关键词不能为空

当前您在: 主页 > 英语 >

什么是CP和CPK

作者:高考题库网
来源:https://www.bjmy2z.cn/gaokao
2021-02-22 18:44
tags:

-

2021年2月22日发(作者:安全感英文)


什么是


CP



CPK


CP(



Cpk)

< br>是英文


Process Capability index

缩写,汉语译作工序能力指数,也有译作工艺能力指数


,


过程能力指数。





工序能力指数,是指工序在一定时间里,处于控制状态


(


稳定状态


)


下的实 际加工能力。它是工序固有的能力,或者说它是工序


保证质量的能力。

< br>




这里所指的工序,是指 操作者、机器、原材料、工艺方法和生产环境等五个基本质量因素综合作用的过程,也就是产品质量的

< p>
生产过程。产品质量就是工序中的各个质量因素所起作用的综合表现。





对于任何生产过程,产品质量总是 分散地存在着。若工序能力越高,则产品质量特性值的分散就会越小;若工序能力越低,则


产品质量特性值的分散就会越大。那么,应当用一个什么样的量,来描述生产过程所造成的总分散呢

< p>
?


通常,都用


6


σ


(



μ


< p>
3


σ


)


来表示工序能力: 工序能力=


6


σ





若用符号


P


来表示工序能力,则:


P



6


σ





式中:


σ


是 处于稳定状态下的工序的标准偏差





工序能力是表示生产过程客观存在着分散的一个参数。但是这 个参数能否满足产品的技术要求,仅从它本身还难以看出。因此,


还需要另一个参数来反 映工序能力满足产品技术要求


(


公差、规格等质量标准


)


的程度。这个参数就叫做工序能力指数。它是技术要

< br>求和工序能力的比值,即



工序能力指数


=


技术要求/工序能力





当分布中心与公差中心重合时,工序能力指数记为

< p>
Cp


。当分布中心与公差中心有偏离时,工序能力指数记为


Cpk


。运用工序能


力指数,可以帮助我们掌握生产过 程的质量水平。



工序能力指数的判断





工序的质量水平按


Cp


值可划分为五个等级。按其等级的高低,在管理上可以作出相应的判断和处置


(


见表


1)


。该表中的分级、


判断和处置对于


Cpk


也同样适用。

< p>



1


工序能力指数的 分级判断和处置参考表



Cp




Cp>1.67


1.67



Cp



1.33


1.33



Cp>1.0


1.0



Cp>0.67


0.67>Cp


级别



特级



一级



二级



判断



能力过高



能力充分



能力尚可



双侧公差范


(T)


T



10


σ


< br>T



8


σ



10


σ



T



6


σ


—< /p>


8


σ







(1)


可将公差缩小到约土


4


σ


的范围


(2)


允许较大 的外来波动,以提高效



(3)


改用精度差些的设备,以降低成本


(4)


简略检验



若加工件不是关键零件,允许一定程度的外来波动



(2)


简化检验


(3)


用控制图进行控制



(1)


用控制图控制,防止外来波动


(2)


对产品抽样检验,注意抽样方式和


间隔

< br>


(3)Cp



1.0


时,应检查设备等方面的显示器



(1)< /p>


分析极差


R


过大的原因,并采取措施


(2)


若不影响产品最终质量和装


配工作, 可考虑放大公差范围


(3)


对产品全数检查,或进行分级筛选


(1)


必须追查各方面原因,对工艺进行改革


(2)


对产品进行全数检查



三级



四级



能力不足



能力严重不足



T


4


σ



6


σ



T


<< /p>


4


σ



Ppk, Cpk, Cmk


三者的区别及计算



1


、首先我们先说明


Pp



Cp


两者的定义及公式



Cp



Capability Indies of Process



:稳定过程的能力指数 ,定义为容差宽度除以过程能力,不考虑过程有无偏移,一般表达式为:



Pp



Performance Indies of Process



:过程性能指数,定义 为不考虑过程有无偏移时,容差范围除以过程性能,一般表达式为:




