TH6340B卧式加工中心鼠牙盘式分度工作台机械设计
TH6340B卧式加工中心鼠牙盘式分度工作台机械设计,TH6340B卧式加工中心鼠牙盘式分度工作台机械设计,th6340b,卧式,加工,中心,鼠牙盘式,分度,工作台,机械设计
毕业设计(论文)任务书设计(论文)题目:TH6340B卧式加工中心鼠牙盘式分度工作台机械设计 学 院 名 称: 机械工程学院 专 业: 机械设计制造及其自动化 学 生 姓 名: 刘凯 学号: 11403010113 指 导 教 师: 郑书华 2014年 12月 7日1设计(论文)拟解决的主要问题设计TH6340B卧式加工中心鼠牙盘式分度工作台,是一种鼠牙盘定位、液压定量分度的分度工作台,B轴是该分度工作台的第四轴,是单向定量旋转轴,要求工作台有松开、锁紧机构。设计TH6340B卧式加工中心鼠牙盘式分度工作台的机械结构图、非标零件图并核算。2设计(论文)的主要内容和基本要求(1)主要技术参数工作台回转直径:400 mm *400mm,工作台重量800KGB轴重复定位精度:2B轴定位精度:8B轴的切削进给速度:010r/min主轴转速:606000r/min(高低挡无级变速),主轴锥孔MT6;径纵向进给最大速度:4m/min,横向进给最大速度:4m/min。主电机功率11/15(30min)Kw。定量角度分度;分析TH6340B卧式加工中心鼠牙盘式分度工作台的工作原理,设计机械结构装配图、零件图并核算。(2)设计要求分析TH6340B卧式加工中心鼠牙盘式分度工作台结构,查阅相关文献资料,书写文献综述(2000字以上);翻译外文文献2篇(2000字以上), 翻译提供文章的摘要、前言和相关核心段落;撰写开题报告1篇(2000字以上);绘制鼠牙盘式分度工作台的机械结构装配图一张(A0);绘制非标零件图若张;图纸工作量3张A0,设计说明书 (10000字以上)。图样质量:计算机绘图,符合最新标准;表达完整,布置合理清晰、尺寸标注齐全、技术要求全面;零件图同时要注意结构要素和加工工艺性。(3)查阅文献关键词 TH6340B卧式加工中心 定位销式分度工作台 锁紧机构 松开机构3推荐参考文献1 陈俊龙,陈俊华。MCV_50A立式加工中心使用探索J. 宁波高等专科科学学报, 1999.2 廉元国,张永洪. 加工中心设计与应用M.北京:机械工业出版社,1995 3 机床设计手册编写组.机床设计手册(第三册)M. 北京:机械工业出版社,19864 陈蔚芳,机床数控技术及应用J.科学出版社, 20055 劳动和社会保障部教材办公室组织编写. 数控机床机械系统M. 中国劳动和社会保障出版社,2004.66 夏田, 数控加工中心设计M .北京:化学出版社,20067 杨有君,数控技术M .北京:机械工业出版社20068 龚仲华,数控技术M .北京:机械工业出版社20049 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):221232 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): 637660 Boldea,Sayed A Nasar. Linear electric actuators and generators. Cambridge University Press, 1997.4进度安排2014年11月18日 接受任务2014年11月21日14月5日 熟悉内容,完成文献综述和英文翻译;2014年12月5日14年12月12日 完成开题报告,毕业实习开始 方案确定,计算和草图绘制;2014年12月12日15年1月12日 方案确定,草图绘制;进给机构计算、装配图绘制;电气设计图绘制;完成所有零、部件结构设计及设计说明书;修改图样和整理设计计算书,上交毕业设计资料。