減振器活塞及支承座上雙面活塞孔的加工組合機(jī)床畢業(yè)設(shè)計(jì)
減振器活塞及支承座上雙面活塞孔的加工組合機(jī)床畢業(yè)設(shè)計(jì),減振器,活塞,支承,雙面,加工,組合,機(jī)床,畢業(yè)設(shè)計(jì)
河南理工大學(xué)本科畢業(yè)設(shè)計(jì)（英文翻譯）
Taguchi 的申請(qǐng)和回應(yīng)表面方法學(xué) 為幾何學(xué)的錯(cuò)誤在表面磨的程序
摘要
幾何學(xué)的錯(cuò)誤在于表面的磨程序中主要地被熱的效果和磨的 系統(tǒng).為 將幾何學(xué)的錯(cuò)誤減到最少的堅(jiān)硬影響,磨的叁數(shù)選擇非常重要。 這張紙呈現(xiàn)了 Taguchi 的一個(gè)申請(qǐng) 而且為幾何學(xué)的錯(cuò)誤回應(yīng)表面方法學(xué)。 幾何學(xué)的錯(cuò)誤被評(píng)估的磨叁數(shù)對(duì)～的效果和將幾何學(xué)的錯(cuò)誤減到最少的最適宜磨的情況是堅(jiān)決的。 一個(gè)秒- 次序回應(yīng)模型為幾何學(xué)的錯(cuò)誤被發(fā)展，而且回應(yīng)表面模型的利用率， 表面的粗糙和那材料的限制被評(píng)估移動(dòng)率。 證實(shí)實(shí)驗(yàn)在一種最佳的情況被引導(dǎo)而且為觀察被發(fā)展的回應(yīng)準(zhǔn)確性表面模型選擇了二種情況。
一. 介紹
輪磨是一個(gè)復(fù)雜的機(jī)制程序由于許多交談式叁數(shù), 仰賴輪磨產(chǎn)品的類(lèi)型和需求。 表面的質(zhì)量在表面的輪磨中生產(chǎn)被影響被各種不同的依下列各項(xiàng)被給的叁數(shù) [1].
(i)旋轉(zhuǎn)叁數(shù): 研磨劑，谷粒大小，等級(jí) , 結(jié)構(gòu),縛者，形狀和尺寸等
(ii)細(xì)工品叁數(shù): 破碎模態(tài),機(jī)械的財(cái)產(chǎn) , 和化學(xué)藥品作文等
(iii)處理叁數(shù): 輪子速度, 深度減低,桌子加速, 和穿衣情況等
(iv)以機(jī)器制造叁數(shù): 靜電和動(dòng)態(tài)的特性,變細(xì)長(zhǎng)系統(tǒng) , 和桌子系統(tǒng)等
限制范圍的完全跟據(jù)經(jīng)驗(yàn)的情況有有效性照慣例被用在練習(xí)因?yàn)槟サ某绦虬ㄔS多無(wú)法控制的叁數(shù)。
的確如此不可靠的或可接受的在任何的特效藥情形中。 達(dá)成在一種特定的情形中的必需表面質(zhì)量,程序叁數(shù)能被決定經(jīng)過(guò)一系列的實(shí)驗(yàn)奔跑。 但是, 可能是一耗時(shí)的和貴的方法和它也不能夠決定正確因?yàn)橄拗频膶?shí)驗(yàn)最適宜。 Taguchi 和回應(yīng)表面方法學(xué)能方便的最佳化有一些實(shí)驗(yàn)的奔跑磨叁數(shù)好的設(shè)計(jì)。 許多研究有被引導(dǎo)為決定最佳的程序叁數(shù) 。Kim[6] 運(yùn)行了實(shí)驗(yàn)的分析為一圓筒形的研磨程序使用 Taguchi 方法獲得那變數(shù)和最大的百分比的比較效果表面的粗糙進(jìn)步。Dhavlikar[7] 呈現(xiàn)了Taguchi 和回應(yīng)方法為減到最小限度決定健康的情況由細(xì)工品的圓錯(cuò)誤為那無(wú)中心的磨的程序。 Hashmi[8] 有預(yù)知工具生活在那結(jié)束磨程序藉著回應(yīng)表面方法。 那
最適宜銳利的情況是堅(jiān)決的對(duì)一必需的在瞬間用工具工作生活-次序預(yù)言模型。 Suresh[9] 使用過(guò)的回應(yīng)表面方法和遺傳基因的運(yùn)算法則預(yù)知表面的粗糙而且將程序叁數(shù)最佳化。
這項(xiàng)研究一評(píng)估 Taguchi 方法的一個(gè)幾何學(xué)的錯(cuò)誤上的磨參數(shù)的影響而且發(fā)展回應(yīng)的一個(gè)數(shù)學(xué)的模型表面強(qiáng)迫預(yù)知幾何學(xué)的錯(cuò)誤方法。 回應(yīng)表面模型用實(shí)驗(yàn)被查證。