(该指数仅用来与


Cp



Cpk


对比,或


/< /p>



Cp



Cpk


一起去度量和确认一段时间内改进的优先次序)



CPU


:稳定过程的上限能力指数,定义为容差范围上限除以实际过程分布宽 度上限,



CPL


:稳定过程的下限 能力指数,定义为容差范围下限除以实际过程分布宽度下限,



2


、现在我们来阐述


Cpk



Ppk


的含义



Cpk


:这是考虑到过程中心的能力(修正)指数,定义为


CPU



CPL


的最小 值。它等于过程均值与最近的规范界限之间的差除以过


程总分布宽度的一半。

< p>


Ppk


:这是考虑到过程中心的性能(修正)指数,定义为:





的最小值。




其实,公式中的


K


是定义分布中心


μ


与公差中心


M


的偏离度,


μ



M


的偏离为

< p>
ε


=| M-


μ


|




3


、公式中标准差的不同含义



①在


Cp



Cpk< /p>


中,计算的是稳定过程的能力,稳定过程中过程变差仅由普通原因引起,公式中的标准差可 以通过控制图中的样本平


均极差



估计得出:



因此,

< br>Cp



Cpk


一般与控制图一起 使用,首先利用控制图判断过程是否受控,如果过程不受控,要采取措施改善


过程,使过 程处


于受控状态。确保过程受控后,再计算


Cp



Cpk




②由于普通和特殊两种原因所造成的变差,可以用样本标准差


S


来估计,过程性能指数的计算使用该标准差。



4


、几个指数的比较与说明





无偏离的


Cp


表示过程加工的均匀性(稳定性)


,即“质量能力”



Cp


越大,这质量特性的分布越“苗条”< /p>


,质量能力越强;而有


偏离的


Cpk


表示过程中心


μ


与公差中心


M


的偏离情况,


Cpk


越大, 二者的偏离越小,也即过程中心对公差中心越“瞄准”


。使过程


的“质量能力”与“管理能力”二者综合的结果。


Cp



Cpk


的着重点不同,需要同时加以考虑。




Pp



P pk


的关系参照上面。





关于


Cp k



Ppk


的关系,

< br>这里引用


QS9000



PPA P


手册中的一句话:


“当可能得到历史的数据或有足够的初始数 据来绘制控制图



(至少


100


个个体样本)



可以在过程稳定时计算


Cpk



对于输出满足规格要求且呈可预测图 形的长期不稳定过程,


应该使用


Ppk






< /p>


另外,我曾经看到一位网友的帖子,在这里也一起提供给大家(没有征得原作者本人同意, 在这里向原作者表示歉意和感谢)



上面是这样写的:



“所谓


PPK


,是进 入大批量生产前,对小批生产的能力评价,一般要求≥


1.67


;而


CPK


,是进入大批量生产后,为保证批量生产下


的产品的品质状况不至于下降,且为保证与小批生产具有同样的控制能力,所进行的生产能力的评 价,一般要求≥


1.33


;一般来说,


CPK


需要借助


PPK


的控制界限来作 控制。?





One is in QS9000. Ppk in QS9000 means Preliminary Process Capability Index. It should be calculated before Mass Production


and based on limited product quantity. Normally, it should be more than 1.67 because it's a short term process capability which


doesn't consider the long term variation. But, in QS9000 3rd edition, there's no Compulsory Requirement that the Ppk must be


more than 1.67. In QS9000 3rd edition, it states like Ppk/Cpk >=1.33.


Another one is in 6-Sigma. Ppk in 6-Sigma means Process Performance Index. It's a long term process capability covered the


long term process variation and based on more product quantity. Generally, in 6-Sigma, the Ppk value is less than Cpk value.


Ppk



Overall performance capability of a process, see Cpk.


过程的整体表现能力。



Cp



A widely used capability index for process capability studies. It may range in value from zero to infinity with a larger value


indicating a more capable process. Six Sigma represents Cp of 2.0.

