TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)機(jī)械設(shè)計(jì)
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畢業(yè)設(shè)計(jì)（論文）
任務(wù)書(shū)
設(shè)計(jì)（論文）題目：TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)機(jī)械設(shè)計(jì)
學(xué) 院 名 稱(chēng)： 機(jī)械工程學(xué)院
專(zhuān) 業(yè)： 機(jī)械設(shè)計(jì)制造及其自動(dòng)化
學(xué) 生 姓 名： 劉凱 學(xué)號(hào)： 11403010113
指 導(dǎo) 教 師： 鄭書(shū)華
2014年 12月 7日
1．設(shè)計(jì)（論文）擬解決的主要問(wèn)題
設(shè)計(jì)TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)，是一種鼠牙盤(pán)定位、液壓定量分度的分度工作臺(tái)，B軸是該分度工作臺(tái)的第四軸，是單向定量旋轉(zhuǎn)軸，要求工作臺(tái)有松開(kāi)、鎖緊機(jī)構(gòu)。設(shè)計(jì)TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)的機(jī)械結(jié)構(gòu)圖、非標(biāo)零件圖并核算。
2．設(shè)計(jì)（論文）的主要內(nèi)容和基本要求
（1）主要技術(shù)參數(shù)
①工作臺(tái)回轉(zhuǎn)直徑：400 mm *400mm，工作臺(tái)重量800KG
②B軸重復(fù)定位精度：±2″
③B軸定位精度：±8°
④B軸的切削進(jìn)給速度：0~10r/min
⑤主軸轉(zhuǎn)速：60～6000r/min（高低擋無(wú)級(jí)變速），主軸錐孔MT6；
⑥徑縱向進(jìn)給最大速度：4m/min，橫向進(jìn)給最大速度：4m/min。
⑦主電機(jī)功率11/15（30min）Kw。
⑧定量角度分度；
分析TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)的工作原理，設(shè)計(jì)機(jī)械結(jié)構(gòu)裝配圖、零件圖并核算。
（2）設(shè)計(jì)要求
分析TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)結(jié)構(gòu)，查閱相關(guān)文獻(xiàn)資料，書(shū)寫(xiě)文獻(xiàn)綜述（2000字以上）；翻譯外文文獻(xiàn)2篇（2000字以上）, 翻譯提供文章的摘要、前言和相關(guān)核心段落；撰寫(xiě)開(kāi)題報(bào)告1篇（2000字以上）；繪制鼠牙盤(pán)式分度工作臺(tái)的機(jī)械結(jié)構(gòu)裝配圖一張（A0）；繪制非標(biāo)零件圖若張；圖紙工作量3張A0，設(shè)計(jì)說(shuō)明書(shū) (10000字以上)。
圖樣質(zhì)量：計(jì)算機(jī)繪圖，符合最新標(biāo)準(zhǔn)；表達(dá)完整，布置合理清晰、尺寸標(biāo)注齊全、技術(shù)要求全面；零件圖同時(shí)要注意結(jié)構(gòu)要素和加工工藝性。
（3）查閱文獻(xiàn)關(guān)鍵詞
TH6340B臥式加工中心 定位銷(xiāo)式分度工作臺(tái) 鎖緊機(jī)構(gòu) 松開(kāi)機(jī)構(gòu)
3．推薦參考文獻(xiàn)
[1] 陳俊龍，陳俊華。MCV_50A立式加工中心使用探索[J]. 寧波高等專(zhuān)科科學(xué)學(xué)報(bào), 1999.
[2] 廉元國(guó)，張永洪. 加工中心設(shè)計(jì)與應(yīng)用［M］.北京：機(jī)械工業(yè)出版社，1995
[3] 機(jī)床設(shè)計(jì)手冊(cè)編寫(xiě)組.機(jī)床設(shè)計(jì)手冊(cè)（第三冊(cè)）［M］. 北京：機(jī)械工業(yè)出版社，1986
[4] 陳蔚芳，機(jī)床數(shù)控技術(shù)及應(yīng)用[J].科學(xué)出版社, 2005
[5] 勞動(dòng)和社會(huì)保障部教材辦公室組織編寫(xiě). 數(shù)控機(jī)床機(jī)械系統(tǒng)[M]. 中國(guó)勞動(dòng)和社會(huì)保障出版社,2004.6
[6] 夏田, 數(shù)控加工中心設(shè)計(jì)[M] .北京：化學(xué)出版社，2006
[7] 楊有君，數(shù)控技術(shù)[M] .北京：機(jī)械工業(yè)出版社2006
[8] 龔仲華，數(shù)控技術(shù)[M] .北京：機(jī)械工業(yè)出版社2004
[9] Tao Cheng, Jie Zhang, Chunhua Hu, Bo Wu and Shuzi Yang .Intelligent Machine Tools in a Distributed Network Manufacturing Mode Environment [J]. Adv Manuf Technol, 2001,(17):221–232
[10] Shiuh-Tarng Chiang, Ding-I Liu, An-Chen Lee and Wei-Hua Cheng.Adaptive control optimization in end milling using nerual Networks [J]. International Journal of Machine Tools and Manufacture, 1995,34(5): 637–660 Boldea,Sayed A Nasar. Linear electric actuators and generators. Cambridge University Press, 1997.