二. 文學(xué)檢討
2.1 在磨的程序中的幾何學(xué)的錯(cuò)誤
熱的高比在表面的輪磨期間生產(chǎn)程序轉(zhuǎn)移到一個(gè)細(xì)工品和能引起各種不同的類(lèi)型對(duì)～的熱傷害 ～最后的產(chǎn)品例如那燃燒現(xiàn)象，幾何學(xué)的錯(cuò)誤，剩余壓迫力,結(jié)構(gòu)變形 , 和其他人。 這些是對(duì)～的衡量那土地的表面質(zhì)量評(píng)估而且應(yīng)該是
在下面限制如某范圍。 最重要的熱的損害考慮是那幾何學(xué)的錯(cuò)誤, 與～有關(guān)最后產(chǎn)品錯(cuò)誤。幾何學(xué)的錯(cuò)誤只有藉由一個(gè)熱的方面被影響不在細(xì)工品和磨的輪子之間的連絡(luò)地域但是也藉著磨的系統(tǒng)一個(gè)堅(jiān)硬那因素一個(gè)垂直的換置一些程度反對(duì)磨的力量。
在熱的方面情況, 熱在輪磨期間程序能在細(xì)工品中產(chǎn)生熱的擴(kuò)充如中凸的形狀表面。 縮減的真正深度是不常數(shù)和比縮減的理想深度深是那簽姓名的首字母意圖。 然而，細(xì)工品能被冷卻和在輪磨之後有收縮。 如此，細(xì)工品表面是反的改變?nèi)绨嫉男螤睢?數(shù)量那熱的效果幾何學(xué)的錯(cuò)誤能有關(guān)數(shù)十到達(dá)依照應(yīng)用的磨情況的測(cè)微計(jì)。在磨的系統(tǒng)堅(jiān)硬的情況,那例如 磨的機(jī)器紗錠的成份,磨的輪子，細(xì)工品和磨的桌子,不是硬的而且沒(méi)有一個(gè)重的堅(jiān)硬。 磨的力量演戲在這些成份垂直地制造一個(gè)合量換置。在對(duì)熱的方面反對(duì)派方面, 縮減的真正深度在這個(gè)情形是比縮減的理想深度較多的燕子。如圖 1 所示, 合量描繪那幾何學(xué)的被熱的方面和堅(jiān)硬引起的錯(cuò)誤那磨的系統(tǒng)用～展現(xiàn)各種不同的表格復(fù)雜的依照應(yīng)用的磨情況的作文。如此，最適宜的磨情況應(yīng)該被選擇為將最后的產(chǎn)品幾何學(xué)的錯(cuò)誤減到最少。
2.2 實(shí)驗(yàn)的設(shè)計(jì)
2.2.1. Taguchi 設(shè)計(jì)
Taguchi 設(shè)計(jì)方法是一簡(jiǎn)單的和健康的將程序叁數(shù)最佳化的技術(shù)。在這個(gè)方法中, 被假定有程序結(jié)果上的影響力的主要部份叁數(shù)位於不同的排在被設(shè)計(jì)的直角排列中。 藉由如此的一個(gè)安排完全地隨機(jī)化實(shí)驗(yàn)?zāi)鼙灰龑?dǎo)。
大體上, 向謠傳 (S/N) 作信號(hào)比 (h,分貝) 表現(xiàn)質(zhì)量特性為被觀察的數(shù)據(jù)在 Taguchi 中實(shí)驗(yàn)的設(shè)計(jì)。 仰賴那實(shí)驗(yàn)的目的,有一些質(zhì)量特性。 在那幾何學(xué)的錯(cuò)誤和表面的粗糙情形,比較低的價(jià)值他們是令人想要的。 在 Taguchi 中的這些 S/ N 比方法被呼叫當(dāng)做那-比較低的- 比較好的特性和依下列各項(xiàng)被定義。
（1）
yi 在 ith 審判是被觀察的數(shù)據(jù)哪里，而且 n 是那審判的數(shù)字。從 S/N 比,有效的叁數(shù)有程序結(jié)果上的影響力能被看到和最佳的組合程序，叁數(shù)可能是堅(jiān)決的。
2.3 回應(yīng)表面方法學(xué)
時(shí)常工程實(shí)驗(yàn)者愿找那一個(gè)某程序達(dá)到的情況那最佳的結(jié)果。 那是, 他們想要決定水平那設(shè)計(jì)叁數(shù)在回應(yīng)延伸它的最適宜。 最適宜可以不是最大值就是一設(shè)計(jì)叁數(shù)的一個(gè)功能的最小量。 一獲得最適宜的方法學(xué)是回應(yīng)升至水面技術(shù)。
回應(yīng)表面方法學(xué)是一個(gè)收集統(tǒng)計(jì)的和是有用的數(shù)學(xué)方法對(duì)靠模切和分析工程問(wèn)題。 在這技術(shù),主要的目的要將回應(yīng)最佳化被各種不同的程序叁數(shù)影響的表面。回應(yīng)表面方法學(xué)也定量關(guān)系在可管理的輸入叁數(shù)之間和那獲得了回應(yīng)表面。
