家用多功能和面機的設計【雙葉片攪拌和面容量為10kg】

購買設計請充值后下載，，資源目錄下的文件所見即所得，都可以點開預覽，，資料完整，充值下載可得到資源目錄里的所有文件。。?！咀ⅰ浚篸wg后綴為CAD圖紙，doc,docx為WORD文檔，原稿無水印，可編輯。。。具體請見文件預覽，有不明白之處，可咨詢QQ:12401814

家用多功能和面機盧佳敏工學院農業(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國，而和面機是其生產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)、感官等質量指標，進而影響著企業(yè)的經濟效益和社會效益。隨著中國綜合實力的躍升，消費者對食品的質量前所未有的關注，這也就對和面機市場形成一種倒逼驅動力，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。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，本次設計所得經驗與成果，經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。也能體會到嚴謹的思維對于設計人員的重要性?？赡芤粋€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)： 農業(yè)機械化及其自動化 年 級： 農機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 Abstract Mixer 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;dough II 目 錄 摘 要 II Abstract III 目 錄 V 1 緒論 1 1.1 本課題的研究目的 1 1.2 我國的和面機市場 1 1.3 本課題應達到的要求 1 2 和面機初步設計 1 2.1 設計準備 1 2.1.1 和面的基本過程 1 2.1.2 和面機各部件設計 1 2.2 家用多功能和面機設計 2 2.2.1 確定和面機容量 2 2.2.2 總方案的設計 3 2.2.3 攪拌裝置的設計 4 2.2.4 攪拌軸傳動系統(tǒng)的設計 6 2.2.5 取料裝置的設計 21 2.2.6 支架的設計 28 2.2.7 家用和面機裝配圖 28 3 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（根據我們需要的面團用途），啟動和面機。攪拌軸旋轉進行攪拌混合，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，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容器，由電動機，減速器及容器翻轉齒輪組成。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動，使容器翻轉一定的角度，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，減速器及聯軸器等組成。 和面機工作轉速低，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 生產力 （㎏/次） 12 25 50 75 100 主電機 （千瓦） 1.1 2.2 3.0 4.0 5.5 2.2 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可取0.5-0.6 查有關資料可得我國面粉的吸水系數為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。 面粉的質量：10kg 水的質量：4kg 計算得總體積為：10000/0.5+4000/1＝70000mm3 所以取攪拌容器容積為200000mm3 （1）寬度（B） B = 2 ( R+δ) 式中 R = 攪拌漿半徑 R的大小取決于面團性質及生產（2.1） δ= 漿葉與容器的間隙 δ取為5mm δ=5 mm B=2（R＋5） （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。 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 生產力 （㎏/次） 12 25 50 75 100 主電機 （千瓦） 1.1 2.2 3.0 4.0 5.5 根據表格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)攪拌漿轉速 n=14~35 rpm 各種面團所需的轉速不同 現取n≈20rpm。 (2)電機轉速 = 960rpm 同步轉速 (3)傳動鏈總傳動比 =48 （2.5） (4)傳動比分配： ①皮帶傳動 3 取=3 ②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=3.3kw，n1=960r/min，查圖得選用A型v帶 (3)求大、小帶輪基準直徑、 ① 取=100mm ② 驗算帶速v==5.02 （2.7） 5<5.02<30,符合要求 ③ ==300 （2.8） 由i=3 得n2 =320r/min 圓整后得=315mm ④ 求v帶基準長度Ld和中心距a 由0.7（+ ）＜＜2（+ ） 取=450mm 帶長=2+π（+ ）/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） ⑤ 驗算小帶輪包角α （2.11） α =1800-（－）/a×57.30 =153.80＞900 合適 ⑥ 求V帶根數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 ⑦ 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。 ⑧ 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、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 =2 1) 選精度等級、齒輪類型、材料、齒數 ①直齒輪傳動 ②一般工作機器，速度不高，故選用7級精度 ③材料 大齒輪硬度為240HBS的45鋼 小齒輪硬度為280HBS的40cr鋼 ④選小齒輪齒數=24 大齒輪齒數=48 2) （2.14） ①選載荷系數=1.3 ②小齒輪傳遞轉矩 ==8.952×N （2.15） ③由表10-7，取=1 ④由表10-6，材料彈性影響系數=189.8 ⑤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 ⑥應力循環(huán)次數 =1.382× （2.16） =0.6912× ⑦由圖10-19，接觸疲勞壽命系數 =0.9 =0.95 ⑧計算接觸疲取勞許用應力 取失效概率為1% 安全系數為s=1 =0.9×600=540MPa （2.17） 0.95×550=522.5MPa 3) ①=66.01mm ②圓周速度v==1.105m/s （2.18） ③齒寬b==66.01mm （2.19） ④齒寬與齒高之比 （2.20） 模數==2.75 （2.21） 齒高h=2.25=6.19 ==10.66 ⑤載荷系數 =0.675 ==1 =1 =1.315 =1.24 k==0.8870 （2.22） ⑥分度圓直徑=58.113mm ⑦計算模 =2.41 4) 根據齒根彎曲強度計算設計 確定參數 ①小齒輪彎曲強度極限=500MPa 大齒輪彎曲強度極限=380MPa ②彎曲疲勞壽命系數=0.85 =0.88 ③取彎曲疲勞安全系數s=1.4 彎曲許用應力 （2.23） ④ k==1×0.675×1×1.24=0.837 ⑤齒形系數 =2.65 =2.33 應力校正系數 =1.58 =1.69 ⑥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 =2×29=58 6) 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e<2m,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 ①直齒輪傳動 ②一般工作機器，速度不高，故選用7級精度 ③材料 大齒輪硬度為240HBS的45鋼 小齒輪硬度為280HBS的40cr鋼 ④選小齒輪齒數=20 大齒輪齒數=160 2) ①選載荷系數=1.3 ②小齒輪傳遞轉矩 =1.79×N ③由表10-7，取=0.5 ④由表10-6，材料彈性影響系數=189.8 ⑤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 ⑥應力循環(huán)次數 =6.912× =0.864× ⑦由圖10-19，接觸疲勞壽命系數 =0.9 =0.93 ⑧計算接觸疲取勞許用應力 取失效概率為1% 安全系數為s=1 =0.9×600=540MPa 0.95×550=522.5MPa 3) ①=75.5mm ②圓周速度v==0.6322m/s ③齒寬b==75.5mm ④齒寬與齒高之比 模數=3.775mm 齒高h=2.25=8.494mm ==8.89 ⑤載荷系數 =1.02 ==1 =1 =1.425 =1.375 k==1.4535 ⑥分度圓直徑=79.83mm ⑦計算模 =4mm 4) 根據齒根彎曲強度計算設計 確定參數 ①小齒輪彎曲強度極限=500MPa 大齒輪彎曲強度極限=380MPa ②彎曲疲勞壽命系數=0.85 =0.88 ③取彎曲疲勞安全系數s=1.4 彎曲許用應力 ④ k==1.4025 ⑤齒形系數 =2.8 =2.138 應力校正系數 =1.55 =1.835 ⑥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 =8×25=200 6) 分度圓直徑 中心距a=(80+640)/2=360mm 齒寬 =1×58=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400mm<<1000,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、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 =9550000×3×0.96×0.97/(160×0.6836×0.2×30×30×30)=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。 