4．進(jìn)度安排
2014年11月18日 接受任務(wù)
2014年11月21日－14月5日 熟悉內(nèi)容，完成文獻(xiàn)綜述和英文翻譯；
2014年12月5日－14年12月12日 完成開(kāi)題報(bào)告，畢業(yè)實(shí)習(xí)開(kāi)始 方案確定，
計(jì)算和草圖繪制；
2014年12月12日－15年1月12日 方案確定，草圖繪制；進(jìn)給機(jī)構(gòu)計(jì)算、
裝配圖繪制；電氣設(shè)計(jì)圖繪制；完成
所有零、部件結(jié)構(gòu)設(shè)計(jì)及設(shè)計(jì)說(shuō)明書(shū)；
修改圖樣和整理設(shè)計(jì)計(jì)算書(shū)，上交畢
業(yè)設(shè)計(jì)資料。
2015年1月12日－15年1月16日 論文評(píng)閱
2015年1月16日－15年1月19日 答辯
指導(dǎo)教師（簽字） 鄭書(shū)華
2014年11月18日
教研室主任審核意見(jiàn)：
教研室主任（簽字） 郭建亮
2014 年 11 月 18 日
畢業(yè)設(shè)計(jì)（論文）
開(kāi)題報(bào)告
設(shè)計(jì)（論文）題目：TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)機(jī)械設(shè)計(jì)
學(xué) 院 名 稱(chēng)： 機(jī)械工程學(xué)院
專(zhuān) 業(yè)： 機(jī)械設(shè)計(jì)制造及其自動(dòng)化
學(xué) 生 姓 名： 劉凱 學(xué)號(hào)： 11403010113
指 導(dǎo) 教 師： 鄭書(shū)華
2014年 12 月 7 日
一、 研究的基本內(nèi)容與擬解決的主要問(wèn)題（或研究的主要內(nèi)容及預(yù)期目標(biāo)）：
研究主要內(nèi)容：
分析TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)的工作原理，設(shè)計(jì)機(jī)械結(jié)構(gòu)裝配圖、零件圖并核算。設(shè)計(jì)TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)，即一種鼠牙盤(pán)定位、液壓定量分度的分度工作臺(tái)，B軸是該分度工作臺(tái)的第四軸，是單向定量旋轉(zhuǎn)軸，要求工作臺(tái)有松開(kāi)、鎖緊機(jī)構(gòu)。具體需要設(shè)計(jì)TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)的機(jī)械結(jié)構(gòu)圖、非標(biāo)零件圖并核算。
設(shè)計(jì)流程1電機(jī)的選擇2傳動(dòng)比的分配及傳動(dòng)效率的計(jì)算 3同步帶的選擇及同步帶輪的設(shè)計(jì)計(jì)算4圓錐齒輪的設(shè)計(jì)計(jì)算 5圓柱齒輪的設(shè)計(jì)計(jì)算 6鼠牙盤(pán)的設(shè)計(jì)計(jì)算 7液壓系統(tǒng)的設(shè)計(jì)計(jì)算 8主要軸承的選擇 9潤(rùn)滑與密封10其他元件的選擇
主要技術(shù)參數(shù)
①工作臺(tái)回轉(zhuǎn)直徑：400 mm *400mm，工作臺(tái)重量800KG
②B軸重復(fù)定位精度：±2″
③B軸定位精度：±8°
④B軸的切削進(jìn)給速度：0~10r/min
⑤主軸轉(zhuǎn)速：60～6000r/min（高低擋無(wú)級(jí)變速），主軸錐孔MT6；
⑥徑縱向進(jìn)給最大速度：4m/min，橫向進(jìn)給最大速度：4m/min。
⑦主電機(jī)功率11/15（30min）Kw。
⑧定量角度分度
預(yù)期目標(biāo)：
課題的設(shè)計(jì)對(duì)于我們綜合運(yùn)用所學(xué)的理論知識(shí)和技能方面有很大的幫助，并要求我們必須具備扎實(shí)的機(jī)械設(shè)計(jì)基礎(chǔ)，具有全方面的機(jī)械專(zhuān)業(yè)知識(shí)，熟悉組合機(jī)床等相關(guān)部件的設(shè)計(jì)原理，并且在設(shè)計(jì)過(guò)程中遇到的問(wèn)題要及時(shí)反饋和查閱相關(guān)資料，對(duì)于那些計(jì)算的設(shè)計(jì)過(guò)程，更是要保證計(jì)算數(shù)據(jù)的準(zhǔn)確，同時(shí)也要考慮設(shè)計(jì)的情況能否符合實(shí)際生產(chǎn)加工中的要求。
通過(guò)本次設(shè)計(jì)我希望可以培養(yǎng)自己的設(shè)計(jì)計(jì)算、工程繪圖、實(shí)驗(yàn)研究、數(shù)據(jù)處理、查閱文獻(xiàn)、外文資料的閱讀與翻譯、計(jì)算機(jī)應(yīng)用、文字表達(dá)等基本工作實(shí)踐能力，使自己初步掌握科學(xué)研究的基本方法和思路。加強(qiáng)對(duì)臥式加工中心的了解，熟悉加工中心的基本原理和操作，通過(guò)研究核心部件工作臺(tái)，能熟練使用加工中心，為以后的學(xué)習(xí)和工作奠定一定的基礎(chǔ)。
二、 研究的方法與技術(shù)路線(xiàn)（或研究步驟、方法和研究措施）：
研究的基本方法：分析TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)題目的目地和意義，查閱相關(guān)文獻(xiàn)資料搜集相關(guān)信息，結(jié)合國(guó)內(nèi)外相關(guān)現(xiàn)狀，最后規(guī)劃出自己的設(shè)計(jì)方向和思路。
研究的步驟：1查找相關(guān)資料做好設(shè)計(jì)準(zhǔn)備工作；2老師確定課題下發(fā)任務(wù)書(shū)；3下載相關(guān)文獻(xiàn)和外文資料借鑒整理；4近距離觀(guān)察加工中心工作臺(tái)，了解其工作原理；5與老師進(jìn)行學(xué)習(xí)交流；6歸納總匯。
研究措施：在設(shè)計(jì)前期，要充分的了解分度工作臺(tái)的發(fā)展現(xiàn)狀及發(fā)展趨勢(shì)，掌握其動(dòng)作原理、整體構(gòu)造及工作特點(diǎn)，為整個(gè)機(jī)構(gòu)是設(shè)計(jì)捋清思路。在設(shè)計(jì)的過(guò)程中，要經(jīng)常與同組同學(xué)、指導(dǎo)教師一起商討自己的設(shè)計(jì)方案及計(jì)算，做到發(fā)現(xiàn)問(wèn)題及時(shí)修改。在設(shè)計(jì)的后期，要多方位的分析設(shè)計(jì)方案、計(jì)算方法，力爭(zhēng)將本次設(shè)計(jì)做到最佳。最后對(duì)本次設(shè)計(jì)進(jìn)行全面的總結(jié)。
要求：分析TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)的工作原理，設(shè)計(jì)機(jī)械結(jié)構(gòu)裝配圖、零件圖并核算。計(jì)算機(jī)繪圖，符合最新標(biāo)準(zhǔn)；表達(dá)完整，布置合理清晰、尺寸標(biāo)注齊全、技術(shù)要求全面；零件圖同時(shí)要注意結(jié)構(gòu)要素和加工工藝性。
三、 研究的總體安排與進(jìn)度：
2014年11月18日 接受任務(wù)
2014年11月21日－14月5日 熟悉內(nèi)容，完成文獻(xiàn)綜述和英文翻譯；
2014年12月5日－14年12月12日 完成開(kāi)題報(bào)告，畢業(yè)實(shí)習(xí)開(kāi)始 方案確定，計(jì)算和草圖繪制；
2014年12月12日－15年1月12日 方案確定，草圖繪制；進(jìn)給機(jī)構(gòu)計(jì)算、裝配圖繪制；電氣設(shè)計(jì)圖繪制；完成所有零、部件結(jié)構(gòu)設(shè)計(jì)及設(shè)計(jì)說(shuō)明書(shū)；修改圖樣和整理設(shè)計(jì)計(jì)算書(shū)，上交畢業(yè)設(shè)計(jì)資料。
2015年1月12日－15年1月16日 論文評(píng)閱
2015年1月16日－15年1月19日 答辯
四、 論文提綱：
文章前言
文章主體結(jié)構(gòu)
第一章 緒論
第1節(jié)加工中心的功能及其特點(diǎn)
第2節(jié)加工中心的結(jié)構(gòu)組成
第3節(jié)加工中心的發(fā)展趨勢(shì)
第4節(jié)本文研究目的和主要研究?jī)?nèi)容
第二章 TH6363B加工中心分度工作臺(tái)設(shè)計(jì)方案的擬訂
第1節(jié) 加工中心常用回轉(zhuǎn)工作臺(tái)簡(jiǎn)介
第2節(jié)本課題設(shè)計(jì)方案的擬訂
第3節(jié)本課題設(shè)計(jì)的分度工作臺(tái)原理簡(jiǎn)介
第4節(jié)設(shè)計(jì)方案具體操作技術(shù)路線(xiàn)簡(jiǎn)介
第三章 回轉(zhuǎn)工作臺(tái)設(shè)計(jì)