回應(yīng)的設(shè)計(jì)程序升至水面方法學(xué)依下列各項(xiàng)是 [10]:
(i)為興趣的回應(yīng)適當(dāng)?shù)暮涂煽康臏y(cè)量一系列的 實(shí)驗(yàn)的設(shè)計(jì)。
(ii)用～發(fā)展第二個(gè)次序回應(yīng)表面的一個(gè)數(shù)學(xué)的模型最好的配件。
(iii)發(fā)現(xiàn)生產(chǎn)最大值或最小量回應(yīng)的價(jià)值實(shí)驗(yàn)的叁數(shù)最佳的組合。
(iv)經(jīng)過(guò)二和三個(gè)空間的情節(jié)表現(xiàn)程序叁數(shù)的直接和交談式效果。
如果所有的變數(shù)被假定可測(cè)量,回應(yīng)表面能被表示成追從。
（2
目標(biāo)要將回應(yīng)變數(shù) y 最佳化。 它是假定獨(dú)立變數(shù)是連續(xù)的和
可管理的藉著有可以忽略的錯(cuò)誤實(shí)驗(yàn)。 它是需要找一個(gè)適當(dāng)?shù)慕浦禐槟钦鎸?shí)的
功能的關(guān)系在獨(dú)立變數(shù)之間和回應(yīng)表面。 通常一個(gè)秒- 次序模型是回應(yīng)表面中利用。
（3）
那里 3 是一個(gè)任意的錯(cuò)誤。 系數(shù), 應(yīng)該在秒- 次序模型中被決定, 被獲得最沒(méi)有正直的方法。 大體上情緒商數(shù)。 (3)能被以母體形式寫(xiě)
Y=bX+E (4)
在 Y 被定義是量過(guò)的價(jià)值一個(gè)母體的地方, X 到是獨(dú)立變數(shù)的一個(gè)母體。 母體 b 和 E由～所組成系數(shù)和錯(cuò)誤,分別地。 解決情緒商數(shù)。 (4)能被母體方式獲得
(5)
在哪里那母體 X 和 (X) K1 調(diào)換是那母體X 的相反。
三. 實(shí)驗(yàn)的細(xì)節(jié)
一系列的 實(shí)驗(yàn)有被引導(dǎo)評(píng)估哪些磨的叁數(shù)影響幾何學(xué)的錯(cuò)誤在磨的表面。 例如 谷粒的四個(gè)磨的叁數(shù)大小,輪子速度, 深度減低而且桌子速度被選擇因?yàn)閷?shí)驗(yàn)。 磨的輪子采用鋁在磨的輪子中的有 vitrified 束縛的氧化物研磨劑
用來(lái)磨擦高速度工具鋼。 (SKH51) 一化學(xué)藥品細(xì)工品的作文在表 1 中被列出。一個(gè)幾何學(xué)的描繪考試人 (Mahr,OMS-600) 習(xí)慣於測(cè)量幾何學(xué)的錯(cuò)誤。 幾何學(xué)的錯(cuò)誤價(jià)值是定義到一種高度不同在最大的點(diǎn)之間和土地的表面最小的點(diǎn)在總長(zhǎng)度里面一個(gè)量過(guò)的細(xì)工品。
表 2 列出了可管理的因素 (磨的叁數(shù))而且他們的水平在這項(xiàng)研究中考慮。 輪磨叁數(shù)是旋轉(zhuǎn)速度 (V) ，桌子速度 (S), 深度減少 (D) 和谷粒大小.(M) 每個(gè)因素有了三個(gè)水平(程序排列). 例如 研磨劑的類(lèi)型另一個(gè)因素,細(xì)工品，冷凍劑和火花在外是不變的。 那冷凍劑不被供應(yīng)和出自途徑火花是不實(shí)行 。 被選擇的 L27 直角的排列有27 排在表 3 中被顯示。 交互作用在因素不被考慮。 自由的程度為
實(shí)驗(yàn)是 26 。
四. 實(shí)驗(yàn)的結(jié)果和討論
量過(guò)的幾何學(xué)的錯(cuò)誤一個(gè)例子是在圖 2 中顯示。 依照縱觀的方向土地的細(xì)工品, 一般看到那重要的幾何學(xué)的錯(cuò)誤數(shù)量是量過(guò)的。 價(jià)值被觀察的數(shù)據(jù)為幾何學(xué)的錯(cuò)誤被列出在表 4
圖 3 呈現(xiàn)了四的有計(jì)畫(huà)的 S/ N 比在幾何學(xué)的錯(cuò)誤上的因素依照那每個(gè)水平。 在最小量之間的那比較高地不同而且在每個(gè)因素中的最大 S/ N 比是,愈比較高幾何學(xué)的錯(cuò)誤上的效果是。 如圖 3 所示,深度減低是一個(gè)占優(yōu)勢(shì)的叁數(shù)為那幾何學(xué)的錯(cuò)誤和下一個(gè)是谷粒大小。輪子速度和桌子速度有了比較低的效果在幾何學(xué)的錯(cuò)誤上。