電動機轉速為n1=800r/min 取容器轉速n3≈10r/min 取=32 =2.5 則=800/2.5/32=10r/min 1、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 電壓為80V 最大靜轉距為80kgf.cm=50×9.8×10=7840Nmm 負載轉距一般為靜轉距的30%-50%，取個中間值40% 則負載轉距為7840×0.4=3136Nmm 2、蝸桿傳動設計 (1)選擇蝸桿頭數z1與蝸輪齒數z2 查表得傳動比i在14-30之間時[6] z1取2 29＜z2＜82 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γ tgγ= z1/q=2/10=0.087 則γ=5.5250 蝸桿與蝸輪的交錯角取為900 蝸輪螺旋角β=γ=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45—55HRC 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=0.263kw×0.80×0.99=0.2kw n=25r/s i=2.5 1) 初步確定齒輪幾何尺寸及參數 模數m=4mm 齒數 =40 =100 分度圓直徑：=160mm =400mm 齒寬 =30mm =28mm 2) 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： ①選載荷系數=1.3 ②小齒輪傳遞轉矩 =5.74×N ③取==0.1875 ④查得材料彈性影響系數=189.8 ⑤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 ⑥應力循環(huán)次數 =0.216× =0.108× ⑦由圖10-19，接觸疲勞壽命系數 =1.03 =1.08 ⑧計算接觸疲取勞許用應力 取失效概率為1% 安全系數為s=1 =1.03×600=618MPa =1.08×550=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 聯軸器傳動的效率為0.99，電機功率為0.243kw，轉速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的治學態(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，2001 [2] 王大康 盧頌峰.機械設計課程設計. 北京工業(yè)大學出版社，2000 [3] 劉混舉 機械可靠性設計 科學出版社 2012 [4] 濮良貴 紀名剛 機械設計 高等教育出版社2012 [5] 余桂英 郭紀林 AutoCAD 2008基礎教程 大連理工大學出版社2008 [6] 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。可以說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?；貞浤嵌螘r光，不經有點恍惚，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。 31 Robot companion localization at home and in the office Arnoud Visser J¨urgen 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 as vision and localization. Although great progress has been made to explore and map extensive public areas with large holonomic robots on wheels, less attention is paid on the localization of a small robot companion in a confined environment as a room in office or at home. In this article, a localization algorithm for the popular Sony entertainment robot Aibo inside a room is worked out. This algorithm can provide localization information based on the natural appearance of the walls of the room. The algorithm starts making a scan of the surroundings by turning the head and the body of the robot on a certain spot. The robot learns the appearance of the surroundings at that spot by storing color transitions at different angles in a panoramic index. The stored panoramic appearance is used to determine the orientation (including a confidence value) relative to the learned spot for other points in the room. When multiple spots are learned, an absolute position estimate can be made. The applicability of this kind of localization is demonstrated in two environments: at home and in an office. 1 Introduction 1.1 Context Humans orientate easily in their natural environments. To be able to interact with humans, mobile robots also need to know where they are. Robot localization is therefore an important basic skill of a mobile robot, as a robot companion like the Aibo. Yet, the Sony entertainment software contained no localization software until the latest release1. Still, many other applications for a robot 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 the Aibo, is based on odometry after sparse observations, no robust and accurate position estimates can be expected. Most of the localization research with the Aibo has concentrated on the RoboCup. At the RoboCup2 artificial landmarks as colored flags, goals and field lines can be used to achieve localization accuracies below six centimeters [6, 8]. The price that these RoboCup approaches pay is their total dependency on artificial landmarks of known shape, positions and color. Most algorithms even require manual calibration of the actual colors and lighting conditions used on a field and still are quite susceptible for disturbances around the 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) growing over the past few years. The almost-SLAM challenge3 of the 4-Legged league is a good example of the state-of-the-art in this community. For this