation.(b) Sectors and frequent color transitionsvisualized. Figure 2: Image processing: from the raw image to sector representation. This conversion consumesapproximately 6 milliseconds/frame on a Sony Aibo ERS7.2.1 Sector signature correlation Every incoming image is now divided into its corresponding sectors6. The sectors are located abovethe calculated horizon, which is generated by solving the kinematics of the robot. Using the lookuptable from the unsupervised learned color clustering, we can compute the sector features by countingper sector the transition frequencies between each two color classes in vertical direction. This yieldsthe histograms of 10x10 transition frequencies per sector, which we subsequently discretize into 5logarithmically scaled bins. In figure 2(b) we displayed the most frequent color transitions for eachsector. Some sectors have multiple color transitions in the most frequent bin, other sectors have asingle or no dominant color transition. This is only visualization; not only the most frequent colortransitions, but the frequency of all 100 color transitions are used as characteristic feature of thesector. In the learning phase we estimate all these 80x(10x10) distributions7 by turning the head andbody of the robot. We define a single distribution for a currently perceived sector byPcurrent (i, j, bin) =_1 discretize (freq (i, j) = bin0 otherwise(1) where i, j are indices of the color classes and bin one of the five frequency bins. Each sector isseen multiple times and the many frequency count samples are combined into a distribution learned680 sectors corresponding to 360o; with an opening angle of the Aibo camera of approx. 50o, this yields between10 and 12 sectors per image (depending on the head pan/tilt)7When we use 16bit integers, a complete panorama model can be described by (80 sectors)x(10 colors x 10colors)x(5 bins)x(2 byte) = 80 KB of memory.for that sector by the equation:Plearned (i, j, bin) = Pcountsector (i, j, bin)bin2frequencyBinscountsector (i, j, bin)(2) After the learning phase we can simply multiply the current and the learned distribution to getthe correlation between a currently perceived and a learned sector:Corr(Pcurrent, Plearned) =Yi,j2colorClasses,bin2frequencyBinsPlearned (i, j, bin) Pcurrent (i, j, bin) (3)2.2 Alignment After all the correlations between the stored panorama and the new image signatures were evaluated,we would like to get an alignment between the stored and seen sectors so that the overall likelihoodof the alignment becomes maximal. In other words, we want to find a diagonal path with theminimal cost through the correlation matrix. This minimal path is indicated as green dots in figure3. The path is extended to a green line for the sectors that are not visible in the latest perceivedimage. We consider the fitted path to be the true alignment and extract the rotational estimate robotfrom the offset from its center pixel to the diagonal (_sectors):robot =360_80_sectors (4) This rotational estimate is the difference between the solid green line and the dashed white linein figure 3, indicated by the orange halter. Further, we try to estimate the noise by fitting again apath through the correlation matrix far away from the best-fitted path.SNR =P(x,y)2minimumPathCorr(x, y)P(x,y)2noisePathCorr(x, y)(5)The noise path is indicated in figure 3 with red dots.(a) Robot standing on the trained spot (matchingline is just the diagonal)(b) Robot turned right by 45 degrees (matchingline displaced to the left) F igure 3: Visualization of the alignment step while the robot is scanning with its head. Thegreen solid line marks the minimum path (assumed true alignment) while the red line marks thesecond-minimal path (assumed peak noise). The white dashed line represents the diagonal, whilethe orange halter illustrates the distance between the found alignment and the center diagonal(_sectors).2.3 Position Estimation with Panoramic Localization The algorithm described in the previous section can be used to get a robust bearing estimatetogether with a confidence value for a single trained spot. As we finally want to use this algorithmto obtain full localization we extended the approach to support multiple training spots. Themain idea is that the robot determines to which amount its current position resembles with thepreviously learned spots and then uses interpolation to estimate its exact position. As we thinkthat this approach could also be useful for the RoboCup Home league (where robot localizationin complex environments like kitchens and living rooms is required) it could become possible thatwe finally want to store a comprehensive panorama model library containing dozens of previouslytrained spots (for an overview see 1). However, due to the computation time of the feature space conversion and panorama matching,per frame only a single training spot and its corresponding panorama model can be selected.Therefore, the robot cycles through the learned training spots one-by-one. Every panorama modelis associated with a gradually changed confidence value representing a sliding average on the confidence values we get from the per-image matching. After training, the robot memorizes a given spot by storing the confidence values received fromthe training spots. By comparing a new confidence value with its stored reference, it is easy todeduce whether the robot stands closer or farther from the imprinted target spot.We assume that the imprinted target spot is located somewhere between the training spots.Then, to compute the final position estimate, we simply weight each training spot with its normalizedcorresponding confidence value:positionrobot =XipositioniPconfidenceij confidencej(6) This