帶側(cè)臺方套管模具設計（全套含CAD圖紙）

帶側(cè)臺方套管模具設計（全套含CAD圖紙）,帶側(cè)臺方,套管,模具設計,全套,CAD,圖紙 參加畢業(yè)設計（論文）答辯申請書 學生姓名 學院 機械工程學院 專業(yè)班級 機械134 題　　目 帶側(cè)臺方套管注塑模具設計 指導教師 答辯前學生填寫畢業(yè)設計（論文）工作總結(jié)和參加答辯申請：方套管模具是注塑模具中比較典型的 模具當然也有它自身的特點。典型在于它是方柱形，內(nèi)有貫通的圓形孔，形狀比較規(guī)則，相對比較 對稱；它的特點在于柱子的長度相比于截面的圓直徑比較大，所軸向長度很大，而且他的一個面上 有圓柱凸臺且有通孔。所以在設計時必須考慮測抽芯，哈呋分型的設計等很多問題。在設計過程中， 主要對模具的型芯、型腔、澆注系統(tǒng)、導向系統(tǒng)和脫模系統(tǒng)進行了精密的計算和合理的選擇。設計 中主要運用了Pro/E和Auto CAD軟件，根據(jù)制件的零件圖繪制了模具的各個零件圖和裝配圖。 指導教師對學生參加畢業(yè)設計（論文）答辯的意見： 　　　　　　　　　　　　　　指導教師：　　　　　　　　年　　 月　 　　日 畢業(yè)設計(論文)成績考核表 學生姓名 專業(yè)班級 機械134 題 目 帶側(cè)臺方套管注塑模具設計 評 審 者 考 核 項 目 評分（每項滿分10分） 指導教師 1 工作態(tài)度與遵守紀律的情況 2 掌握基本理論、專業(yè)知識、基本技能的程度和閱讀外文的水平 3 獨立工作能力、綜合運用所學知識分析和解決問題能力及實際工作能力提高的程度 4 完成任務的情況與水平（工作量及說明書、論文與圖紙、軟、硬件實物成果質(zhì)量和創(chuàng)新性） 評閱人 5 說明書、論文質(zhì)量（正確性、條理性、創(chuàng)造性和實用性）及圖紙質(zhì)量（視圖完整、布局合理、圖面整潔、線條清晰、標注規(guī)范） 6 技術水平（理論、分析、計算、實驗和軟、硬件實物性能以及創(chuàng)新性） 答辯小組（或答辯委員會） 7 完成任務的情況與水平（按規(guī)范化要求） 8 說明書、論文、圖紙與實物成果的質(zhì)量和創(chuàng)新性 9 答辯時講述的條理性及系統(tǒng)性 10 回答問題的正確性 總評成績 總評成績等級（優(yōu)、良、中、及格、不及格） 指導教師簽字： 評閱人簽字： 答辯小組組長（答辯委員會主任）簽字： 注：此表一式兩份，一份由教研室保存，另一份裝入學生畢業(yè)設計（論文）檔案袋中。 畢業(yè)設計(論文)成績考核表 學生姓名 專業(yè)班級 機械134 題 目 帶側(cè)臺方套管注塑模具設計 評 審 者 考 核 項 目 評分（每項滿分10分） 指導教師 1 工作態(tài)度與遵守紀律的情況 2 掌握基本理論、專業(yè)知識、基本技能的程度和閱讀外文的水平 3 獨立工作能力、綜合運用所學知識分析和解決問題能力及實際工作能力提高的程度 4 完成任務的情況與水平（工作量及說明書、論文與圖紙、軟、硬件實物成果質(zhì)量和創(chuàng)新性） 評閱人 5 說明書、論文質(zhì)量（正確性、條理性、創(chuàng)造性和實用性）及圖紙質(zhì)量（視圖完整、布局合理、圖面整潔、線條清晰、標注規(guī)范） 6 技術水平（理論、分析、計算、實驗和軟、硬件實物性能以及創(chuàng)新性） 答辯小組（或答辯委員會） 7 完成任務的情況與水平（按規(guī)范化要求） 8 說明書、論文、圖紙與實物成果的質(zhì)量和創(chuàng)新性 9 答辯時講述的條理性及系統(tǒng)性 10 回答問題的正確性 總評成績 總評成績等級（優(yōu)、良、中、及格、不及格） 指導教師簽字： 評閱人簽字： 答辯小組組長（答辯委員會主任）簽字： 注：此表一式兩份，一份由教研室保存，另一份裝入學生畢業(yè)設計（論文）檔案袋中。 畢業(yè)設計(論文)答辯記錄卡 學生姓名 專業(yè)班級 機械134 題 目 帶側(cè)臺方套管注塑模具設計 自述情況： 回答問題情況： 答辯得分 1 2 3 4 5 6 7 8 9 10 總分 平均得分 答辯地點： 記錄人： 年 月 日 　 畢業(yè)設計(論文)評閱書 題 目 帶側(cè)臺方套管注塑模具設計 　 學生姓名 專業(yè)班級 機械134 所在學院 機械工程學院 指導教師 職　稱 副教授 所在單位　 　　 1.指導教師評語： 簽名： 年 月 日 2.評閱人評語： 簽名： 　　　　年 月 日 3.答辯小組評語： 簽名： 　　　　年 月 日 　 畢業(yè)設計(論文)評閱書 題 目 帶側(cè)臺方套管注塑模具設計 　 學生姓名 專業(yè)班級 機械134 所在學院 機械工程學院 指導教師 職　稱 副教授 所在單位　 　　 1.指導教師評語： 簽名： 年 月 日 2.評閱人評語： 簽名： 　　　　年 月 日 3.答辯小組評語： 簽名： 　　　　年 月 日 　 畢業(yè)設計(論文)評閱書 題 目 帶側(cè)臺方套管注塑模具設計 　 學生姓名 專業(yè)班級 機械134 所在學院 機械工程學院 指導教師 職　稱 副教授 所在單位　 　　 1.指導教師評語： 簽名： 年 月 日 2.評閱人評語： 簽名： 　　　　年 月 日 3.答辯小組評語： 簽名： 　　　　年 月 日 調(diào)研報告 一、注塑模來源及意義 注塑模具起源：在1943年，位于葡馬立尼亞.格蘭特市（Marinha Grande）一家小型玻璃模具廠股東阿尼巴爾（AníbalH.Abrantes）萌發(fā)了生產(chǎn)注塑模的構想。由于未能獲得其他股東的支持，阿尼巴爾不得不出售自己擁有的公司股份已籌集資金，并開始專注于注塑模具的研發(fā)與制造。兩年后，他成功的制造了第一只注塑模具。此后，在馬立尼亞.格蘭特市和奧利維拉.德.阿澤麥伊斯市(oliveiradeAzeméis)（葡萄牙另一傳統(tǒng)玻璃工業(yè)區(qū)）逐步出現(xiàn)其它注塑模具企業(yè)。隨著國外先進技術的引進，葡模具工藝水平不斷提高，并于1955年首次實現(xiàn)模具出口，產(chǎn)品銷往英國。 注塑模具設計的意義：塑料工業(yè)是當今世界上增長最快的工業(yè)門類之一，而注塑模具是其中發(fā)展較快的種類。因此，研究注塑模具對了解塑料產(chǎn)品的生產(chǎn)過程和提高產(chǎn)品質(zhì)量有很大意義[1]。 模具是制造業(yè)的一種基本工藝裝備，它的作用是控制和限制材料（固態(tài)或液態(tài)）的流動，使之形成所需要的形體。用模具制造零件以其效率高，產(chǎn)品質(zhì)量好，材料消耗低，生產(chǎn)成本低而廣泛應用于制造業(yè)中。模具工業(yè)是國民經(jīng)濟的基礎工業(yè)，是國際上公認的關鍵工業(yè)。模具生產(chǎn)技術水平的高低是衡量一個國家產(chǎn)品制造水平高低的重要標志，它在很大程度上決定著產(chǎn)品的質(zhì)量，效益和新產(chǎn)品的開發(fā)能力。振興和發(fā)展我國的模具工業(yè)，正日益受到人們的關注[2－3]。 模具工業(yè)既是高新技術產(chǎn)業(yè)的一個組成部分，又是高新技術產(chǎn)業(yè)化的重要領域。模具在機械，電子，輕工，汽車，紡織，航空，航天等工業(yè)領域里，日益成為使用最廣泛的主要工藝裝備，它承擔了這些工業(yè)領域中60％～90％的產(chǎn)品的零件，組件和部件的生產(chǎn)加工[4]。 二、模具國內(nèi)外發(fā)展現(xiàn)狀 國內(nèi)注塑模具的發(fā)展現(xiàn)狀：目前，國內(nèi)生產(chǎn)的小模數(shù)塑料齒輪等精密塑料模具已達到國外同類產(chǎn)品水平。在齒輪模具設計中采用最新的齒輪設計軟件，糾正了由于成型壓縮造成的齒形誤差，達到了標準漸開線造型要求。顯示管隔離器注塑模、高效多色注射塑料模、純平彩電塑殼注塑模等精密、復雜、大型模具的設計制造水平也已達到或接近國際水平。使用CAD三維設計、計算機模擬注塑成形、抽芯脫模機構設計新穎等對精密、復雜模具的制造水平提高起到了很大作用。