第1節(jié)回轉(zhuǎn)工作臺(tái)的主要設(shè)計(jì)內(nèi)容
1.1電機(jī)的選擇
1.2傳動(dòng)比的分配及傳動(dòng)效率的計(jì)算
1.3同步帶的選擇及同步帶輪的設(shè)計(jì)計(jì)算
1.4圓錐齒輪的設(shè)計(jì)計(jì)算
1.5圓柱齒輪的設(shè)計(jì)計(jì)算
1.6鼠牙盤(pán)的設(shè)計(jì)計(jì)算
1.7液壓系統(tǒng)的設(shè)計(jì)計(jì)算
1.8主要軸承的選擇
1.9潤(rùn)滑與密封
1.10其他元件的選擇
第2節(jié)繪制回轉(zhuǎn)工作臺(tái)總圖和零件圖
文章結(jié)束語(yǔ)
謝辭
參考文獻(xiàn)
五、主要參考文獻(xiàn)：
[1] 陳俊龍，陳俊華。MCV_50A立式加工中心使用探索[J]. 寧波高等專(zhuān)科科學(xué)學(xué)報(bào), 1999
[2] 廉元國(guó)，張永洪. 加工中心設(shè)計(jì)與應(yīng)用［M］.北京：機(jī)械工業(yè)出版社，1995
[3] 機(jī)床設(shè)計(jì)手冊(cè)編寫(xiě)組.機(jī)床設(shè)計(jì)手冊(cè)（第三冊(cè)）［M］. 北京：機(jī)械工業(yè)出版社，1986
[4] 陳蔚芳，機(jī)床數(shù)控技術(shù)及應(yīng)用[J].科學(xué)出版社, 2005
[5] 勞動(dòng)和社會(huì)保障部教材辦公室組織編寫(xiě). 數(shù)控機(jī)床機(jī)械系統(tǒng)[M]. 中國(guó)勞動(dòng)和社會(huì)保障出版社,2004.6
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設(shè)計(jì)（論文）題目：TH6340B臥式加工中心鼠牙盤(pán)式分度工作臺(tái)機(jī)械設(shè)計(jì)
學(xué) 院 名 稱(chēng)： 機(jī)械工程學(xué)院
專(zhuān) 業(yè)： 機(jī)械設(shè)計(jì)制造及其自動(dòng)化
學(xué) 生 姓 名： 劉凱 學(xué)號(hào)： 11403010113
指 導(dǎo) 教 師： 鄭書(shū)華
2014年 12月 10日
一、前言部分
隨著科學(xué)技術(shù)的進(jìn)步與發(fā)展，數(shù)控機(jī)床及加工中心的應(yīng)用已日趨普及，現(xiàn)代數(shù)控加工技術(shù)使得制造過(guò)程發(fā)生了巨大的變化，對(duì)技術(shù)人員的要求也越來(lái)越高。因此，我們要充分了解和熟悉現(xiàn)代數(shù)控機(jī)床的基本知識(shí)，使企業(yè)盡可能投入少，見(jiàn)效快，讓資源合理化運(yùn)用、讓投資更加合適已成為眾多企業(yè)所不可忽視的一項(xiàng)重要任務(wù)。
同時(shí)數(shù)控技術(shù)是實(shí)現(xiàn)機(jī)械制造自動(dòng)化的關(guān)鍵,直接影響到一個(gè)國(guó)家的經(jīng)濟(jì)發(fā)展和綜合國(guó)力,關(guān)系到一個(gè)國(guó)家的戰(zhàn)略地位。作為制造系統(tǒng)最基本的加工單元,以數(shù)控技術(shù)為核心的數(shù)控機(jī)床的生產(chǎn)和應(yīng)用已成為衡量一個(gè)國(guó)家工業(yè)化程度和技術(shù)水平的重要標(biāo)志。世界各國(guó)制造業(yè)廣泛采用數(shù)控技術(shù),以提高制造能力和水平,提高對(duì)市場(chǎng)的適應(yīng)能力和競(jìng)爭(zhēng)力。我國(guó)是制造大國(guó),無(wú)論是從戰(zhàn)略的角度還是從發(fā)展策略上,都需要加強(qiáng)數(shù)控產(chǎn)業(yè)的發(fā)展。而本次介紹的則是數(shù)控機(jī)床代表加工中心的核心部件——工作臺(tái)。
二、主題部分
1數(shù)控機(jī)床發(fā)展歷史
數(shù)控技術(shù)的應(yīng)用給傳統(tǒng)制造行業(yè)帶來(lái)了革命性的變化，使制造行業(yè)成文工業(yè)化的象征。從1952年美國(guó)麻省理工學(xué)院研制出第一臺(tái)試驗(yàn)性數(shù)控系統(tǒng)，數(shù)控系統(tǒng)經(jīng)歷了分立式晶體管、小規(guī)模集成電路、大規(guī)模集成電路、小型計(jì)算機(jī)、超大規(guī)模集成電路、微機(jī)式數(shù)控系統(tǒng)的發(fā)展階段。
我國(guó)數(shù)控技術(shù)起步于20世紀(jì)50年代末期,經(jīng)歷了初期的封閉式開(kāi)發(fā)階段,“六五”、“七五”期間的消化吸收、引進(jìn)技術(shù)階段,“八五”期間建立國(guó)產(chǎn)化體系階段,“九五”期間產(chǎn)業(yè)化階段,現(xiàn)已基本掌握了現(xiàn)代數(shù)控技術(shù),建立了數(shù)控開(kāi)發(fā)、生產(chǎn)基地,培養(yǎng)了一批數(shù)控專(zhuān)業(yè)人才,初步形成了自己的數(shù)控產(chǎn)業(yè)。
2數(shù)控機(jī)床發(fā)展方向——加工中心
帶有自動(dòng)換刀裝置的臥式數(shù)控鏜銑床統(tǒng)稱(chēng)臥式加工中心。智能數(shù)控系統(tǒng)的開(kāi)發(fā)提高了傳統(tǒng)的數(shù)控銑床的加工效率和加工質(zhì)量的能力。臥式加工中心是從數(shù)控鏜銑床基礎(chǔ)上發(fā)展起來(lái)的一種自動(dòng)化加工設(shè)備，他可通過(guò)自動(dòng)換刀，實(shí)現(xiàn)一次裝夾、多工序加工，機(jī)床的功能更強(qiáng)、適用范圍更廣?；诠δ懿考募庸ぶ行脑O(shè)計(jì), 是以模塊化設(shè)計(jì)思想為基礎(chǔ)的產(chǎn)品設(shè)計(jì)，它的制造是以產(chǎn)業(yè)鏈為紐帶的社會(huì)化生產(chǎn)。加工中心是典型的集高新技術(shù)于一體的機(jī)械加工設(shè)備, 它的發(fā)展代表了一個(gè)國(guó)家設(shè)計(jì)和制造業(yè)的水平。
加工中心所能完成的工序是多種多樣的,例如銑、鑊、鉆、鉸、擴(kuò)、惚、攻絲等。機(jī)床的這種多工序性和高度自動(dòng)化的特點(diǎn)是由組成機(jī)床的各個(gè)部份來(lái)共同保證的。近兩年來(lái), 加工中心技術(shù)又有長(zhǎng)遠(yuǎn)的發(fā)展, 主要仍集中在高速化高精度化及智能化三個(gè)方面。
3當(dāng)前研究主題——臥式加工中心鼠牙盤(pán)式分度工作臺(tái)
分度轉(zhuǎn)位工作臺(tái)廣泛應(yīng)用于機(jī)械加工、組合機(jī)床、產(chǎn)品裝配，可以實(shí)現(xiàn)工件一次裝夾后完成多個(gè)工作面的多工序同時(shí)加工。分度轉(zhuǎn)位角度根據(jù)零件的結(jié)椅需要而定。但零件一旦處于新的加工位置后．必須對(duì)回轉(zhuǎn)工作臺(tái)定位，避免加工時(shí)工件的位置發(fā)生變化。分度工作臺(tái)不同于圓周進(jìn)給回轉(zhuǎn)工作臺(tái)，主要完成分廑運(yùn)動(dòng)．分度運(yùn)動(dòng)過(guò)程中不加工。一般分度工作臺(tái)不能實(shí)現(xiàn)無(wú)級(jí)丹莊運(yùn)動(dòng)，而是按照工位數(shù)目確定分度轉(zhuǎn)位角度，分度傳動(dòng)機(jī)構(gòu)實(shí)現(xiàn)回轉(zhuǎn)工作臺(tái)轉(zhuǎn)位，分度精度由定位機(jī)構(gòu)的精度保證。
目前用于加工中心分度工作臺(tái)上的定位機(jī)構(gòu)有銷(xiāo)定位和鼠牙盤(pán)定位兩種。隨著許多行業(yè)對(duì)高精度、快節(jié)拍的加工母機(jī)的大量需求，高速高精臥式加工中心得到飛速發(fā)展。從而對(duì)高精度轉(zhuǎn)臺(tái)的要求也越來(lái)越高。鼠牙盤(pán)式回轉(zhuǎn)工作臺(tái)以其定位精度高、加工過(guò)程性能穩(wěn)定的優(yōu)勢(shì)得到廣泛應(yīng)用。