在流程能力分析方面被广泛应用的能力指数,在数值方面它可


能是从零到显示更强有 力流程的无穷大之间的某个点。六个西格玛代表的是


Cp=2.0




Cpk



A process capability index combining Cp and k (difference between the process mean and the specification mean) to


determine whether the process will produce units within tolerance. Cpk is always less than or equal to Cp.


一个将


Cp



k


(表示流

程平均值与上下限区间平均值之间的差异)结合起来的流程能力指数,它用来确定流程是否将在容忍度范围内 生产产品,


Cpk


通常


要么比


Cp


值小,要么与


Cp


值相同。





如何正确计算设备的


Cpk


非常重要。在选择不同供应商设备 产品时,


Cpk


为用户用于比较设备性能的参数,


Cpk


还是生产线设置、


设备查错、成品率管理使用 的统计学工具。



Sort your Sigmas out!


The theory behind the all- important Sigma or Cpk rating for machines on the factory floor can be confusing. A Statistical Process


Control (SPC) tool can calculate the answer, but what if the machine consistently falls short of its manufacturer's claims? Even


some machine vendors cannot necessarily agree on when a machine has reached the Holy Grail of 6-Sigma repeatability. Most


uncertainties center on how to interpret the data and how to apply appropriate upper and lower limits of variability. The key lies with


the standard deviation of the process, which, fortunately, everyone can agree on.


Greater Accuracy, Maximum Repeatability



Industrial processes have always demanded the utmost repeatability, to maximize yield within accepted quality limits. Take


electronic surface mount assembly: as fine-pitch packages including 0201 passives and CSPs enter mainstream production,


assembly processes must deliver that repeatability with significantly higher accuracy. As manufacturing success becomes more


delicately poised, this issue will become relevant to a growing audience, including product designers, machine purchasers, quality


managers, and process engineers focused on continuous improvement.


This article will explain and demystify the secrets locked up in the charmingly simple - yet obstinately inscrutable - expression


buried some where down a machine's specification sheet. You may have seen it written like this:


Repeatability = 6-Sigma @ ±


25 m


This shows that the machine has an extremely high probability (6-sigma) that, each time it repeats, it will be within 25 m of the


nominal, ideal position.


A great deal of analysis, including the work of the Motorola Six Sigma quality program, among others, has led to 6-Sigma becoming


accepted throughout manufacturing businesses as the Gold Standard as far as repeatability is concerned. A machine or process


capable of achieving 6-Sigma is surely beyond reproach. Not true: many do not understand how to correctly calculate the value for


sigma based on the machine's performance. The selection of limits for the maximum acceptable variance from nominal is also


critical. In practice, virtually any machine or process can achieve 6-Sigma if those limits are set wide enough.


This is an important subject to grasp. Understanding it will help you make meaningful comparisons between the claims of various


equipment manufacturers when evaluating capital purchases, for example. You will also be able to set up lines and individual


machines quickly and confidently, troubleshoot and address yield issues, and ensure continuous improvement in the emerging chip


scale assembly era. And you will have a clearer view of the capabilities of a machine or process in action on the shop floor, and


apply extra knowledge when analyzing the data you are collecting through a SPC tool such as QC-CALC, in order to regularly


reassess equipment and process performance.


The aim of this article, therefore, is to provide a basic understanding of the subject, and empower all types of readers to make better


decisions at almost every level of the enterprise.


Grasp it Graphically



Instead of diving into a statistical treatise, let's take a graphical view of the proposition.


All processes vary to one degree or another. A buyer needs to ask


can I be sure?


to a standards organization.


Consider the possibilities of accuracy versus repeatability. Suppose we measure the X & Y offset error 10 times and plot the ten


points on a target chart as seen in figure 1. Case 1 in this diagram shows a highly repeatable machine since all measurements are


tightly clustered and


or the Greek symbol σ), is small.