20噸以上的大型塑料模具的設計制造也已達到相當高的水平。34英寸彩電塑殼和48英寸背投電視機殼模具，汽車保險杠和儀表盤的注塑模等大型模具，國內(nèi)都已可生產(chǎn)。國內(nèi)最大的塑料模具已達50噸[5]。?? 20世紀80年代開始，發(fā)達工業(yè)國家的模具工業(yè)已從機床工業(yè)中分離出來，并發(fā)展成為獨立的工業(yè)部門，其產(chǎn)值已超過機床工業(yè)的產(chǎn)值。改革開放以來，我國的模具工業(yè)發(fā)展也十分迅速。近年來，每年都以15％的增長速度快速發(fā)展。許多模具企業(yè)十分重視技術發(fā)展。加大了用于技術進步的投入力度，將技術進步作為企業(yè)發(fā)展的重要動力[6]。此外，許多科研機構和大專院校也開展了模具技術的研究與開發(fā)。模具行業(yè)的快速發(fā)展是使我國成為世界超級制造大國的重要原因。今后，我國要發(fā)展成為世界制造強國，仍將依賴于模具工業(yè)的快速發(fā)展，成為模具制造強國。 國外注塑模具的發(fā)展現(xiàn)狀：國外注塑模具制造行業(yè)的最基本特征是高度集成化、智能化、柔性化和網(wǎng)絡化。追求的目標是提高產(chǎn)品質(zhì)量及生產(chǎn)效率。國外發(fā)達國家模具標準化程度達到70%～80%，實現(xiàn)部分資源共享，大大縮短設計周期及制造周期，降低生產(chǎn)成本.最大限度地提高模具制造業(yè)的應變能力 滿足用戶需求。模具企業(yè)在技術上實現(xiàn)了專業(yè)化，在模具企業(yè)的生產(chǎn)管理方面，也有越來越多的采用以設計為龍頭、按工藝流程安排加工的專業(yè)化生產(chǎn)方式，降低了對模具工人技術全面性的要求，強調(diào)專業(yè)化[7－8]。? 　　國外注塑成型技術在也向多工位、高效率、自動化、連續(xù)化、低成本方向發(fā)展。因此，模具向高精度復雜、多功能的方向發(fā)展。例如：組合模、即鈑金和注塑一體注塑鉸鏈一體注塑、活動周轉(zhuǎn)箱一體注塑；多色注塑等；向高效率、高自動化和節(jié)約能源，降低成本的方向發(fā)展。例如：疊模的大量制造和應用，水路設計的復雜化、裝夾的自動化、取件全部自動化[9]。? 國內(nèi)外注塑模具發(fā)展狀況對比：隨著塑料制品在社會發(fā)展中的廣泛應用，模具技術已成為衡量一個國家制造業(yè)發(fā)展水平的重要標志之一，標準化、智能化、網(wǎng)絡化成為工業(yè)發(fā)達國家注塑模具制造業(yè)的基本特征。近些年來，隨著我國注塑行業(yè)的發(fā)展和先進制造技術的研發(fā)與引進，我國注塑模具的制造水平也得到了很大的提高。但是由于起步晚、基礎薄弱、技術設備、管理水平都比較低等問題，我國的注塑模具總水平還與國外存在10 年以上差距。 （1）注塑模具的精度 在塑料成型方面，注塑模具具有其他模具無法比擬的優(yōu)勢，隨著人們需求的不斷提高，對塑件的精度要求也越來越高，高精度的模具是生產(chǎn)出高精度塑件的保證。模具的精度通常包括尺寸精度、形狀精度、位置精度、表面精度等四個方面。目前，很多工業(yè)發(fā)達國家注塑模型腔的精度達到了0. 005 ～ 0. 001 mm，型腔表面的粗糙度（Ra）為0. 10 ～ 0. 05 μm。 隨著零件微型化的發(fā)展，我國注塑模具的精度程度也在不斷提高，十年前，我國注塑模型腔的精度一般為0. 05 mm，現(xiàn)在已達到0. 02 ～ 0. 03 mm［10］，型腔表面的粗糙度（Ra）達到0. 20 μm。注塑模具的這些發(fā)展對于提高手機、數(shù)碼相機等塑件的精確程度具有重要的意義。 （2） 熱流道模具使用率 熱流道模具是采用絕熱或加熱方式，使流道內(nèi)熔體始終保持熔融狀態(tài)的模具。其起始階段發(fā)展緩慢，隨著制造工業(yè)的發(fā)展，由于熱流道模具具有生產(chǎn)周期短、節(jié)省原材料、提高生產(chǎn)率等優(yōu)點，成為注塑模具發(fā)展的熱點之一。當前，歐美發(fā)達工業(yè)國家和地區(qū)的熱流道模具使用率占塑料模具總量的比例的60% ～70%及以上［11］。 在我國模具制造業(yè)中，傳統(tǒng)的冷流道技術仍然處于主導地位。熱流道模具生產(chǎn)的技術難度大、價格昂貴等因素，在我國的發(fā)展速度非常緩慢，其使用率不足10%，其中擁有自主產(chǎn)權的熱流道模具技術比較少，尚處于逐步應用和自主開發(fā)階段，很多設備依賴于進口。但是，隨著我國塑料制造業(yè)的發(fā)展，近些年來熱流道模具在國內(nèi)的迅速推廣，而且出現(xiàn)了深圳熱流道科技公司、浙江的貝佳熱流道公司等［12－13］ 專業(yè)的熱流道模具制造商。 （3）標準化程度 模具標準化工作主要包括模具塑件的標準化生產(chǎn)、模具技術的標準化執(zhí)行等。模具的標準化生產(chǎn)可以節(jié)省材料、縮短制造周期、降低成本，能給制造工業(yè)帶來效率和效益［14］，因此其代表未來注塑模具的一個重要發(fā)展方向。目前，發(fā)達國家模具標準化程度達到70% ～ 80%，其專業(yè)模具制造廠具有分工細、人員少、采用計算機管理、產(chǎn)值高等特點，其模具的研發(fā)與生產(chǎn)已形成了完善的體系。據(jù)統(tǒng)計，德國HASCO 公司標準件規(guī)格達3 萬多種。 我國也于1983 年1O 月在長沙成立了全國模具標準化技術委員會［15］。近些年來，隨著國外先進技術的引進，我國注塑模具的標準化程度也在不斷提高，從目前的形勢看，我國模具標準件的推廣和應用有很大的市場潛力。但是，由于我國注塑模具的標準化工作起步較晚，其標準化程度不足30%，遠遠落后于美國、日本等工業(yè)發(fā)達國家，特別是精密、復雜的注塑模具標準化程度更低。 （4）注塑模具的使用壽命 在注塑成型過程中，模具的使用壽命直接關系到生產(chǎn)效率的高低?，F(xiàn)代注塑模具的壽命比傳統(tǒng)注塑模具的壽命要高出5 ～ 10 倍，使用壽命長是現(xiàn)代注塑模具發(fā)展的主要目標之一。以淬火鋼模具壽命為例，目前許多工業(yè)發(fā)達國家的淬火鋼模具壽命高達160 萬～300 萬次。 模具材料的選用、模具的設計與維護都對模具的使用壽命具有重要的影響。隨著先進技術的使用，我國注塑模具的使用壽命也不斷增長，淬火鋼模具壽命高達到了50 萬～ 100 萬次［16］，與發(fā)達國家相比還存在一定的差距。 總之，我國在注塑模具先進技術的研發(fā)與應用方面還與發(fā)達國家存在一定的差距，注塑模的精度、熱流道模具使用率、模具的使用壽命、標準化程度等都有待于進一步提高。所以，我國應加大技術投入，重視技術創(chuàng)新，使我國的注塑模具得到快速高效的發(fā)展。 三、研究目標 帶側(cè)臺方套管注塑模設計。 四、研究內(nèi)容 1、模具總體方案設計； 2、澆注系統(tǒng)、導向機構、脫模機構、排氣系統(tǒng)等的結(jié)構設計； 3、模具零部件以及整個模具裝配的二維和三維圖； 五、研究方法和手段 通過圖書館查找資料、與在網(wǎng)絡上自學了很多模具方面的知識，并在設計過程中，主要對模具的型芯、型腔、澆注系統(tǒng)、導向系統(tǒng)和脫模系統(tǒng)等進行了精密的計算和合理的選擇。最后使用三維繪圖軟件繪制了模具的各個零件圖和裝配圖。 六、進度安排 1、外文翻譯； 2、撰寫調(diào)研報告； 3、用proe進行塑件的建模； 4、擬定模具的結(jié)構形式； 5、成型零件設計計算； 6、模架選擇； 7、澆注系統(tǒng)的設計排氣槽設計脫模推出機構設計； 8、冷卻系統(tǒng)設計，導向與定位機構設計； 9、三維圖與二維圖的繪制； 10、畢業(yè)設計計算說明書； 六、總結(jié) 雖然在這十多年中注塑模具工業(yè)取得了令人矚目的發(fā)展，但許多方面與工業(yè)發(fā)達國家相比仍有較大的差距。