以及因?yàn)槟?降低-比較好的特性,最高的 S/在每個(gè)因素中的 N 比是令人想要的獲得最小的幾何學(xué)的錯(cuò)誤。 在那深度的情形減低何時(shí)最低的深度減低當(dāng)做10 公厘的價(jià)值是應(yīng)用的, 幾何學(xué)的錯(cuò)誤可以被減到最少。 它是由於低度的深度減低了對(duì)熱的方面是有利潤(rùn)的實(shí)在。 低度的谷粒大小,( 反的平均谷粒的直徑)哪一在磨的程序期間減少了熱世代,可以減少幾何學(xué)的錯(cuò)誤。
和深度相反減低而且谷粒估計(jì),最大的 S/N 比, 是最小的幾何學(xué)的錯(cuò)誤價(jià)值,在中央的水平被獲得,這些結(jié)果是由於作文在熱的方面和堅(jiān)硬之間那磨的系統(tǒng)。 最小的幾何學(xué)的錯(cuò)誤將會(huì)是在低度的深度減低達(dá)成和谷粒大小聯(lián)合由于中央水平的輪子速度和桌子速度。 如此,最適宜的情況為幾何學(xué)的錯(cuò)誤能是建立在:
輪子速度:(V) 1800 轉(zhuǎn)/每分,
桌子速度:(S) 10.0 m/最小,
深度減低:(D) 10 公厘
谷粒大小:(M) 46 網(wǎng)孔。
表 5 呈現(xiàn)了變化的分析結(jié)果(ANOVA)為幾何學(xué)的錯(cuò)誤 S/ N 比。因素的 F- 比被計(jì)算而且與～相較F- 比的統(tǒng)計(jì)價(jià)值為特定水平的信心。 F- 比的統(tǒng)計(jì)價(jià)值是3.55而且 6.01 以 95 和 99%個(gè)信心消除,分別地[11]. 因?yàn)橛杏?jì)畫(huà)的價(jià)值那F- 比對(duì)如表 5 所顯示的所有因素沒(méi)有超過(guò) 95% 信心水平，全部的效果因素可以被認(rèn)為適當(dāng)?shù)摹?那有計(jì)畫(huà)的百分比分配, 可以展現(xiàn)如何很多的影響力是為幾何學(xué)的錯(cuò)誤,是
在圖 4 中顯示。
4.2.1 秒- 次序幾何學(xué)的錯(cuò)誤模型
秒- 次序回應(yīng)表面的表現(xiàn)那幾何學(xué)的錯(cuò)誤 (Y,公厘) 能被表示成一個(gè)功能，例如 輪子速度 (V) 的磨叁數(shù),桌子速度(S), 深度減低 (D) 和谷粒大小.(M) 關(guān)系在幾何學(xué)的錯(cuò)誤和磨的叁數(shù)之間是表示成追從。
從被觀察的數(shù)據(jù)為幾何學(xué)的錯(cuò)誤列出在表 4 和情緒商數(shù)。 (5),秒- 次序回應(yīng)功能是在下面決定。
ANOVA 的一個(gè)結(jié)果為回應(yīng)升至水面功能幾何學(xué)的錯(cuò)誤在表 6 中被顯示。 藉由比較由于那有計(jì)畫(huà)的和統(tǒng)計(jì)的 F-比,它被看到秒- 次序回應(yīng)功能相當(dāng)適當(dāng)。圖 5 表演 3D 反應(yīng)為幾何學(xué)的錯(cuò)誤升至水面在二的情形改變?nèi)?shù)。
4.2.2. 次序幾何學(xué)的錯(cuò)誤模型的利用從情緒商數(shù)。 (7), 幾何學(xué)的錯(cuò)誤依照各種不同的磨的情況可以被預(yù)知而且減到最少容易地。 但是選擇磨的叁數(shù)從那
第二個(gè)次序的幾何學(xué)的錯(cuò)誤模型,在練習(xí)中,應(yīng)該是藉由改良限制升至水面粗糙和材料移動(dòng)率。 平均的粗糙完全跟據(jù)經(jīng)驗(yàn)的價(jià)值,Ra(公厘),可以與～一起呈現(xiàn)下列各項(xiàng)公式[12].
如情緒商數(shù)所看到。 (8), 減低增加深度和那桌子速度產(chǎn)生平均的表面比較高的價(jià)值粗糙而且如此表面惡化。 但是增加那谷粒大小作一個(gè)好的表面。 物質(zhì)的移動(dòng)率在表面的輪磨中， Z(mm3/最小), 被呈現(xiàn)當(dāng)做那在如下式;