2015年1月12日15年1月16日 论文评阅2015年1月16日15年1月19日 答辩指导教师(签字) 郑书华 2014年11月18日教研室主任审核意见: 教研室主任(签字) 郭建亮 2014 年 11 月 18 日毕业设计(论文)开题报告设计(论文)题目:TH6340B卧式加工中心鼠牙盘式分度工作台机械设计 学 院 名 称: 机械工程学院 专 业: 机械设计制造及其自动化 学 生 姓 名: 刘凯 学号: 11403010113 指 导 教 师: 郑书华 2014年 12 月 7 日一、 研究的基本内容与拟解决的主要问题(或研究的主要内容及预期目标):研究主要内容:分析TH6340B卧式加工中心鼠牙盘式分度工作台的工作原理,设计机械结构装配图、零件图并核算。设计TH6340B卧式加工中心鼠牙盘式分度工作台,即一种鼠牙盘定位、液压定量分度的分度工作台,B轴是该分度工作台的第四轴,是单向定量旋转轴,要求工作台有松开、锁紧机构。具体需要设计TH6340B卧式加工中心鼠牙盘式分度工作台的机械结构图、非标零件图并核算。设计流程1电机的选择2传动比的分配及传动效率的计算 3同步带的选择及同步带轮的设计计算4圆锥齿轮的设计计算 5圆柱齿轮的设计计算 6鼠牙盘的设计计算 7液压系统的设计计算 8主要轴承的选择 9润滑与密封10其他元件的选择 主要技术参数工作台回转直径:400 mm *400mm,工作台重量800KGB轴重复定位精度:2B轴定位精度:8B轴的切削进给速度:010r/min主轴转速:606000r/min(高低挡无级变速),主轴锥孔MT6;径纵向进给最大速度:4m/min,横向进给最大速度:4m/min。主电机功率11/15(30min)Kw。 定量角度分度预期目标:课题的设计对于我们综合运用所学的理论知识和技能方面有很大的帮助,并要求我们必须具备扎实的机械设计基础,具有全方面的机械专业知识,熟悉组合机床等相关部件的设计原理,并且在设计过程中遇到的问题要及时反馈和查阅相关资料,对于那些计算的设计过程,更是要保证计算数据的准确,同时也要考虑设计的情况能否符合实际生产加工中的要求。通过本次设计我希望可以培养自己的设计计算、工程绘图、实验研究、数据处理、查阅文献、外文资料的阅读与翻译、计算机应用、文字表达等基本工作实践能力,使自己初步掌握科学研究的基本方法和思路。加强对卧式加工中心的了解,熟悉加工中心的基本原理和操作,通过研究核心部件工作台,能熟练使用加工中心,为以后的学习和工作奠定一定的基础。二、 研究的方法与技术路线(或研究步骤、方法和研究措施): 研究的基本方法:分析TH6340B卧式加工中心鼠牙盘式分度工作台题目的目地和意义,查阅相关文献资料搜集相关信息,结合国内外相关现状,最后规划出自己的设计方向和思路。 研究的步骤:1查找相关资料做好设计准备工作;2老师确定课题下发任务书;3下载相关文献和外文资料借鉴整理;4近距离观察加工中心工作台,了解其工作原理;5与老师进行学习交流;6归纳总汇。 研究措施:在设计前期,要充分的了解分度工作台的发展现状及发展趋势,掌握其动作原理、整体构造及工作特点,为整个机构是设计捋清思路。在设计的过程中,要经常与同组同学、指导教师一起商讨自己的设计方案及计算,做到发现问题及时修改。在设计的后期,要多方位的分析设计方案、计算方法,力争将本次设计做到最佳。最后对本次设计进行全面的总结。 要求:分析TH6340B卧式加工中心鼠牙盘式分度工作台的工作原理,设计机械结构装配图、零件图并核算。计算机绘图,符合最新标准;表达完整,布置合理清晰、尺寸标注齐全、技术要求全面;零件图同时要注意结构要素和加工工艺性。三、 研究的总体安排与进度:2014年11月18日 接受任务2014年11月21日14月5日 熟悉内容,完成文献综述和英文翻译;2014年12月5日14年12月12日 完成开题报告,毕业实习开始 方案确定,计算和草图绘制;2014年12月12日15年1月12日 方案确定,草图绘制;进给机构计算、装配图绘制;电气设计图绘制;完成所有零、部件结构设计及设计说明书;修改图样和整理设计计算书,上交毕业设计资料。