精密加工設備在模具加工設備中的比重還比較低，CAD/CAE/CAM技術的普及率不高，許多先進的模具技術應用還不夠廣泛等。特別在大型、精密、復雜和長壽命模具技術上存在明顯差距，這些類型模具的生產(chǎn)能力也不能滿足國內(nèi)需求，因而需要大量從國外進口。 參考文獻 [1]馬金駿.塑料模具設計（修訂版）[M].北京：中國科學技術出版社，1994. [2]王樹勛.注塑模具設計與制造實用技術[M].廣州：華南理工大學出版,1993. [3]屈華昌.塑料成型工藝與模具設計[M].北京：機械工業(yè)出版社，2003. [4]梁艷豐． 注塑模結(jié)構設計要點分析［J］． 中國科技縱橫，2010 ( 9) : 21． [5]洪慎章． 現(xiàn)代模具技術的現(xiàn)狀及發(fā)展趨勢［J］．航空制造技術，2006 ( 6) : 30－32． [6]周永泰． 中國模具工業(yè)的現(xiàn)狀與發(fā)展［J］． 電加工與模具，2005 ( 4) : 8－12． [7]楊守濱． 淺談注塑模具先進制造關鍵技術的發(fā)展［J］．科技創(chuàng)新導報，2008 ( 2) : 78． [8]王昌，胡修鑫． 注塑模具的先進制造技術綜述［J］． 機床與液壓，2012，40 ( 14) : 123－125． [9]姜愛菊，吳宏武． 微注射成型的最新進展［J］． 塑料工業(yè)，2008，31 ( 8) : 1－4． [10]江健． 淺析注塑模具的發(fā)展［J］． 廣西輕工業(yè)，2011( 3) : 51－52． [11]WILDEＲ Ｒ V． Hot Ｒunners ［J］． Plast Technol，2003，49 ( 9) : 23． [12]梅啟武． 注塑模熱流道輔助設計技術與應用研究［D］．杭州: 浙江大學，2004: 1 －11． [13]李聰，李輝． 淺談注塑模具中一些先進技術［J］．電子世界，2011 ( 9) : 38－39． [14]許發(fā)鍵． 模具標準化及其生產(chǎn)技術［J］． 現(xiàn)代制造，2004 ( 9) : 46－48． [15] 宋滿倉． 注塑模具設計與制造標準化體系的研究［D］．大連: 大連理工大學，2004: 1－14． [16] 阮雪榆，李志剛，武兵書，等． 中國模具工業(yè)和技術的發(fā)展［J］． 模具技術，2001 （2）: 72－74． 5 ORIGINAL ARTICLEOptimization of injection molding process parametersusing integrated artificial neural network modeland expected improvement function methodHuizhuo Shi&Yuehua Gao&Xicheng WangReceived: 16 October 2008 /Accepted: 24 September 2009 /Published online: 20 November 2009#Springer-Verlag London Limited 2009Abstract In this study, an adaptive optimization methodbased on artificial neural network model is proposed tooptimize the injection molding process. The optimizationprocess aims at minimizing the warpage of the injectionmolding parts in which process parameters are designvariables. Moldflow Plastic Insight software is used toanalyze the warpage of the injection molding parts. Themold temperature, melt temperature, injection time, packingpressure, packing time, and cooling time are regarded asprocess parameters. A combination of artificial neuralnetwork and design of experiment (DOE) method is usedto build an approximate function relationship betweenwarpage and the process parameters, replacing the expensivesimulation analysis in the optimization iterations. Theadaptive process is implemented by expected improvementwhich is an infilling sampling criterion. Although the DOEsize is small, this criterion can balance local and global searchand tend to the global optimal solution. As examples, acellular phone cover and a scanner are investigated. Theresults show that the proposed adaptive optimization methodcan effectively reduce the warpage of the injection moldingparts.Keywords Injectionmolding.Warpage.Optimization.Design ofexperiment.Artificialneuralnetwork.Expectedimprovementfunction1 IntroductionInjection molding is the most widely used process forproducingplastic products.The entireinjectionmoldingcyclecan be divided into three stages: filling, post-filling, and moldopening 1. During production, warpage is one of the mostimportantqualityproblems,especiallyforthethin-shellplasticproducts.Severalresearcheshavebeendevotedtothewarpageoptimization of thin-shell plastic parts 29. Warpage can bereduced by modifying the geometry of parts, or changing thestructure of molds, or adjusting the process parameters.The part design and mold design are usually determined inthe initial stage of product development, which cannot beeasily changed. Therefore, optimizing process parameters isthe most feasible and reasonable method.It is an important issue in plastic injection molding topredict and optimize the warpage before manufacturingtakes