However, a small standard deviation does not guarantee an accurate machine. Case 2 shows a very repeatable machine that is not


very accurate. This case is usually correctable by adjusting the machine at installation. It is the combination of Accuracy and


Repeatability we strive to perfect.


A simple way of determining both accuracy and precision is to repeatedly measure the same thing many times. With screen printing


machines the critical measurement is X & Y fiducial alignment. Theoretically, the X & Y offset measurements should be identical but


practically we know the machine cannot move to the exact location every time due to the inherent variation. The larger the variation


the larger the standard deviation.





After making many repeated measurements, the laws of nature take over. Plotting all your readings graphically will result in what is


known as the normal distribution curve (the bell curve of figure 2 also called Gaussian). The normal distribution shows how the


standard deviation relates to the machine's accuracy and repeatability. A consistent inaccuracy will displace the curve to the left or


right of the nominal value, while a perfectly accurate machine will result in a curve centered on the nominal. Repeatability, on the


other hand, is related to the gradient of the curve either side of the peak value; a steep, narrow curve implies high repeatability. If


the machine were found to be repeatable but inaccurate, this would result in a narrow curve displaced to the left or right of the


nominal. As a priority, machine users need to be sure of adequate repeatability. If this can be established, the cause of a consistent


inaccuracy can be identified and remedied. The remainder of this section will describe how to gain an accurate understanding of


repeatability by analyzing the normal distribution.


A number of laws apply to a normal distribution, including the following:


1. 68.26% of the measurements taken will lie within one standard deviation (or sigma) either side of average or mean


2. 99.73% of the measurements taken will lie within three standard deviations either side of average


3. 99.9999998% of the measurements taken will lie within six standard deviations either side of average



Consider the bell curve shown in figure 2. The process it depicts has three standard deviations between nominal and 25 m.


Therefore, we can describe the process as follows:


Repeatability = 3-sigma at ±


25 m


There are two important facts to understand right away:



+25 m: this is not a 6-sigma process. The laws governing the normal distribution say it is 3-sigma.



25 m limits. It continues to 6-sigma, described


by note 3 above, and even beyond. Simply by drawing extra sigma zones onto the graph, we can illustrate that the 3-sigma process


at ±


25 m achieves 6-sigma repeatability at ±


50 m. It is the same process, with the same standard deviation, or variability.


Now consider what happens if we analyze a more repeatable process. Clearly, as the bulk of the measurements are clustered more


closely around the target, the standard deviation becomes smaller, and the bell curve will become narrower.


For example, let's discuss a situation where the machine has a repeatability of 4-sigma at ±


25 microns, and is centered at a


nominal of 0.000 as shown in figure 3. This bell curve shows an additional sigma zone between nominal and the 25 m limit. Quite


clearly, a higher percentage of the measurements lie within the specified upper and lower limits. The narrowing of the bell curve


relative to the specification limits highlights what is referred to as the


produce the narrowest spread within the stated limits of the equipment, increasing the probability that the equipment will operate


within those limits.



Lastly, we draw our bell curve with 6 sigma zones to show what it means to state that a machine has ±


25 micron accuracy and is


repeatable to 6-sigma. You can see how the 6-sigma machine has a very much smaller standard deviation compared to the


3-sigma machine. In fact, the standard deviation is halved. This means the 6-sigma machine has less variation and therefore is


more repeatable. Consider the very narrow bell curve of figure 4 in relation to the laws governing the normal distribution, which


state 99.9999998% of measurements will lie within 6 standard deviations of nominal.



At this point, we can summarize a number of important points regarding the repeatability of a process:




deliver 6-sigma repeatability



deviation of the process


Relationship to ppm



We can also now see why 6-sigma is so much better than 3-sigma in terms of the capability of a process. At 3-sigma, 99.73% of the


measurements are within limits. Therefore, 0.27% lie outside; but this equates to 2700 parts per million (ppm). This is not very good


in a modern industrial process such as screen printing, or any other SMT assembly activity for that matter. 6-sigma, on the other


hand, implies only 0.0000002% or 0.002 ppm (2 parts per billion) outside limits. Readers familiar with the Motorola Six Sigma


quality program will have expected to see 3.4 ppm failures. This is because the methodology allows for a 1.5 sigma


in mean not included in the classical statistical approach, which this article is following.