在 B 是磨的輪子寬度 (公厘) 的地方。 情緒商數(shù)。 (8)和(9) 能被利用評(píng)估幾何學(xué)的錯(cuò)誤在一特效藥表面粗糙和物質(zhì)的移動(dòng)率。
圖 6 表演回應(yīng)表面為幾何學(xué)的錯(cuò)誤有關(guān)於桌子速度和縮減的深度在一常數(shù)輪子 1800 轉(zhuǎn)/每分的速度和常數(shù)谷粒大小120 網(wǎng)孔。 圖 7 表現(xiàn)了表面的等高線為幾何學(xué)的錯(cuò)誤回應(yīng)。 表面的粗糙等高線0.35,0.40 和 0.45 公厘和物質(zhì)的2500,3200 和 4000 mm3/ 最小的等高線也被增加。使用過(guò)的 B 的價(jià)值是 22 公厘。 表面的粗糙可以與～一起改良減低減退所有的深度和桌子速度。 增加這些叁數(shù)引導(dǎo)到一壞的
表面以及一個(gè)重的幾何學(xué)的錯(cuò)誤。
但是不管 相同的表面粗糙在點(diǎn) A,B而且 C,幾何學(xué)的錯(cuò)誤和那材料的價(jià)值移動(dòng)率不同於彼此。 增加那有一個(gè)不變的表面粗糙的物質(zhì)移動(dòng)率增加幾何學(xué)的錯(cuò)誤。 如此,適當(dāng)范圍的輪磨叁數(shù)應(yīng)該被藉由犧牲一選擇是不重要的。
五. 證實(shí)實(shí)驗(yàn)
在秒- 次序回應(yīng)表面的證實(shí)中模型 , 確認(rèn)測(cè)試被引導(dǎo)在那最佳的被 Taguchi 方法決定的情況 (測(cè)試 1) 和二選擇了情況 ( 測(cè)試 2 和 3) 那不是在表 3 中實(shí)行 . 在測(cè)試 2 中和 3,那12 網(wǎng)孔的 15 公厘和谷粒大小的縮減相同的深度被用。 輪子速度和桌子速度是2100 轉(zhuǎn)/每分和 15 m/ 最小和 1500 轉(zhuǎn)/每分和 12.5 m/最小。圖 8 呈現(xiàn)了測(cè)試結(jié)果。 如圖 8 所示,在被預(yù)知的幾何學(xué)的錯(cuò)誤之間的不同被那秒- 次序回應(yīng)表面和尺寸結(jié)果被實(shí)驗(yàn)很小。 如此,秒-次序回應(yīng)模型對(duì)預(yù)知非常有用那幾何學(xué)的錯(cuò)誤。
六. 結(jié)論
在表面的磨程序中的幾何學(xué)的錯(cuò)誤是測(cè)量依照實(shí)驗(yàn)的直角排列。 被那實(shí)驗(yàn)的和分析的結(jié)果, 那獲得結(jié)論是依下列各項(xiàng)。
1. 磨的叁數(shù)對(duì)～的效果幾何學(xué)的錯(cuò)誤用 來(lái)自 Taguchi 方法的幫忙被評(píng)估。 那深度減低是一個(gè)占優(yōu)勢(shì)的叁數(shù)為幾何學(xué)的錯(cuò)誤和下一個(gè)是谷粒大小。 最佳的磨
以將減到最少為條件幾何學(xué)的錯(cuò)誤是決定
2. 一個(gè)秒- 次序回應(yīng)表面模型為那幾何學(xué)的錯(cuò)誤從被觀察的數(shù)據(jù)被發(fā)展?；貞?yīng)的利用升至水面模型是評(píng)估用～選擇適當(dāng)?shù)哪デ闆r表面的粗糙和那材料的限制移動(dòng)率。
3. 回應(yīng)表面模型的證實(shí)實(shí)驗(yàn)在一種最佳的情況被引導(dǎo)和二選擇情況。 一般顯示被發(fā)展的回應(yīng)表面模型對(duì)預(yù)知非常有用那幾何學(xué)的錯(cuò)誤。
承認(rèn)
這個(gè)工作部份地被腦韓國(guó) 21 支援在 2004 年的計(jì)畫(huà).
Application of Taguchi and response
surface methodologiesfor geometric error in surface
grinding process
Jae-Seob Kwak*
School of Mechanical Engineering, Pukyong National University, San 100, Yongdang-Dong, Nam-Ku, Busan 608-739, South Korea Received 24 February 2004; accepted 3 August 2004 Available online 16 September 2004