place. Many literatures have been devoted to warpageoptimization. Lee and Kim 10 optimized the wallthickness and process conditions using the modifiedcomplex method to reduce warpage and obtained areduction in warpage of over 70%. Sahu et al. 11optimized process conditions to reduce warpage by acombined implementation of the modified complex methodand design of experiments. Their results showed that thesemethods can effectively reduce warpage.Although these methods can reduce warpage effectively,they are costly and time-consuming because they performlots of expensive function evaluations. Compared to thesemethods, the Taguchi method 1214 is easier to performandcananalyze the effective factors,but itcanonlyobtainthebetter combination of process parameters, not the optimalsolution in the design space.The warpage is a nonlinear and implicit function of theprocess parameters, which is typically estimated by theH. Shi:Y. Gao:X. Wang (*)State Key Laboratory of Structural Analysis for IndustrialEquipment, Dalian University of Technology,Dalian,116024 Liaoning, Chinae-mail: Int J Adv Manuf Technol (2010) 48:955962DOI 10.1007/s00170-009-2346-7solution of finite element equations. In general, a complicatedtask often requires huge computational cost. Hence, in orderto reduce the computational cost in warpage optimization,many researchers have introduced some surrogate models,such as Kriging surrogate model, artificial neutral network(ANN), response surface method, and support vector regres-sion. Gao et al. 1517 optimized process conditions toreduce the warpage by combining the kriging surrogatemodel with modified rectangular grid approach or expectedimprovement (EI) function method. Kurtaran et al. combinedthe genetic algorithms with a neural network or responsesurface method to optimize the process parameters forreducing the warpage of plastic parts 18, 19. Zhou et al.20 optimized injection molding process using supportvector regression model and genetic algorithm. Their resultshave shown that the methods based on the surrogate modelcan reduce the high computational cost in the warpageoptimization, and the genetic algorithm can be used toapproach to the global optimal design effectively.In this study, the mold temperature, melt temperature,injection time, packing pressure, packing time, and coolingtime are considered as process parameters. A small-sizedesign of experiment is obtained by Latin hypercube design(LHD), and the warpage values are evaluated by MoldFlowPlastic Insight software. An adaptive optimization based onartificial neural network model is proposed. The adaptiveprocess is performed by an EI function, which canadaptively select the additional sample points to improvethe surrogate model and find the optimum value 17. Thismethod has been viewed as effective global optimization21. The numerical results show that this method canreduce warpage efficiently.2 Artificial neural networkANN is a powerful tool for the simulation and prediction ofnonlinear problems. A neural network comprises manyhighly interconnected processing units called neurons. Eachneuron sums weighted inputs and then applies a linear ornonlinear function to the resulting sum to determine theoutput, and all of them are arranged in layers and combinedthrough excessive connectivity.The typical ANN is a back propagation network (BPN)2226 which has been widely used in many researchfields. A BPN has hierarchical feed-forward networkarchitecture, and the output of each layer is sent directlyto each neuron in the layer above. Although a BPN canhave many layers, all pattern recognition and classificationtasks can be accomplished with a three-layer BPN 27. pac ktct packP Warpage1mel tTmoldT 6 int 6Fig. 1 Configuration of the ANN model Add the modified design as a new sample in set of samplesStartGenerate a set of samples Run Moldflow to generate corresponding warpage values Perform ANN simulation Optimize EI function Is the convergence criterion satisfied?Obtain optimal design End YNFig. 