2015年1月12日15年1月16日 论文评阅2015年1月16日15年1月19日 答辩四、 论文提纲:文章前言文章主体结构第一章 绪论第1节加工中心的功能及其特点 第2节加工中心的结构组成第3节加工中心的发展趋势 第4节本文研究目的和主要研究内容第二章 TH6363B加工中心分度工作台设计方案的拟订 第1节 加工中心常用回转工作台简介 第2节本课题设计方案的拟订 第3节本课题设计的分度工作台原理简介 第4节设计方案具体操作技术路线简介第三章 回转工作台设计第1节回转工作台的主要设计内容1.1电机的选择1.2传动比的分配及传动效率的计算 1.3同步带的选择及同步带轮的设计计算1.4圆锥齿轮的设计计算 1.5圆柱齿轮的设计计算 1.6鼠牙盘的设计计算 1.7液压系统的设计计算 1.8主要轴承的选择 1.9润滑与密封1.10其他元件的选择第2节绘制回转工作台总图和零件图文章结束语谢辞参考文献五、主要参考文献: 1 陈俊龙,陈俊华。MCV_50A立式加工中心使用探索J. 宁波高等专科科学学报, 19992 廉元国,张永洪. 加工中心设计与应用M.北京:机械工业出版社,1995 3 机床设计手册编写组.机床设计手册(第三册)M. 北京:机械工业出版社,19864 陈蔚芳,机床数控技术及应用J.科学出版社, 20055 劳动和社会保障部教材办公室组织编写. 数控机床机械系统M. 中国劳动和社会保障出版社,2004.66 夏田, 数控加工中心设计M .北京:化学出版社,20067 杨有君,数控技术M .北京:机械工业出版社20068 龚仲华,数控技术M .北京:机械工业出版社20049 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):221232 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): 637660 Boldea,Sayed A Nasar. Linear electric actuators and generators. Cambridge University Press, 1997指导教师审核意见: 指导教师签字 年 月 日教研室审核意见:教研室主任签字 年 月 日毕业设计(论文)文献综述设计(论文)题目:TH6340B卧式加工中心鼠牙盘式分度工作台机械设计 学 院 名 称: 机械工程学院 专 业: 机械设计制造及其自动化 学 生 姓 名: 刘凯 学号: 11403010113 指 导 教 师: 郑书华 2014年 12月 10日一、前言部分随着科学技术的进步与发展,数控机床及加工中心的应用已日趋普及,现代数控加工技术使得制造过程发生了巨大的变化,对技术人员的要求也越来越高。因此,我们要充分了解和熟悉现代数控机床的基本知识,使企业尽可能投入少,见效快,让资源合理化运用、让投资更加合适已成为众多企业所不可忽视的一项重要任务。同时数控技术是实现机械制造自动化的关键,直接影响到一个国家的经济发展和综合国力,关系到一个国家的战略地位。作为制造系统最基本的加工单元,以数控技术为核心的数控机床的生产和应用已成为衡量一个国家工业化程度和技术水平的重要标志。世界各国制造业广泛采用数控技术,以提高制造能力和水平,提高对市场的适应能力和竞争力。我国是制造大国,无论是从战略的角度还是从发展策略上,都需要加强数控产业的发展。而本次介绍的则是数控机床代表加工中心的核心部件工作台。二、主题部分1数控机床发展历史数控技术的应用给传统制造行业带来了革命性的变化,使制造行业成文工业化的象征。从1952年美国麻省理工学院研制出第一台试验性数控系统,数控系统经历了分立式晶体管、小规模集成电路、大规模集成电路、小型计算机、超大规模集成电路、微机式数控系统的发展阶段。我国数控技术起步于20世纪50年代末期,经历了初期的封闭式开发阶段,“六五”、“七五”期间的消化吸收、引进技术阶段,“八五”期间建立国产化体系阶段,“九五”期间产业化阶段,现已基本掌握了现代数控技术,建立了数控开发、生产基地,培养了一批数控专业人才,初步形成了自己的数控产业。2数控机床发展方向加工中心带有自动换刀装置的卧式数控镗铣床统称卧式加工中心。智能数控系统的开发提高了传统的数控铣床的加工效率和加工质量的能力。卧式加工中心是从数控镗铣床基础上发展起来的一种自动化加工设备,他可通过自动换刀,实现一次装夹、多工序加工,机床的功能更强、适用范围更广。基于功能部件的加工中心设计, 是以模块化设计思想为基础的产品设计,它的制造是以产业链为纽带的社会化生产。