challenge additional landmarks with bright colors are placed around the borders on a RoboCup field. The robots get one minute to walk around and explore the field. Then, the normal beacons and goals are covered up or removed, and the robot must then move to a series of five points on the field, using the information learnt during the first 1Aibo Mind 3 remembers the direction of its station and toys relative to its current orientation 2RoboCup Four Legged League homepage, last accessed in May 2006, http://www.tzi.de/4legged 3Details about the Simultaneous Localization and Mapping challenge can be found at http://www.tzi.de/ 4legged/pub/Website/Downloads/Challenges2005.pdf 1 minute. The winner of this challenge [6] reached the five points by using mainly the information of the 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. In this challenge the robot has to safely navigate toward objects in the living room environment. The robot gets 5 minutes to learn the environment. After the learning phase, the robot has to visit 4 distinct 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 images [1, 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 nose has 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 of the walking robot. So each image is taken from a slightly different perspective, and the path of the camera center is only in first approximation a circle. Further, the images are taken while the head is moving. When moving at full speed, this can give a difference of 5.4 degrees between the top and the bottom of the image. So the image seems to be tilted as a function of the turning speed of the head. Still, the location of the horizon can be calculated by solving the kinematic equations of the robot. To process the images, a 576 Mhz processor is available in the Aibo, which means that only simple image processing algorithms are applicable. In practice, the image is analyzed by following scan-lines with a direction relative the calculated horizon. In our approach, multiple sectors above the horizon are analyzed, with in each sector multiple scan-lines in the vertical direction. One of the general approaches [3] divides the image in multiple sectors, but this image is omni-directional and the sector is analyzed on the average color of the sector. Our method analysis each sector on a 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 circular panorama image of its environment while it is in the learning phase. After that, it should align each new image with the stored panorama, and from that the robot should be able to derive its relative orientation (in the localization phase). This alignment is not trivial because the new image can be translated, rotated, stretched and perspectively distorted when the robot does not stand at the 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 fullresolution images. Therefore a reduced feature space is designed so that the computations become tractable5 on an Aibo. So, a reduced circular 360o panorama model of the environment is learned. Figure 1 gives a quick overview of the algorithm’s main components. The Aibo performs a calibration phase before the actual learning can start. In this phase the Aibo first decides on a suitable camera setting (i.e. camera gain and the shutter setting) based on the dynamic range of brightness in the autoshutter step. Then it collects color pixels by turning its head for a while and finally clusters these into 10 most important color classes in the color clustering step using a standard implementation of the Expectation-Maximization algorithm assuming a Gaussian mixture model [9]. The result of the calibration phase is an automatically generated lookup-table that maps every YCbCr color onto one of the 10 color classes and can therefore 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 images at the full Aibo frame rate (30fps). Figure 1: Architecture of our algorithm (a) Unsupervised learned color segmentation. (b) Sectors and frequent color transitions visualized. Figure 2: Image processing: from the raw image to sector representation. This conversion consumes approximately 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 above the calculated horizon, which is generated by solving the kinematics of the robot. Using the lookup table from the unsupervised learned color