Abstract
The geometric error in the surface grinding process is mainly affected by the thermal effect and the stiffness of the grinding system. For minimizing the geometric error, the selection of grinding parameters is very important. This paper presented an application of Taguchi and response surface methodologies for the geometric error. The effect of grinding parameters on the geometric error was evaluated and optimum grinding conditions for minimizing the geometric error were determined. A second-order response model for the geometric error was developed and the utilization of the response surface model was evaluated with constraints of the surface roughness and the material
removal rate. Confirmation experiments were conducted at an optimal condition and selected two conditions for observing accuracy of the developed response surface model.
q 2004 Elsevier Ltd. All rights reserved.
Keywords: Geometric error; Grinding process; Taguchi method; Response surface; Optimal conditions。
1. Introduction
Grinding is a complex machining process with a lot of interactive parameters, which depend upon the grinding type and requirements of products. The surface quality produced in surface grinding is influenced by various parameters given as follows [1].
(i) Wheel parameters: abrasives, grain size, grade, structure,binder, shape and dimension, etc.
(ii) Workpiece parameters: fracture mode, mechanical properties, and chemical composition, etc.
(iii) Process parameters: wheel speed, depth of cut, table speed, and dressing condition, etc.
(iv) Machine parameters: static and dynamic characteristics,spindle system, and table system, etc.
The empirical conditions having restricted range of validity are conventionally used in practice because grinding process involves many uncontrollable parameters.
* Tel.: C82 51 620 1622; fax: C82 51 620 1531.
E-mail address: jskwak5@pknu.ac.kr.