Whichever approach is taken all machine vendors, and also contractors such as EMS businesses, understandably wish to be able


to say they have 6-sigma capability. For this reason, buyers of machines and manufacturing services need to be very careful when


evaluating the vendor's claims.


For instance, if a machine vendor claims 6-sigma at ±


12.5 m, you must ask for the standard deviation of the machine. Then divide


12.5 m by the figure provided to find the repeatability, in sigma, of the machine: if the result is 6, the repeatability is 6-sigma and you


can rely on the vendor's claim for process capability. Depending on the intent of the vendor, you may find a different answer. For


example, the machine may be only half the stated accuracy. This is because there is room for confusion over whether limits of


±


12.5 m would allow repeatability to be calculated by dividing the total spread, i.e 25 m, by the standard deviation. This is not


consistent with the laws governing the normal distribution, but it does provide scope to claim 6-sigma performance for a process


that is, in fact, only 3-sigma. Be careful.


When purchasing a new piece of equipment be sure the manufacturer provides some proof. You should request a report showing


how the machine performed at the rated specification.


Most SMT equipment has built-in video cameras to align itself and in some cases, inspect the product it produces. Screen printers


use the cameras to align the incoming board and stencil, Even though the board / stencil alignment is relative alignment to one


another, an independent verification tool can be mounted in the screen printer to produce an unbiased measurement verifying the


machine's stated accuracy and repeatability.


The SPC tools used, for example, by an equipment manufacturer, to characterize their machines' ability to support particular


processes,


will calculate the standard deviation, σ, from measurements taken directly from the machine. For example, a number of


vendors use Prolink's QC-CALC SPC tool to verify the performance of each new machine, prior to delivery, against their own


published performance specifications for the relevant model. Any manufacturer that follows a similar characterization procedure


should be able to provide a value for the standard deviation of a particular machine when performing a specific process.


Relationship to Cp and Cpk



The term Cp or Cpk describes the capability of a process. Cp is related to the standard deviation of the process by the following


expression:



where USL is Upper Specification Limit and LSL is Lower Specification Limit


But where the process capability is expressed in these terms, the majority of machine data sheets quote a figure for Cpk. Cpk


includes a factor that takes process inaccuracy into account, as follows:




where is the center point of the process.


You can see how Cpk varies with any offset in the bell curve caused by process inaccuracies. In the ideal situation, when = 0, the


process is perfectly centered and Cpk is equivalent to Cp.


Assuming the machine is set up by the manufacturer to be accurate, we can accept that = 0 such that Cp = Cpk. In this case, we


can see from the formula for Cp that 6-Sigma corresponds to Cpk 2.0, 4-Sigma corresponds to Cpk 1.33, and 3-Sigma corresponds


to Cpk 1.0. Note again, however, that the critical factors affecting Cpk are the limits and the standard deviation of the process.


It is also worth pointing out at this stage that Cp and Cpk refer to the capability of the entire process the machine is expected to


perform. Consider the screen printing example again. Repeatedly measuring the board-to-fiducial alignment alone will yield a set of


data from which we could assess the capability of the machine, expressed as Cm or Cmk. But several further operations, beyond


initial alignment of the board and stencil, are required before a printed board is available for analysis. To extract a true figure for Cp


or Cpk, then, we must be sure that we are not merely measuring the machine's capability to perform a subset of the target process.


The following section discusses this argument.


Process capability, or alignment capability?