鼠牙盤(pán)定位的分度工作臺(tái)結(jié)構(gòu)是一種較成熟的優(yōu)越性較廣泛的結(jié)構(gòu)形式。這種工作臺(tái)的結(jié)構(gòu)包括:分度定位機(jī)構(gòu)、轉(zhuǎn)位機(jī)構(gòu)、抬起和夾緊以及位置檢測(cè)機(jī)構(gòu)四部份。鼠牙盤(pán)(端面齒輪)具有高的分度精度,能傳遞大扭矩,所以廣泛用于數(shù)控機(jī)床、加工中心機(jī)床、轉(zhuǎn)塔車(chē)床和鍵床等分度機(jī)構(gòu)和圓分度工作臺(tái)上.鼠牙盤(pán)的嚙合相當(dāng)于一對(duì)帶梯形齒的端齒離合器.嚙合時(shí)由于整個(gè)齒面都能達(dá)到均勻的接觸,分度精度非常高.加工中心機(jī)床上采用鼠牙盤(pán)定位的分度工作臺(tái)的定位精度一般都在士5″左右,最高可達(dá)1.5″.而美國(guó)莫爾144。齒精密分度盤(pán)已達(dá)土0.1″這是銷(xiāo)定位等機(jī)構(gòu)難于實(shí)現(xiàn)的。隨著許多行業(yè)對(duì)高精度、快節(jié)拍的加工母機(jī)的大量需求，高速高精臥式加工中心得到飛速發(fā)展，從而對(duì)高精度轉(zhuǎn)臺(tái)的要求也越來(lái)越高，鼠牙盤(pán)式回轉(zhuǎn)工作臺(tái)以其定位精度高、加工過(guò)程性能穩(wěn)定的優(yōu)勢(shì)得到廣泛應(yīng)用。
三、總結(jié)部分
鼠牙盤(pán)式分度工作臺(tái)作為加工中心的重要組成部分，它對(duì)加工的精度和效率都起到了非常大作用，在未來(lái)的發(fā)展趨勢(shì)中必將會(huì)是主流，我希望通過(guò)本次設(shè)計(jì)培養(yǎng)自己綜合應(yīng)用所學(xué)專(zhuān)業(yè)的基礎(chǔ)理論、基本技能和專(zhuān)業(yè)知識(shí)的能力，加強(qiáng)這方面的學(xué)習(xí)研究。
四、參考文獻(xiàn)
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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 2, pp. 177-182 APRIL 2011 / 177 DOI: 10.1007/s12541-011-0025-8 1. Introduction With the recent rapid development of industry, the need has increased for precision cutting of various kinds of machine parts. In particular, in the cutting industry, it is important to enhance cutting efficiency and precision simultaneously. 1,2 In the field of metal cutting, machining error in milling, drilling and external lathe turning has been studied much more than internal boring. Boring means enlarging a hole that was already cut by drilling, or casting to the designed dimension. Therefore, control of dimensional tolerance and surface roughness is important. 3 Boring is similar to external lathe turning, in the sense of using a single point cutting tool. However, the shape of the boring tool has to be restricted by the workpiece hole diameter and depth. This is the difference between boring and external lathe turning. Generally, the overhang of the boring bar has to be short to guarantee machining stability. Thomas et al. 4 emphasize that because of the reduced damping ratio, the short overhang of the boring tool is good for tool stiffness but poor for vibration. Chun and Ko 5 point out that the change in dynamic stiffness of the boring tool is decided by overhang and dynamic stiffness is increased nonlinearly with overhang length. The sources of machining error are tool deflection and wear, thermal effects, and machine tool errors. Tool deflection caused by cutting forces is a dominant factor in machining errors. 6 The cutting force is separated into main, thrust, and feed cutting forces. Among these, the main and thrust cutting forces induce tool deflection, whereas machining leads to machining error. 7 With recent enhancements in technology, the shapes of cutting tools and workpieces have become more complicated. Therefore, it is difficult to predict the cutting force and tool deflection precisely, and the experience of field operators is inevitable. The purpose of this paper was to identify the effect of overhang and cutting conditions on machining error quantitatively, during internal lathe boring of AISI4140, which is generally used for machine elements. To this end, the response surface method (RSM) 8,9 was applied to establish an estimation model. Similar to the study of Chun and Ko 5 overhang, feed per revolution and cutting depth were chosen as factors for the model. The cutting speed, which is the main factor of built-up edge (BUE) and tool life, was kept constant at 200 m/min. A central composite design was used for the purpose of minimizing the number of experiments. Fitness was verified by analysis of variance (ANOVA), residual analysis, and coefficient of determination after building the first and second regression model, respectively. 2. The Response Surface Method RSM is a collection of mathematical and statistical techniques Study on the Response Surface Model of Machining Error in Internal Lathe Boring Se-Ho Chun 1 and Tae Jo Ko 2,# 1 Graduate School of Mechanical Engineering, Yeungnam University, 214-1, Dae-dong, Gyeonsan, Gyeongbuk, South Korea, 712-749 2 School of Mechanical Engineering, Yeungnam University, 214-1, Dae-dong, Gyeonsan, Gyeongbuk, South Korea, 712-749 # Corresponding Author / E-mail: tjkoyu.ac.kr, TEL: +82-53-810-3836, FAX: +82-53-810-4627 KEYWORDS: Boring Bar, Machining Error, Response Surface Method, Central Composite Design, ANOVA, Residual