clustering, we can compute the sector features by counting per sector the transition frequencies between each two color classes in vertical direction. This yields the histograms of 10x10 transition frequencies per sector, which we subsequently discretize into 5 logarithmically scaled bins. In figure 2(b) we displayed the most frequent color transitions for each sector. Some sectors have multiple color transitions in the most frequent bin, other sectors have a single or no dominant color transition. This is only visualization; not only the most frequent color transitions, but the frequency of all 100 color transitions are used as characteristic feature of the sector. In the learning phase we estimate all these 80x(10x10) distributions7 by turning the head and body of the robot. We define a single distribution for a currently perceived sector by Pcurrent (i, j, bin) = _ 1 discretize (freq (i, j)) = bin 0 otherwise (1) where i, j are indices of the color classes and bin one of the five frequency bins. Each sector is seen multiple times and the many frequency count samples are combined into a distribution learned 680 sectors corresponding to 360o; with an opening angle of the Aibo camera of approx. 50o, this yields between 10 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 10 colors)x(5 bins)x(2 byte) = 80 KB of memory. for that sector by the equation: Plearned (i, j, bin) = Pcountsector (i, j, bin) bin2frequencyBins countsector (i, j, bin) (2) After the learning phase we can simply multiply the current and the learned distribution to get the correlation between a currently perceived and a learned sector: Corr(Pcurrent, Plearned) = Y i,j2colorClasses, bin2frequencyBins Plearned (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 likelihood of the alignment becomes maximal. In other words, we want to find a diagonal path with the minimal cost through the correlation matrix. This minimal path is indicated as green dots in figure 3. The path is extended to a green line for the sectors that are not visible in the latest perceived image. We consider the fitted path to be the true alignment and extract the rotational estimate 'robot from 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 line in figure 3, indicated by the orange halter. Further, we try to estimate the noise by fitting again a path through the correlation matrix far away from the best-fitted path. SNR = P (x,y)2minimumPath Corr(x, y) P (x,y)2noisePath Corr(x, y) (5) The noise path is indicated in figure 3 with red dots. (a) Robot standing on the trained spot (matching line is just the diagonal) (b) Robot turned right by 45 degrees (matching line displaced to the left) F igure 3: Visualization of the alignment step while the robot is scanning with its head. The green solid line marks the minimum path (assumed true alignment) while the red line marks the second-minimal path (assumed peak noise). The white dashed line represents the diagonal, while the 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 estimate together with a confidence value for a single trained spot. As we finally want to use this algorithm to obtain full localization we extended the approach to support multiple training spots. The main idea is that the robot determines to which amount its current position resembles with the previously learned spots and then uses interpolation to estimate its exact position. As we think that this approach could also be useful for the RoboCup @ Home league (where robot localization in complex environments like kitchens and living rooms is required) it could become possible that we finally want to store a comprehensive panorama model library containing dozens of previously trained 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 model is 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 from the training spots. By comparing a new confidence value with its stored reference, it is easy to deduce 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 normalized corresponding confidence value: positionrobot = X i positioni Pconfidencei j confidencej (6) This should yield zero when the robot is assumed to stand at the target spot or a translation estimate towards the robot’s 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 field in 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 fully autonomously by the Aibo and took less than 10 minutes. After training was complete, the Aibo walked back to the center of the field. We recorded the found position and kidnapped the robot to an 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 primitive and has to be only understood as a proof-of-concept. In the end, the panoramic localization data from vision should of course be processed by a more sophisticated localization algorithm, like a Kalman or particle filter (last not least to incorporate movement data from the robot). 