After the alignment stage, several further elements of the machine's design, its build, or its setup will influence the repeatability of


the print process. For example, the lead screw for the table-raise mechanism could be warped or may have been cut inaccurately;


on an older machine it could be worn or damaged, especially if the service history is not known. Other variables include the stencil


retention or board clamping mechanisms; these may not be fully secure. Other machine components, such as the chassis, may


lack rigidity. The act of moving a print head across the stencil, exerting a vertical force of some 5 kg while traveling at a typical


excursion speed of 25 m/s, will almost certainly make the print performance less repeatable if the machine has weaknesses in


these areas. Figure 5 illustrates the conundrum. To assess whether a machine will produce the print results required in a particular


target process, the buyer needs to know that the capability figures refer to the machine's overall ability to output boards that are


printed accurately to within the quoted limits.




Figure 5. Alignment capability versus full process capability




Home and Dry…



OK, so you have quizzed your machine supplier about its standard deviation, and the stated limits of repeatability. You have made


sure the quoted performance figures relate to overall process capability, not to one aspect of its activities, such as alignment. You


have verified the manufacturer's claims using your newfound familiarity with statistical analysis; and your new machine is now up


and running on your line. But it is not producing the repeatability you expected when running your target process. What do you do?


Depending on the type of machine, any number of factors could work alone or interdependently to cause a gradual or more abrupt


deterioration in repeatability. In a screen printer, selection and setup of tooling, for example, is very important. Inadequate


underscreen cleaning may be causing blocked apertures over a longer time period. Or a change in solder paste supplier could


introduce a step change in the results you are experiencing.


Some of these issues can be identified and resolved quite easily. Others may demand a more scientific approach to arrive at a


satisfactory solution. Using a data collection and SPC package can help machine owners analyze their machines' performance


historically or in real- time, in the same way that the machine vendor may use such a tool to accurately characterize the machine


before delivery. A tool such as QC-CALC has comprehensive reporting features, including graphical tools showing process


capability, ranges, pareto charts, correlation, and probability plots to help process engineers locate just where errors are occurring.


You can also perform trend analysis and have one or more actions, such as a point outside sigma limits, trigger automatically to


help you isolate the causes of poor performance.


Remember there is a difference between machine parameters and process parameters. The OEM gives you the machine


parameters to work within and you set- up the machine with your process parameters. Stay within this limit and you will produce


good product. This is similar to buying a car that has a guaranteed top speed of 125 mph but you can't make the car go beyond 70


mph. Upon further investigation the service department determined you never shifted the car out of 1st gear! Don't


machine!


Summary



Reading this article should have provided a number of points to consider when evaluating and operating industrial equipment:


1. Be aware that many people, including machine manufacturers, may be confused about how to calculate the capability of a


process or machine.


2. Test the performance figures published by the machine vendor, by asking for the machine's standard deviation. Divide the


standard deviation into the upper or lower limit quoted by the manufacturer to find the machine's capability, in sigma.


3. Find out if the figure quoted applies to the entire process or only a certain part of it, such as dry fiducial alignment.


4. Depending on the answer to 3, above, this may change your opinion of the machine's capabilities.


5. Be aware that your selection of other components, such as tooling, machine settings and process parameters also influence the


repeatability you will see on the factory floor.


6. Wear or damage to the machine may also impair repeatability.


7. Monitoring via a statistical process control tool allows an assessment of repeatability, can help identify trends, and can aid


troubleshooting and continuous process optimization.



[


原创


]Cp,Cpk,Pp,Ppk,Z



MINITAB


中的计算公式




有的时候有人会问在


M INITAB


中的


Cp,Cpk,Pp,Ppk,Z


怎么计算出来的?怎么和我们自己手工计算的有差别的呢?看看这些计算公式吧。



Cp,Cpk,Pp,Ppk,Z



MINITAB


中的计算公式:



CCpk = min { (USL - uST)/3sST , (mST - LSL)/ 3sST}


Cp = (USL - LSL) / (6sST)


Cpk = min { (USL - uLT) /3sST


, (uLT - LSL)/3sST}


CPL = (uST - LSL) / (3sST)


CPU = (USL - uST) / (3sST)


Pp = (USL - LSL) / (6sLT)