Analysis To achieve high quality and precision of machining products, the machining error must be examined. The machining error, defined as the difference between designed surface and the actual tool, is generally caused by tool deflection and wear, thermal effects and machine tool errors. Among these error sources, tool deflection is usually known as the most significant factor. The tool deflection problem is analyzed using the instantaneous cutting forces on the cutting edge. This study presents a model of the machining error caused by tool deflection in the internal boring process. The machining error prediction model was described by the surface response method using overhang, feed per revolution and depth of cut as the factors for the analysis. The least square method revealed that overhang and depth of cut were significant factors within 90% confidence intervals. Analysis of variance (ANOVA) and residual analysis show that the second-order model is adequate. Manuscript received: November 23, 2009 / Accepted: November 24, 2010 KSPE and Springer 2011 178 / APRIL 2011 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 2 useful for modeling and analyzing problems in which a response of interest is influenced by several variables, and the objective is to optimize this response. A response surface is a functional relation between response variable and factors. RSM assumes a statistical model with respect to the response surface. Then, the response surface model is estimated with regression analysis of test data generated by several conditions composed from the design factors. Generally, it is difficult to know the response surface formula, and therefore the approximated model is assumed first. After that, this model is verified by lack of fit. In RSM, the first and second order regression models are normally used. The third order regression model can be, but is seldom, used. 8 Central composite design is a representative experimental design of RSM. To estimate the experimental surface with the minimum number of experiments, central and axial points are added in the 2 k experiments, where k means the number of factors. Therefore, sequential experiments are possible here. If the 2 k factor experiments are lack of fit with the first-order regression model, the second-order regression model do not need new experiments but need to adding new data points on the center and axes of the 2 k experiments. To analyze the first and second order regression models simultaneously, in this study the experimental design, including experimental points (2 k ), axial points (2k) and central points (n c ), was selected. Therefore, the total number of experiments was 2 k +2k+n c . 8-11 3. Machining Error Mechanism The cutting force induces deflection in the cutting tool and workpiece. The cutting force is a dominant factor in analyzing machining error from the deflection of cutting tool and workpiece. The cutting tool deflection is analyzed as a response to the instantaneous cutting force. 12 In the case of the boring tool, the cutting force model for analyzing a cutting tool deflection is simplified as the cantilever beam (see Fig. 1). The expression for the cutting tool deflection(x) at position x from the free end point is as follows. 3 () () 3 FLx x EI = (1) where F is the cutting force, L is tool overhang, E and I are the elasticity modulus and moment of inertia of the tool. The deflection of the boring tool is determined by the tool material, diameter and overhang. Obviously, overhang changes according to the clamping position, as shown in Fig. 2. In this case, the tool deflection is composed of deflections m by the main cutting force and t by the thrust cutting force. Deflection by the main cutting force moves the cutting edge under the tool center line. Therefore, the radial rake angle becomes negative, and consequently, the relief angle decreases, which induces large flank wear. 7 In this paper, the difference between tool diameter before machining (designed surface, D ) and workpiece diameter (machined surface, M ) is defined as machining