2 Flowchart of combining ANN/EI optimizationFig. 3 Mid-plane model of a cellular phone coverTable 1 Ranges of the process parametersParameterTmold(C)Tmelt(C)tin(s)Ppack(%)tpack(s)tc(s)Lower limit502600.26015Upper limit903000.890515956Int J Adv Manuf Technol (2010) 48:955962A BPN is trained by repeatedly presenting a series ofinput/output pattern sets to the network. The neural networkgradually “l(fā)earns” the input/output relationship of interestby adjusting the weights between its neurons to minimizethe error between the actual and predicted output patterns ofthe training set. After training, a separate set of data whichis not in the training set is used to monitor the networksperformance. When the mean squared error (MSE) reachesa minimum, network training is considered complete andthe weights are fixed.In this paper, a three-layer ANN model with one hiddenlayer was used. The mold temperature (Tmold), melttemperature (Tmelt), injection time (tin), packing pressure(Ppack), packing time (tpack), and cooling time (tc) areregarded as input variables, and warpage is regarded asoutput variable. So the neuron numbers of the input layerand output layer of ANN are determined. The neuronnumber of the middle layer was determined by trials. Thetransfer function between the input layer and the hiddenlayer is “Logsig,” and the transfer function between thehidden layer and the output layer is “Purelin.” The trainfunction is trainlm, performance function is MSE, learningcycle is 50,000, learning rate is 0.05, and momentum factoris 0.9. The configuration of ANN used in this paper isshown in Fig. 1.3 EI methodANNs can be used as an arbitrary function approximationmechanism which “l(fā)earns” from observed data. ANN is hereused to build an approximate function relationship betweenthe warpage and the process parameters, replacing theexpensive analysis and reanalysis of simulation programs inthe optimizationprocess.Ingeneral,the approximate functionmay have many extremum points, making the optimizationalgorithms employing such functions converge to a localminimum. EI algorithm is here introduced to close to theglobal optimization solution.EI involves computing the possible improvement at agiven point. It is a heuristic algorithm for a sequential designstrategy for detecting the global minimum of a deterministicfunction 17, 21. It can balance local and global search.Beforesamplingatsomepointx, the value of Y(x) is uncertain.Y(x) at a candidate point x is normally distributed with b yx,and variance 2given using the ANN predictor. If the currentbest function value is Ymin, then an improvement I Ymin? yx by the ANN predictor can be achieved. Thelikelihood of this improvement is given by the normal density:1ffiffiffiffiffi2pps x exp ?Ymin? I ?b y x 22s2x #:1Then, the expected value of the improvement is found byintegrating over this density:EI ZI1I01ffiffiffiffiffi2pps x exp ?Ymin? I ?b y x 22s2x #()dI:2Fig. 5 Warpage of the cover after optimizationFig. 