Ppk = min {(USL - uLT)/3sLT


, (uLT - LSL)/3sLT}


PPL = (uLT - LSL) / (3sLT)


PPU = (USL - uLT) / (3sLT)


注 解:


u=[


平均值,读


miu]



ST=Short Term, LT=Lonterm



平均值计算公式:



uLT =Sum(X11+X12+...Xnk)/Sum(n1+n2+nk), n


为组 数,


k


为每组的样本容量。



注解:也就是整个样本的平均值。



uST =(USL+LSL)/2


注解:也就是公差中心。




标准差计算公式:



sLT = Cum SD(LT)K


sST = Cum SD(ST)K


(LT)j = F(P


.Total(LT)j)


(ST)j = F(P


.Total(ST)j)


(LT)j = (mLT - LSL) / Cum SD(LT)j


(ST)j = (mST - LSL) / Cum SD(LT)j


(LT)j = (USL - mLT) / Cum SD(LT)j


(ST)j = (USL - mST) / Cum SD(LT)j


= (ST)j - (LT)j



CCpk


CCpk is a measure of potential capability. It is identical to the Cpk index except that, instead of being centered at the process mean


all the time, it is centered at the target when given or the midpoint of the specification limits when the specification limits are given.


CCpk is precisely Cpk when one of the specification limits and the target is not given


Cpm


Cpm is an overall capability index defined as the ratio of the specification spread (USL - LSL) to the square root of the mean squared


deviation from the target.



给大家澄清一下看法!



1. Cpk --


短期过程能力指数;


Ppk --


过程性能指数,即长期过程能力指数。



2. Cpk=min{CpL,CpU}=(T/2-|M-


μ


|)/3


σ


ST


Ppk=min{PpL,PpU}=(T/2-|M-


μ


|)/3


σ


LT

;其中,


σ


ST


为短期过程标准差 ,


σ


LT



长 期过程标准差。



3. Cpk


的测定要求过程稳定,而


Ppk


不要求过程稳定。





1


点:概念不能说不对,很多书上是这样说的,但 会误导观众;




2

< br>点:公式基本正确,


σ


ST


和< /p>


σ


LT


提法不大好,请参阅


MINITAB-help-Method and Formulas Process Capability


(可搜索)




CP=(USL-


LSL)/6σ


within


PP=(USL-


LSL)/6σ overall




3


点:非常正确。此为


CPK



PPK


差异所在:以


Between/Within/Overall


的角度来理解


CPK



PPK


比长

< br>/


短期制程能力更有说服


力。



案例一:



1


、打开


MINITAB


数据表:


(共< /p>


75


个数据、子组数


=3



SPEC



50+/- 3




2


、用


Quality tools- Capability Analyze -Normal





请自主计算


CPK=


?、


PPK=





Quality tools-Capability Analyze -Between /WIthin





再请自主计算


CPK=

< p>
?、


PPK=




为什么有不一样?



您再进行


Xbar-R Control Chart


试试,有什么发现。



案例二:



Part 01:



600.744


599.106


599.207


Part 02:


599.054


600.1


599.432


599.242



从上述几个案例,您有什么发现?



、用


Quality tools-Capability Analyze -Normal




598.726


599.432


600.234


600.124


600.465


600.782


601.573


600.768


601.023


600.886


600.782


601.046


601.467


601.282


601.447


602.04


603.691


601.803


602.333


599.414


600.171


599.987


600.849


600.779


601.983


602.21


602.102


601.803


598.414








599.777


600.239


600.592


599.125


600.443


600.773


599.303


600.134


599.403


599.243


599.596


600.277


599.8


600.406


599.81


599.91


598.134


599.263


601.604







SPE C



600+/- 2


、子组数


=6



,请计算


CP K



PPK



-


-


-


-


-


-


-


-



本文更新与2021-02-22 18:44,由作者提供,不代表本网站立场,转载请注明出处:https://www.bjmy2z.cn/gaokao/669864.html

什么是CP和CPK的相关文章