error. Fig. 3 shows the simulation analysis of cutting force variation in Fig. 1 Tool deflection model DM Error = Fig. 2 Deflection of the boring tool Fig. 3 Cutting force analysis by AdvantEdge INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 2 APRIL 2011 / 179 the internal boring using the commercial cutting analysis software of AdvantEdge (ThirdWave Co.). The simulation was conducted with the cutting condition of 0.25 mm/rev, depth of cut of 1 mm, and cutting velocity of 200 m/min. The main (y direction) cutting force is the largest while the thrust (x direction) cutting force is ranked second. The feed (z direction) force is the smallest and it acts on the axial direction of the boring bar. The axial stiffness of the boring bar is sufficient, i.e., the influence of the feed force is negligible. 4. Experiment 4.1 Experimental Design The boring bar and insert were S16R SCLCR 09 and CCMT 09T308 MT TT3500 from TaeguTec, respectively. The boring bar was clamped into the tool holder with a sleeve on the tool post. Overhang was defined as the distance from the end of the insert to the front of the sleeve. The workpiece outer and inner diameter were 80 and 40 mm, respectively, and the inner hole depth was 50 mm. The mechanical properties of the workpiece material AISI4140 are specified in Table 1. Consistency of the experiment was kept by brand new tools and changing new specimen at each time. The horizontal lathe was a DC-2 model (DMC Co.) machine, and the power on the main spindle was 5.5 kW. The deflection of boring bar is affected by the cutting force. The cutting force is dependent on tool-workpiece contact area (chip area) which is composed of feed per revolution and depth of cut. In this experiment, overhang, feed per revolution, and depth of cut were selected as the design factors in the sense of the tool deflection. Cutting velocity was set as constant. A central composite design was selected to construct the response model with respect to characteristic values. Uncoded variables were notated as 1 (Overhang), 2 (Feed), and 3 (Cutting depth). The experimental range was assumed to be 1L , 1H , 2L , 2H , and 3L , 3H , and the relations between real and coded variables were determined by Eqs. (2), (3), and (4), as follows. The inverted data is shown in Table 2. 9 11 1 11 2.4( ) HL X = (2) 22 2 22 2.4( ) HL X = (3) 33 3 33 2.4( ) HL X = (4) where 11 1 , 2 LH + = 22 2 2 L H + = and 33 3 . 2 L H + = For all the cutting conditions, the machining power was limited below 70% of the main spindle power. To avoid the ploughing force 13,16 that is mainly influenced by the size effect, the minimum feed per revolution was set higher than 0.03 mm/rev considering the size of the honing dimension at the insert edge. Because the distance from the central to the axial point in experiment design was 1.2, all the cutting conditions were in the allowable range. As shown in Fig. 4, the total number of experiments was 18 based on experiment points (18), axial points (614) and central points (1518). 4.2 Experimental Results Table 3 shows the experimental results according to the designed cutting conditions. As defined in Fig. 2, machining error Table 1 Mechanical properties of AISI4140 Specification Value Yield strength (kg f /mm 2 ) 85 Tensile strength (kg f /mm 2 ) 100 Elongation (%) 12 Reduction of area (%) 45 Charpy impact value (kg f m/cm 2 ) 6 Hardness (HB) 285352 Table 2 Levels of the variables in the experiment Coding 1.2 1 0 1 1.2 Overhang (mm) 30.4 32 40 48 49.6 Feed (mm/rev) 0.03 0.05 0.15 0.25 0.27 Depth of cut (mm) 0.12 0.2 0.6 1 1.08 Fig. 4 Central composite design for experiment 14 Table 3 Design of experiment and results X 1 2 X 3 Error 1 1 1 1 0.105 2 1 1 1 0.281 3 1 1 1 0.144 4 1 1 1 0.289 5 1 1 1 0.162 6 1 1 1 0.275 7 1 1 1 0.153 8 1 1 1 0.304 9 1.2 0 0 0.192 10 1.2 0 0 0.336 11 0 1.2 0 0.190 12 0 1.2 0 0.189 13 0 0 1.2 0.133 14 0 0 1.2 0.287 15 0 0 0 0.184 16 0 0 0 0.180 17 0 0 0 0.191 18 0 0 0 0.187 180 / APRIL 2011 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 2 was defined as the difference between the virtual diameter generated by the air cutting state and the measured internal workpiece diameter after machining. The measurement position for the internal diameter was 10 mm engaging distance from the start of machining, which was selected as the representative data for analysis. If the aspect ratio, defined as diameter to length (L/D) in the lathe process, is less than 4, the machining accuracy is almost the same in all the machined areas, because the workpiece is strong enough. 