4 Warpage of the cover before optimizationTable 2 Optimization resultsParameterTmold(C)Tmelt(C)tin(s)Ppack(%)tpack(s)tc(s)Warpage(mm)Beforeoptimization75.57 288.31 0.57 63.96 1.22 5.700.1941Afteroptimization73.86 298.99 0.20 60.00 1.00 9.480.0833Fig. 6 Model of a scannerInt J Adv Manuf Technol (2010) 48:955962957Using integration by parts, Eq. 2 can be written as:EI sx u6u fu?3where and f are the normal cumulative distributionfunction and density function, respectively, andu Ymin?b y x s x :4The first term of Eq. 3 is the difference between thecurrent minimum response value Yminand the predictedvalueb yx at x, penalized by the probability of improvement.Hence, the first term is large when b yx is small. The secondterm is a product of predicted error (x) and normal densityfunction f(u). The normal density function value is largewhen the error (x) is large and b yx is close to Ymin. Thus,the expected improvement will tend to be large at a pointwith the predicted value smaller than Yminand/or with muchpredicted uncertainty.This infilling sampling method has some advantages: (1)It can intelligently add sample points to improve the ANN,so it allows “l(fā)earns” from observed data with a small size;(2) it can avoid searching the areas with relative largefunction values and reduce the computational cost; (3) itcan also avoid adding some points close to each other in thedesign space and keep the stability of ANN prediction.4 Warpage optimization based on improved ANNmethod4.1 Warpage optimization problemA warpage minimum design problem can be described asfollows:Findx1;x2;?;xmmaxmizeE I x1;x2;?;xm?Subjecttox?j? xj? xjj 1;2;?;m5where the process parameters x1;x2;?;xmare the designvariables and x?jand xjare the lower and upper limits of thejth design variable. The objective function E I x1;x2;? ? ?;xm?is given by Eqs. 3 and 4 inwhich Yminand yx are the currentminimum value and the predicted value of warpage, respectively.4.2 Convergence criterionThe convergence criterion is here to satisfy:E Ix?Ymin? $r6where r is a given convergence tolerance and Yminis theminimum function value in samples. The left-hand side is aratio between the maximum expected improvement and theminimum function value. Thus, r can be given withoutconsideration of the magnitudes, and r=0.1%.4.3 Implementation of optimization procedureImplementation of integrated ANN model and EI functionmethod is given in Fig. 2.5 Warpage optimization for a cellular phone coverand a scanner5.1 The optimization problemIn this section, the results of two warpage optimizationexamples are presented. These are intended to show theTable 3 Ranges of the process parametersParameterTmold(C)Tmelt(C)tin(s)Ppack(%)tpack(s)tc(s)Lower limit802600.26015Upper limit1203000.890515Table 4 Optimization resultsParameterTmold(C)Tmelt(C)tin(s)Ppack(%)tpack(s)tc(s)Warpage (mm)Before optimization92.95298.380.2585.492.8310.300.4805After optimization119.32300.000.2090.004.9215.000.2896Fig. 7 Warpage of the scanner before optimization958Int J Adv Manuf Technol (2010) 48:955962efficiency and accuracy of the integrated ANN model andEI function method.The first example is a cellular phone cover. It isdiscretized by 3,780 triangle elements, as shown in Fig. 3.Its length, width, height, and thickness are 130, 55, 11, and1 mm, respectively. The material is polycarbonate (PC)/acrylonitrile-butadiene-styrene.The mold temperature (Tmold), melt temperature (Tmelt),injection time (tin), packing pressure (Ppack), packing time(tpack), and cooling time (tc) are considered as designvariables. The objective function warpage(x) is quantifiedby the out-of-plane displacement, which is the sum of bothmaximum upward and downward deformations withreference to the default plane in Moldflow Plastics Insightsoftware. The constraints consist of the lower and upperbounds on the design variables given in Table 1. ANNmodel is here used to approximate warpage(x), i.e., b y x inEq. 2.The ranges of mold temperature and melt temperatureare based on the recommended values in Moldflow PlasticsInsight, and the ranges of injection time, packing pressure,packing time, and cooling time are determined by theexperience of the manufacturer.First, ten samples are selected by LHD, then the warpagevalue corresponding to every sample design is obtained byrunning Moldflow Plastics Insight software, and finally, anapproximate function relationship between warpage and theprocess parameters is constructed by means of ANN modelsimulation, replacing the expensive simulation analysis inthe optimization iterations.The optimization problem based on EI function is solvedhere using the sequential quadratic programming 28. Theexpected improvement surface may be highly multimodalFig. 8 Warpage of the scanner after optimization00.010.020.030.040.050.060.070.080.095060708090Mold temperature (oC)Warpage (mm)00.050.10.150.2260270280290300Melt temperature (oC)00.050.10.150.20.250.30.20.30.40.50.60.70.8Injection time(s)Warpage (mm)00.020.040.060.080.10.120.1460708090Packing pressure (MPa)Warpage (mm)Warpage (mm)00.020.040.060.080.10.120.141 23 45Packing time (s)Warpage (mm)0.0760.0780.080.0820.0840.0860.0880.090.0920.094579111315Cooling time (s)Warpage (mm)Fig. 9 Each factors individualeffect on the warpage of acellular phone coverInt J Adv Manuf Technol (2010) 48:955962959and thus difficult to optimize reliably. Firstly, 1,000 randompoints are selected, and EI function values computation areperformed by means of the constructed approximatemathematical function. The point with maximum EIfunction value is then selected to be one initial design. Inaddition, the point with minimum warpage value in samplepoints is selected to be another initial design, i.e., twooptimization processes are executed at each iteration. Incomparison with simulation analysis, these processesconsume very short time and can be ignored.Only 20 iterations were needed to obtain the optimizationsolution; the results are given in Table 3. Figures 4 and 5show the warpage values before and after optimization,respectively (Table 2).The second example is a scanner. The cover is discretizedby 8,046 triangle elements, as shown in Fig. 6. It is made ofPC. The mold temperature (Tmold), melt temperature (Tmelt),injection time (tin), packing pressure (Ppack), packing time(tpack), and cooling time (tc) are considered as designvariables. The objective function warpage(x) is quantifiedby the out-of-plane displacement, which is the sum of bothmaximum and minimum deformations with reference tothe default plane in Moldflow Plastics Insight software. Theconstraints consist of the lower and upper bounds on thedesign variables given in Table 3.The ranges of mold temperature and melt temperatureare based on the recommended values in Moldflow PlasticsInsight, and the ranges of injection time, packing pressure,packing time, and cooling time are determined by theexperience of the manufacturer.Initial ten samples are selected by LHD; the optimalsolution was obtained after 25 iterations. The results