should yield zero when the robot is assumed to stand at the target spot or a translationestimate towards the robots position when the confidence values are not in balance anymore.To prove the validity of this idea, we trained the robot on four spots on regular 4-Legged fieldin our robolab. The spots were located along the axes approximately 1m away from the center.As target spot, we simply chose the center of the field. The training itself was performed fullyautonomously by the Aibo and took less than 10 minutes. After training was complete, the Aibowalked back to the center of the field. We recorded the found position and kidnapped the robot toan arbitrary position around the field and let it walk back again. Please be aware that our approach for multi-spot localization is at this moment rather primitiveand has to be only understood as a proof-of-concept. In the end, the panoramic localization datafrom vision should of course be processed by a more sophisticated localization algorithm, like aKalman or particle filter (last not least to incorporate movement data from the robot).3 Results3.1 Environments We selected four different environments to test our algorithm under a variety of circumstances. Thefirst two experiments were conducted at home and in an office environment8 to measure performanceunder real-world circumstances. The experiments were performed on a cloudy morning, sunnyafternoon and late in the evening. Furthermore, we conducted exhaustive tests in our laboratory.Even more challenging, we took an Aibo outdoors (see 7).3.2 Measured results Figure 4(a) illustrates the results of a rotational test in a normal living room. As the error in therotation estimates ranges between -4.5 and +4.5 degrees, we may assume an error in alignment ofa single sector9; moreover, the size of the confidence interval can be translated into maximal twosectors, which corresponds to the maximal angular resolution of our approach.8XX office, DECIS lab, Delft9full circle of 3600 divided by 80 sectors(a) Rotational test in natural environment (livingroom, sunny afternoon)(b) Translational test in natural environment (childsroom, late in the evening) Figure 4: Typical orientation estimation results of experiments conducted at home. In the rotationalexperiment on the left the robot is rotated over 90 degrees on the same spot, and every 5 degrees itsorientation is estimated. The robot is able to find its true orientation with an error estimate equalto one sector of 4.5 degrees. The translational test on the right is performed in a childs room. Therobot is translated over a straight line of 1.5 meter, which covers the major part of the free spacein this room. The robot is able to maintain a good estimate of its orientation; although the errorestimate increases away from the location where the appearance of the surroundings was learned.Figure 4(b) shows the effects of a translational dislocation in a childs room. The robot wasmoved onto a straight line back and forth through the room (via the trained spot somewhere in themiddle). The robot is able to estimate its orientation quite well on this line. The discrepancy withthe true orientation is between +12.1 and -8.6 degrees, close to the walls. This is also reflected inthe computed confidence interval, which grows steadily when the robot is moved away from thetrained spot. The results are quite impressive for the relatively big movements in a small room andthe resulting significant perspective changes in that room. Figure 5(a) also stems from a translational test (cloudy morning) which has been conducted inan office environment. The free space in this office is much larger than at home. The robot wasmoved along a 14m long straight line to the left and right and its orientation was estimated. Notethe error estimate stays low at the right side of this plot. This is an artifact which nicely reflectsthe repetition of similarly looking working islands in the office. In both translational tests it can be seen intuitively that the rotation estimates are withinacceptable range. This can also be shown quantitatively (see figure 5(b): both the orientationerror and the confidence interval increase slowly and in a graceful way when the robot is movedaway from the training spot. Finally, figure 6 shows the result of the experiment to estimate the absolute position with multiplelearned spots. It can be seen that the localization is not as accurate as traditional approaches,but can still be useful for some applications (bearing in mind that no artificial landmarks are required).We recorded repeatedly a derivation to the upper right that we think can be explained bythe fact that different learning spots dont produce equally strong confidence values; we believe tobe able to correct for that by means of confidence value normalization in the near future.4 Conclusion Although at first sight the algorithm seems to rely on specific texture features of the surroundingsurfaces, in practice no dependency could be found. This can be explained by two reasons: firstly, asthe (vertical) position of a color transition is not used anyway, the algorithm is quite robust against(vertical) scaling. Secondly, as the algorithm aligns on many color transitions in the background(typically more than a hundred in the same sector), the few color transitions produced by objectsin the foreground (like beacons and spectators) have a minor impact on the match (because theirsizes relative to the background are comparatively small). The lack of an accurate absolute position estimates seems to be a clear drawback with respect tothe other methods, but bearing information alone can already be very useful for certain applications.(a) Translational test i