15 In this experiment, the aspect ratio was smaller than 2, so the variation in measurement data is negligible along with the other measuring places. 5. Analysis of Results and Prediction Model 5.1 First-Order Regression Model Equation (5) shows the first-order regression model that is composed of newly defined independent variables by Eqs. (2), (3), and (4). The coefficients estimated by the least squares method are shown in Table 4. 0112233 12 1 2 13 1 3 23 2 3 YXXX XX XX XX =+ + + + (5) Considering the coefficients within 90% significance level, 2 , 12 , 13 , 23 are factors that decrease the models accuracy. The effects of feed per revolution and its interaction terms are insignificant. To improve the model, estimation has to be done without insignificant factors. Expression (6) shows the re-estimated model after removing the insignificant factors, such as X 2 and its interaction effects. 13 0.210111 0.069651 0.023879YXX=+ + (6) Table 5 shows the ANOVA for the estimated model, and the coefficient of determination is 0.77 for the first-order regression. To verify the regression model, we performed residual analysis. The normal probability plot and residual histogram from this analysis are depicted in Figs. 5 and 6, respectively. As shown in Fig. 5, the departures are scattered. It indicates the abnormalities in the residual distribution. Alternatively, the residual histogram shows that frequency of the residual is not satisfied with the normal distribution and the frequency of residual is highest between -0.01 and -0.03. This means that the first-order regression model is weak in explaining the machining errors. 5.2 Second-Order Regression Model The second-order regression model is expressed as Eq. (7), and the estimated coefficients by the least squares method are shown in Table 6. 222 011223311 22 33 12 1 2 13 1 3 23 2 3 YXXXXXX XX XX XX =+ + + + + + + (7) Here, the influence of overhang is larger than the other factors to the machining errors. Since its squared term is also significant, the response surface will be curved with respect to the change in the factor level. Similarly, with the first order regression model, the terms of feed per revolution and its interaction are insignificant within the 90% significance level. Therefore, those factors are pulled down to error terms, and finally, a new estimation model, shown in Eq. (8), was taken. The coefficient of determination estimated from Eq. (8) was 0.852, which was an improvement over the first-order regression model. 2 131 0.18979 0.06965 0.02388 0.03362YXXX=+ + + (8) Table 7 shows the ANOVA result of the second-order regression model, and on the other hand, Figs. 7 and 8 show the results of Table 4 First-order regression coefficient Coef. Coef. SE T P 0 0.210111 0.009199 22.840 0.000 1 0.069651 0.011832 5.887 0.000 2 0.006048 0.011832 0.511 0.619 3 0.023879 0.011832 2.018 0.069 12 0.000875 0.013799 0.063 0.951 13 0.007215 0.013799 0.516 0.616 23 0.003375 0.013799 0.245 0.811 Notes) Coef. SE: Standard error of a coefficient, T: T-test, P: Probability of type I error Table 5 ANOVA of first regression model DF Seq SS Adj. MS F P Regression 2 0.0589 0.02949 25.05 0.000 Linear 2 0.0589 0.02949 25.05 0.000 Residual Error 15 0.0176 0.00117 Pure Error 9 0.0013 0.00014 Sum 17 0.0766 Notes) DF: Degree of freedom, Seq. SS: Sequential sum of squares, Adj. MS: Adjusted mean squares, F: Fisher statistic(F-test) Fig. 5 Normal probability plot of the residuals Fig. 6 Histogram of the residuals INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 2 APRIL 2011 / 181 normal probability and histogram of the residuals, respectively. As shown in Fig. 7, the result is enhanced more than the first-order regression model. Also, the frequency of the residual was satisfied with the normal distribution as shown in Fig. 8, and the second- order regression model was suitable for explaining the machining errors. 