3 Results 3.1 Environments We selected four different environments to test our algorithm under a variety of circumstances. The first two experiments were conducted at home and in an office environment8 to measure performance under real-world circumstances. The experiments were performed on a cloudy morning, sunny afternoon 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 the rotation estimates ranges between -4.5 and +4.5 degrees, we may assume an error in alignment of a single sector9; moreover, the size of the confidence interval can be translated into maximal two sectors, which corresponds to the maximal angular resolution of our approach. 8XX office, DECIS lab, Delft 9full circle of 3600 divided by 80 sectors (a) Rotational test in natural environment (living room, sunny afternoon) (b) Translational test in natural environment (child’s room, late in the evening) Figure 4: Typical orientation estimation results of experiments conducted at home. In the rotational experiment on the left the robot is rotated over 90 degrees on the same spot, and every 5 degrees its orientation is estimated. The robot is able to find its true orientation with an error estimate equal to one sector of 4.5 degrees. The translational test on the right is performed in a child’s room. The robot is translated over a straight line of 1.5 meter, which covers the major part of the free space in this room. The robot is able to maintain a good estimate of its orientation; although the error estimate 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 child’s room. The robot was moved onto a straight line back and forth through the room (via the trained spot somewhere in the middle). The robot is able to estimate its orientation quite well on this line. The discrepancy with the true orientation is between +12.1 and -8.6 degrees, close to the walls. This is also reflected in the computed confidence interval, which grows steadily when the robot is moved away from the trained spot. The results are quite impressive for the relatively big movements in a small room and the resulting significant perspective changes in that room. Figure 5(a) also stems from a translational test (cloudy morning) which has been conducted in an office environment. The free space in this office is much larger than at home. The robot was moved along a 14m long straight line to the left and right and its orientation was estimated. Note the error estimate stays low at the right side of this plot. This is an artifact which nicely reflects the repetition of similarly looking working islands in the office. In both translational tests it can be seen intuitively that the rotation estimates are within acceptable range. This can also be shown quantitatively (see figure 5(b)): both the orientation error and the confidence interval increase slowly and in a graceful way when the robot is moved away from the training spot. Finally, figure 6 shows the result of the experiment to estimate the absolute position with multiple learned 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 by the fact that different learning spots don’t produce equally strong confidence values; we believe to be 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 surrounding surfaces, in practice no dependency could be found. This can be explained by two reasons: firstly, as the (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 objects in the foreground (like beacons and spectators) have a minor impact on the match (because their sizes relative to the background are comparatively small). The lack of an accurate absolute position estimates seems to be a clear drawback with respect to the other methods, but bearing information alone can already be very useful for certain applications. (a) Translational test i