外文原文-金属板成型时易变原因的验证.PDF

1、The International Journal of Flexible Manufacturing Systems,15,518,2003c?2003 Kluwer Academic Publishers.Manufactured in The Netherlands.Identifying Sources of Variation in SheetMetal StampingKARL D.MAJESKEkdmumich.eduThe University of Michigan Business School,701 Tappan Street,Ann Arbor,MI 48109-12

2、34,USAPATRICK C.HAMMETTphammettumich.eduThe University of Michigan,Transportation Research Institute,2901 Baxter Road,Ann Arbor,MI 48109-2150,USAAbstract.Manufacturers using traditional process control charts to monitor their sheet metal stamping pro-cesses often encounter out-of-control signals ind

3、icating that the process mean has changed.Unfortunately,a sheetmetal stamping process does not have the necessary adjustability in its process variable input settings to alloweasily correcting the mean response in an out-of-control condition.Hence the signals often go ignored.Accord-ingly,manufactur

4、ers are unaware of how much these changes in the mean inflate the variance in the processoutput.We suggest using a designed experiment to quantify the variation in stamped panels attributable to changingmeans.Specifically,wesuggestclassifyingstampingvariationintothreecomponents:part-to-part,batch-to

5、-batch,and within batch variation.The part-to-part variation represents the short run variability about a given stable ortrending batch mean.The batch-to-batch variation represents the variability of the individual batch mean betweendiesetups.Thewithinbatchvariationrepresentsanymovementoftheprocessm

6、eanduringagivenbatchrun.Usinga two-factor nested analysis of variance model,a manufacturer may estimate the three components of variation.Afterpartitioningthevariation,themanufacturermayidentifyappropriatecountermeasuresinavariationreductionplan.In addition,identifying the part-to-part or short run

7、variation allows the manufacturer to predict the potentialprocess capability and the inherent variation of the process given a stable mean.We demonstrate the methodologyusing a case study of an automotive body side panel.Key Words:analysis of variance,designed experiment,dynamic batch mean,sheet met

8、al stamping,variationreduction1.IntroductionMost passenger vehicles produced today(automobiles,light trucks,and minivans)havea(structural)body that comprises 100150 stamped metal panels.These panels range insize from small,easy-to-form mounting brackets to large,complex panels such as fenders,hoods,

9、and body sides.The quality characteristics that describe stamped panels are thedimensions of features such as the length of trim edges or the position of a flange usedto assemble multiple panels.The typical approach used to measure a panel feature is todetermine its deviation from the nominal design

10、 specification along a specified plane,forexample,fore/aft from front of car,or in/out from the center of car(Roan and Hu,1995).The first author was supported by NSF grant DDM-9712997 to the University of Michigan.6MAJESKE AND HAMMETTThisresearchprovidesananalysismethodologytoquantifythecomponentsof

11、variationforthese panel quality features,given the particular characteristics of the sheet metal stampingprocess.For each automotive body panel,the sheet metal stamping process requires two distincttypes of equipment:the stamping press and a set of stamping dies.The set of stamping diesrepresents cu

12、stom manufacturing equipment used to make specific product geometry.Thestamping press represents flexible manufacturing equipment,capable of producing manydifferentautomotivebodypanels(hood,door,fender,etc.)simplybychangingthestampingdies.Thus,a particular stamping press produces an individual panel

13、 in batches,making thesetup of the dies critical to controlling the process mean.To monitor the quality of automotive body panels,most manufacturers apply statisticalanalysis methods(Montgomery,1996)such as statistical process control(SPC).In SPCterminology,manufacturing processes contain two types

14、of variation:common cause andspecialcause.Commoncausevariationisthenaturalinherentvariationintheprocessoutputwhen all input variables remain stable,that is,independent and identically distributed.Special cause variation represents any increase in product variability above the level ofcommon cause va

15、riation.Manufacturers detect special case variation by identifying out-of-control signals on control charts.To correct these out-of-control conditions,and eliminatethe associated special cause variation,the manufacturer must have the ability to adjust theprocess mean.Many North American(NA)automotiv

16、e stamping facilities lack the attention to de-tail in their die setup procedures to control mean shifts after changing the stamping dies.When beginning a panel batch,the manufacturer measures a sample of panels to createan SPC subgroup.If this subgroup plots out-of-control,the stamping processes have nosimple adjustment mechanisms to change feature dimensions.This inability to adjust theprocess mean has frustrated NA automotive manufacturers applying SPC to their stampingprocesses.Thus,these ma