0890-6955/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ijmachtools.2004.08.007
So the ground surface quality with these conditions is not reliable or acceptable in any specific situation. To achieve the required surface quality in a specific situation, process parameters can be determined through a series of experimental runs. But, that may be a time-consuming and expensive method and also it cannot determine the exact optimum because of restricted experiments. Taguchi and
response surface methodologies can conveniently optimize the grinding parameters with several experimental runs well designed. A lot of research has been conducted for determining optimal process parameters [2–5].
Kim [6] performed experimental analysis for a cylindrical lapping process using the Taguchi method to obtain the relative effects of variables and the largest percentage improvement of surface roughness.
Dhavlikar [7] presented the Taguchi and response method to determine the robust condition for minimization of out of roundness error of workpieces for the centerless grinding process. Hashmi [8] has predicted a tool life in the end milling process by response surface method. The optimum cutting conditions were determined for a required tool life by a second-order prediction model. Suresh [9] used
the response surface method and genetic algorithm forpredicting the surface roughness and optimizing process parameters.
This study forced on evaluating the grinding parameters’ effect on a geometric error by the Taguchi method and developing a mathematical model by the response surface method for predicting the geometric error. The response surface model was verified with experiments.
2. Literature review
2.1. Geometric error in grinding process
A high ratio of heats produced during surface grinding process transfers to a workpiece and can cause various types of thermal damage to the final product such as the burn phenomenon, geometric error, residual stress, structure transformation, and others. These are a measure of quality evaluation in the ground surface and should limited as a certain range below. The most important consideration of thermal damages is the geometric error, which is related to inaccuracy of the final product.The geometric error is affected not only by a thermal aspect of the contact zone between workpiece and grinding wheel but also by a stiffness of the grinding system that causes some degree of a vertical displacement against grinding force.
In the case of thermal aspect, the heats during grinding process can generate a thermal expansion in the workpiece surface as a convex shape. The real depth of cuts is not constant and deeper than the ideal depth of cuts that are initial intention. However, the workpiece can be cooled and have shrinkage after grinding. So, the workpiece surface inversely changed as a concave shape. The amount of geometric error by the thermal effect can reach about tens micrometers according to the applied grinding conditions.
In the case of stiffness of the grinding system, the components such as the spindle of the grinding machine, grinding wheel, workpiece and grinding table, are not rigid and do not have a heavy stiffness. Grinding forces acting on these components make vertically a resultant displacement.In opposition to the thermal aspect, the real depth of cuts in this case is more swallow than the ideal depth of cuts.As shown in Fig. 1, the resultant profile of the geometric error caused by the thermal aspect and the stiffness of the grinding system shows various forms with complicated compositions according to the applied grinding conditions. So, the optimum grinding condition should be selected forminimizing the geometric error of the final product.
2.2. Design of experiments
2.2.1. Taguchi design
The Taguchi design method is a simple and robust technique for optimizing the process parameters. In this method, main parameters which are assumed to haveinfluence on process results are located at different rows in a designed orthogonal array. With such an arrangement completely randomized experiments can be conducted.
In general, signal to noise (S/N) ratio (h, dB) represents quality characteristics for the observed data in the Taguchi design of experiments. Depending on the experimental objective, there are several quality characteristics. In the case of geometric error and surface roughness, lower values of them are desirable. These S/N ratios in the Taguchi method are called as the-lower-the better characteristics and
are defined as follows.
where yi is the observed data at the ith trial and n is the number of trials.
From the S/N ratio, the effective parameters having influence on process results can be seen and the optimal sets of process parameters can be determined.
2.3. Response surface methodology
Often engineering experimenters wish to find the conditions under which a certain process attains the optimal results. That is, they want to determine the levels of the design parameters at which the response reaches its optimum. The optimum could be either a maximum or a minimum of a function of the design parameters. One of methodologies for obtaining the optimum is response surface technique.
Response surface methodology is a collection of statistical and mathematical methods that are useful for the modeling and analyzing engineering problems. In this technique, the main objective is to optimize the response surface that is influenced by various process parameters. Response surface methodology also quantifies the relationshipbetween the controllable input parameters and the
obtained response surfaces.
The design procedure of response surface methodologyis as follows [10]:
(i) Designing of a series of experiments for adequate and reliable measurement of the response of interest.
(ii) Developing a mathematical model of the second order response surface with the best fittings.
(iii) Finding the optimal set of experimental parameters that produce a maximum or minimum value of response.