aregiven in Table 4. Figures 7 and 8 show the warpage beforeand after optimization, respectively.6 DiscussionsTables 2 and 4 show that several process parameters arelying in the boundaries of the limits. Figures 9 and 10 showeach factors effect on the warpage when all other factorsare kept at their optimal level, respectively.Figures 9 and 10 show that high melt temperature andshort injection time are desirable. The warpage valuedecreases nonlinearly as melt temperature changesfrom260C to 300C. This is because lower melt temper-ature has bad liquidity and can lead to early formation offrozen skin layer, which can generate higher shear stressand block flow. If there is no enough time to release theshear stress, the warpage will increase. However, the00.050.10.150.20.250.30.350.48090100110120Mold temperature (oC)Warpage (mm)00.10.20.30.40.50.6260270280290300Melt temperature (oC)Warpage (mm)00.050.10.150.20.250.30.350.40.450.50.20.30.40.50.60.70.8Injection time (s)Warpage (mm)00.10.20.30.40.50.60.70.80.9160708090Packing pressure (MPa)Warpage (mm)00.10.20.30.40.50.60.70.80.91 23 4 5Packing time(s)Warpage (mm)0.270.280.290.300.310.320.335 79111315Cooling time (s)Warpage (mm)Fig. 10 Each factors individualeffect on the warpage of ascanner960Int J Adv Manuf Technol (2010) 48:955962warpage value increases nonlinearly with the injection time.For the thin-wall injection molded parts, long injection timecan increase the ratio of the frozen skin layer to the moltencore layer. This can block badly the flow and lead to highershear stress and more molecular orientation in the material.The warpage value changes only a period of packing timeand almost is constant when packing time is longer thansome values. Figures 9 and 10 also show that the variationof warpage values is irregular when changing other processparameters such as packing pressure, cooling time, andmold temperature. The warpage value depends on thecombined efforts of all process parameters, and all theseprocess parameters should be provided by means ofoptimization.7 ConclusionsIn this study, an integrated ANN model and EI functionmethod is proposed to minimize the warpage of theinjection molding parts. This method aims at optimizingsome approximate functions trained by the ANN model.The optimization process can be started from anapproximate function trained by a set of a few samplepoints, then adding the best sample point into thetraining set by means of EI function. Every iteration ofthe optimization consists of training the approximatefunction and optimizing the EI function. The EI functioncan take the relatively unexpected space into consider-ation to improve the accuracy of the ANN model andquickly approach to the global optimization solution. Asthe applications, a cellular phone cover and a scanner,are investigated, only a small number of MoldflowPlastics Insight analysis are needed in optimizationsbecause the first iterations for both examples need a setof a few sample points (only ten sample points) andfollow-up of every iteration adds one sample point intothe set only. Numerical results show that the proposedoptimization method is efficient for reducing warpage ofinjection molded parts and can converge to the optimi-zation solution quickly. Although the design variables ofthese relatively examples are limited to the moldtemperature, melt temperature, injection time, packingpressure, packing time, and cooling time, the presentmethod is also applicable to more process parameters.However,