5.3 Contour Plot and Surface Plot A contour plot expresses the response surface in the second- dimensional plane. On the other hand, a surface plot expresses the response surface in the third-dimensional space to explain the response values. Figures 9 and 10, respectively, show the contour and surface plots of the second-order regression model. All points on the contour plot were experimental points, and the factor X 2 was fixed to zero as a median. The contours of X 1 direction are denser than X 3 direction. Alternatively, in Fig. 10, the machining error rises sharply with increase of X 1 . 6. Conclusions The purpose of this study was to build an estimation model for machining errors during internal boring of SCM440 materials. The experiment was performed according to a central composite design with three factors that were believed to be parameters in machining errors. RSM was adopted to estimate machining errors. Alternatively, through ANOVA and residual analysis, the significance of factors and the fitness of the designed model were verified. From the experimental results and model analysis, the following conclusions were drawn. 1. The second-order regression model is more suitable than the first-order regression model for describing the internal boring process, from the view point of ANOVA and residual analysis. In this case, the second regression models coefficient of determination was 0.852. 2. Overhang and depth of cut were relatively more significant than feed per revolution in terms of machining errors. Table 6 Second-order regression coefficient Coef. Coef. SE T P 0 0.196336 0.01475 13.308 0.000 1 0.069651 0.01038 6.712 0.000 2 0.006048 0.01038 0.583 0.576 3 0.023879 0.01038 2.301 0.050 11 0.037342 0.01532 2.437 0.041 22 0.014394 0.01532 0.939 0.375 33 0.000158 0.01532 0.010 0.992 12 0.000875 0.01210 0.072 0.944 13 0.007125 0.01210 0.589 0.572 23 0.003375 0.01210 0.279 0.787 Table 7 ANOVA of second regression model DF Seq SS Adj MS F P Regression 3 0.0652 0.02176 26.82 0.000 Linear 2 0.0589 0.02949 36.35 0.000 Square 1 0.0062 0.00629 7.76 0.015 Residual Error 14 0.0113 0.00081 Pure Error 9 0.0013 0.00014 Sum 17 0.0766 Fig. 7 Normal probability plot of the residuals Fig. 8 Histogram of the residuals Fig. 9 Contour plot of machining error Fig. 10 Surface plot of machining error 182 / APRIL 2011 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 2 3. It is recommended that the control of cutting depth is more effective method for minimizing the machining error in internal lathe boring. Also, short overhang is preferable. This study can be referenced when designing the hole depth and tolerance of products. ACKNOWLEDGEMENT This research was supported by the Yeungnam University research grants in 2008. REFERENCES 1. Onwubolu, C. G., “A Note on Surface Roughness Prediction Model in Machining of Carbon Steel by PVD Coated Cutting Tools,” American Journal of Applied Sciences, Vol. 2, No. 6, pp. 1109-1112, 2005. 2. Sharma, S. V., Dhiman, S., Sehgal, R. and Sharma, S. K., “Assessment and Optimization of Cutting Parameters while Turning AISI 52100 Steel,” Int. J. Precis. Eng. Manuf., Vol. 9, No. 2, pp. 54-62, 2008. 3. Beauchamp, Y., Thomas, M., Youssef, Y. A. and Masounave, J., “Investigation of Cutting Parameter Effects on Surface Roughness in Lathe Boring Operation by Use of a Full Factorial Design,” 18 th International Conference on Computers and Industrial Engineering, Vol. 31, No. 3-4, pp. 645-651, 1996. 4. Thomas, M. and Beauchamp, Y., “Statistical Investigation of Modal Parameters of Cutting Tools in Dry Turning,” International Journal of Machine Tools and Manufacture, Vol. 43, No. 11, pp. 1093-1106, 2003. 5. Chun, S. H. and Ko, T. J., “Study on the Dynamic Stiffness Variation of Boring Bar by Taguc
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