(iv) Representing the direct and interactive effects of process parameters through two and three dimensional plots.
If all variables are assumed to be measurable, the response surface can be expressed as follows.
The goal is to optimize the response variable y. It is assumed that the independent variables are continuous and controllable by experiments with negligible errors. It is required to find a suitable approximation for the true functional relationship between independent variables and the response surface. Usually a second-order model is utilized in response surface methodology.
where 3 is a random error. The b coefficients, which should be determined in the second-order model, are obtained by the least square method. In general Eq. (3) can be written in matrix form.
where Y is defined to be a matrix of measured values, X to be a matrix of independent variables. The matrixes b and E consist of coefficients and errors, respectively. The solution of Eq. (4) can be obtained by the matrix approach.
where XT is the transpose of the matrix X and (XTX)K1 is the inverse of the matrix XTX.
3. Experimental details
A series of experiments have been conducted to evaluate which grinding parameters affect the geometric error in surface grinding. Four grinding parameters such as grain size, wheel speed, depth of cut and table speed were selected for experimentation. Grinding wheel adopting aluminum oxide abrasives with vitrified bond in the grinding wheel was used to grind a high-speed tool steel (SKH51). A chemical composition of the workpiece is listed in Table 1.A geometric profile tester (Mahr, OMS-600) was used to measure the geometric error. Values of geometric error were defined to a height difference between a maximum point and a minimum point of the ground surface within a total length
of a measured workpiece.
Table 2 listed controllable factors (grinding parameters) and their levels considered in this study. The grinding parameters were wheel speed (V), table speed (S), depth of cut (D) and grain size (M). Each factor had three levels (process ranges). The other factors such as type of abrasive, workpiece, coolant, and spark out were constant. The coolant was not supplied and the spark out pass was not
carried out. The selected L27 orthogonal array having 27 rows is shown in Table 3. The interaction between factors was not considered. Degree of freedom for experiments is 26.
Table 2
Levels of independent factors
4. Experimental results and discussion
An example of the measured geometric error was shown in Fig. 2. According to the longitudinal direction of the ground workpiece, it was seen that the significant amount of geometric error was measured. The values of the observed data for geometric error were listed in Table 4.
4.1. Effect of grinding parameters
Fig. 3 presented the calculated S/N ratios of four factors on the geometric error according to the each level. The higher the difference between the minimum and the maximum S/N ratios in each factor is, the higher the effect on the geometric error is. As shown in Fig. 3, the depth of cut was a dominant parameter for the geometric error and the next was the grain size. The wheel speed and the table speed had lower effects on the geometric error.
And also because of the-lower-the better characteristics, the highest S/N ratio in the each factor was desirable to obtain the minimum geometric error. In the case of the depth of cut when the lowest depth of cut as a value of 10 mm was applied, the geometric error could be minimized. It was due to a low level of the depth of cut being profitable to the thermal aspect. A low level of the grain size (inversely average diameter of grain), which reduced heat generation during grinding process,could reduce the geometric error.
Contrary to the depth of cut and the grain size, the maximum S/N ratios, which were the values of
minimum geometric error, were obtained at the middle levels. These results were due to the composition between the thermal aspect and the stiffness of the grinding system. The minimum geometric error will be achieved at the low levels of the depth of cut and of the grain size combined with the middle levels of the wheel speed and the table speed. So, the optimum conditions for the geometric error can be established at:
? wheel speed (V): 1800 rpm,
? table speed (S): 10.0 m/min,
? depth of cut (D): 10 mm
? grain size (M): 46 mesh.
Table 5 presented the result of analysis of variation (ANOVA) for the S/N ratio of the geometric error.F-ratios of factors were calculated and compared with the statistical values of the F-ratio for a specific level of confidence. The statistical values of the F-ratio were 3.55 and 6.01 at 95 and 99% confidence levels, respectively [11]. Because the calculated values of the F-ratio for all factors as shown in Table 5 did not exceed the 95% confidence level, the effects of all factors could be considered adequate. The calculated percentage distributions, which could show how much influence was for the geometric error, were shown in Fig. 4.
4.2. Response surface analysis
4.2.1. Second-order geometric error model
The second-order response surface representing the geometric error (Y, mm) can be expressed as a function of grinding parameters such as wheel speed (V), table speed (S), depth of cut (D) and grain size (M). The relationship between the geometric error and grinding parameters
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