加工中心是典型的集高新技术于一体的机械加工设备, 它的发展代表了一个国家设计和制造业的水平。加工中心所能完成的工序是多种多样的,例如铣、镬、钻、铰、扩、惚、攻丝等。机床的这种多工序性和高度自动化的特点是由组成机床的各个部份来共同保证的。近两年来, 加工中心技术又有长远的发展, 主要仍集中在高速化高精度化及智能化三个方面。3当前研究主题卧式加工中心鼠牙盘式分度工作台分度转位工作台广泛应用于机械加工、组合机床、产品装配,可以实现工件一次装夹后完成多个工作面的多工序同时加工。分度转位角度根据零件的结椅需要而定。但零件一旦处于新的加工位置后必须对回转工作台定位,避免加工时工件的位置发生变化。分度工作台不同于圆周进给回转工作台,主要完成分廑运动分度运动过程中不加工。一般分度工作台不能实现无级丹庄运动,而是按照工位数目确定分度转位角度,分度传动机构实现回转工作台转位,分度精度由定位机构的精度保证。目前用于加工中心分度工作台上的定位机构有销定位和鼠牙盘定位两种。随着许多行业对高精度、快节拍的加工母机的大量需求,高速高精卧式加工中心得到飞速发展。从而对高精度转台的要求也越来越高。鼠牙盘式回转工作台以其定位精度高、加工过程性能稳定的优势得到广泛应用。鼠牙盘定位的分度工作台结构是一种较成熟的优越性较广泛的结构形式。这种工作台的结构包括:分度定位机构、转位机构、抬起和夹紧以及位置检测机构四部份。鼠牙盘(端面齿轮)具有高的分度精度,能传递大扭矩,所以广泛用于数控机床、加工中心机床、转塔车床和键床等分度机构和圆分度工作台上.鼠牙盘的啮合相当于一对带梯形齿的端齿离合器.啮合时由于整个齿面都能达到均匀的接触,分度精度非常高.加工中心机床上采用鼠牙盘定位的分度工作台的定位精度一般都在士5左右,最高可达1.5.而美国莫尔144。齿精密分度盘已达土0.1这是销定位等机构难于实现的。随着许多行业对高精度、快节拍的加工母机的大量需求,高速高精卧式加工中心得到飞速发展,从而对高精度转台的要求也越来越高,鼠牙盘式回转工作台以其定位精度高、加工过程性能稳定的优势得到广泛应用。三、总结部分 鼠牙盘式分度工作台作为加工中心的重要组成部分,它对加工的精度和效率都起到了非常大作用,在未来的发展趋势中必将会是主流,我希望通过本次设计培养自己综合应用所学专业的基础理论、基本技能和专业知识的能力,加强这方面的学习研究。四、参考文献1张杨林.我国数控技术的进展及发展趋势J.轻工机械,2006年3月第1期2Zhao chang-ming,Liu wang-ju.CNC Machining Process and equipmentJ.China 20023Tao Cheng, Jie Zhang et al.Intelligent Machine Tools in a Distributed Network Manufacturing Mode EnvironmentJ.Springer-Verlag London Limited Int J Adv Manuf Technol (2001) 17:2212324龚敏,陈友东.数控技术及开放式数控系统J.机械设计与制造2006年2月第2期5张杨林.我国数控技术的进展及发展趋势J.轻工机械2006年3月第1期6龚仲华.现代数控机床设计典例M.机械工业出版社,2014.7卢行忠.加工中心分度工作台J.现代机械1984,9期.8卢行忠.加工中心分度工作台定位和转位机构的设计及选型分析J.现代机械1987,2期.9廉元国.加工中心的新近技术动向J制造技术与机床1990年01期 10王庆利,孙岗存,吴丽等.精密卧式加工中心回转工作台的设计J.机械工程师,2012年第2期.11卢行忠.加工中心分度工作台结构设计选型分析J.现代机械1985,4期12范超毅.基于功能部件的现代加工中心设计与研究J机床与液压 2007年8期 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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