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Review of the Development in Process Capability Analysis

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Review of the Development in Process Capability Analysis International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-2013 2471 ISSN 2229-5518 Review of the Development in Process Capability Analysis Vidhika Tiwari1, N.K.Singh2 Abstract— This review paper is devoted to the study for the analysis of the characteristics of the product may be interrelated. It is process capability of the manufacturing processes. The process capability indices necessary to develop the process capability measure for the Cp; C pk C pm , C pmk , C py and C pc are presented, related to process parameters and the practical applications of the conventional as well as some new indices in the quality characteristics related to the above mentioned manufacturing industries are provided. distributions and sampling distribution of their estimate. Under non normal distribution, some properties of the PCIs Keywords— Quality control, Process capability index, Non-normal distribution, and their estimators differ from those of normal distribution. Gamma and Weibull Distribution,Industrial application . To utilize the PCIs more reasonably and accurately in —————————— —————————— assessing the lifetime performance of components, this study is conducted. 1. INTRODUCTION 2. PROCESS CAPABILITY INDICES FOR QUALITY Process capability compares the process output with the CHARACTERISTICS FOLLOWING VARIOUS customer’s specification. Two parts of process capability are: i) DISTRIBUTIONS Measure the variability of the output of a process, and ii) Compare that variability with requirement specification or 2.1. PCI For Normal Distribution product tolerance. Process capability analysis is the evaluation of a production Process capability analysis is based on some fundamental process to determine whether or not the inherent variability of assumptions that is, the process is stable and that the studied its output falls within the acceptable range and process characteristic is normally distributed. There are several capability index or process capability ratio is a statistical statistics that can be used to measure the capability of a measure of process capability. The concept of process process. According to the philosophy of the quality control capability holds meaning for processes that are in a state approach, process capability indices of any process can be of statistical control. Process capability indices measure how divided into capability indices of the first and second much "natural variation" a IJSERprocess experiences relative to its generation. The design of the first generation capability specification limits and allows different processes to be indexes (Cp, Cpk) is based on classical philosophy of the compared with respect to how well an organization controls statistical process control. According to that philosophy all them. measurement results within required tolerance interval are A process capability index uses both the process intended to be good. Measurements outside tolerance interval variability and the process specifications to determine whether are considered to be bad. Under these assumptions the two the process is capable. The process capability index (PCI) is a most widely used indices in industry are Cp and Cpk, where value which reflects real-time quality status. The PCI is Cp was presented by Juran (1974) and Cpk by Kane (1986). considered as one of the quality measurements tool. In practice, process capability indices (PCIs) are used as a means of measuring process potential and performance. Moreover, USL− LSL USL−−µµ LSL Cp = Cpk = min , σ σσ most PCIs have been developed or investigated under the 6 33 assumption that components have a lifetime with a normal distribution. In many processes, the quality characteristics may follow the non-normal distribution e.g. Weibull, Exponential, and Geometric distribution etc. In some cases, the ———————————————— • Mrs.Vidhika tiwari1 is currently pursuing PhD degree program in ME & MME Departmen in ISM Dhanbad, India,[email protected]. • Dr. N.K.Singh2 Associate professor in ME &MME Department in ISM Dhanbad.India,[email protected]. IJSER © 2013 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-2013 2472 ISSN 2229-5518 −−ξξ Ue−−11 Le = Cpk Min(,)σσ− ee33−−11 And estimated proportion of non-conforming items as φ{(logLU− ξσ ) /ˆˆ } +− 1 φ (log − ξσ ) / Where ξ = Scale parameter, σˆ = Estimated Standard deviation. A superstructure or family of capability indices, containing Cp, Cpk, Cpm and Cpmk involving two parameters, is introduced by Vannman (1993) as: du−−µ m Cp(,) u v = 3{σµ22+−vT ( ) } Fig.1 Normal distribution Where [LSL, USL] is the specification interval, μ is the process mean and σ is the process standard deviation of the in-control UL− UL+ process. Henceforth, we call the process mean μ and the Where d = and m = 2 2 process standard deviation σ for the process parameters. The capability index Cp relates the distance between the By letting u = 0 or 1 and v = 0 or 1 in the above equation, we specifications limits to the range over which the process is can obtain four basic indices i.e. Cp(0,0) = Cp, Cp(1,0) = Cpk, actually varying. Cp(0,1) = Cpm and Cp (1.1) = Cpmk. Chan and Mak (1993) propose a Process Capability indicator as: 2.2. PCI For Non Normal Distribution T − µ 6σ Second generation capability index (Cpm) is rising from new Cl = +w ,0 ≤≤w 1 UL− ()UL− approach to the quality improvement (Taguchi approach). It is () 2 not enough to know that measurements are so called good = DI + w (VI) (being within the tolerance interval) but important is T-m knowledge on how good they are. Such index enables to Where DI= Deviation Indicator = U-L determine whether the values of the searched quality index () IJSER 2 approach to the tolerance limits even when all measurement 61σ results fit within the tolerance. Since the indices Cp and C pk do VI = Variation Indicator = = ()U− L Cp not take into account of the differences between the processes mean and its target value, Chan (1988) and Pearn (1992) Most capability indexes assume that the quality characteristic is normally distributed. Clements (1989), Kotz and Johnson considered this difference to develop indices Cpm and Cpmk as follows: (1993), and Sommerville and Montgomery (1996) among others, comment in detail on the distortion in information provided by these indices when process distribution moves USL− LSL Cpm = 2 away from the assumed normal. Ahmad and saleh (1999) 2 6 σµ+−()T presented a generalization of Clements’ method with asymmetric tolerances. These quantile-based indices, Cp and Cpk for non normal data are defined as follow: USL −−µµLSL Cpm = min 22, , 22 − 33σµ+−()TT σµ +−() USL LSL USL−− X50 X 50 LSL Cp = Cpk = min, X99.865−− XX 50 50 X 0.135 XX99.865− 0.135 Where, T represents the target value of quality characteristics. The process parameters μ and σ2 are estimated from the Wang (1996), Zimmer (1998), Kan and Yasici (2006) and sample mean variance for X and S2, when μ and σ2 are Mousa & Jaheen (2002) have presented a comprehensive unknown. Rodriguez (1992) proposed a process capability review of Burr distribution and its application to many non- index (Cpk) for Lognormal distribution as normal situations.Ahmed and Rahbar (2001) developed an IJSER © 2013 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-2013 2473 ISSN 2229-5518 asymptotic theory for a capability index for arbitrary industry. Pyzdek (1992) mentioned that the distributions of populations. Nahar and Zimmer (2001) and Chang(2002) certain chemical processes, such as zinc plating in a hot-dip considered the process capability indices for skewed galvanizing process, are very often skewed. Choi et al. (1996) populations. Abbasi (2006) used simulated annealing presented an example of a skewed distribution in the ‘‘active algorithm to estimate three parameters of Weibull distribution. area’’ shaping stage of the wafer’s production processes. Process capability analysis when observations are Gamma distribution (skewed) covers a wide class of non- autocorrelated is investigated by Noorossana (2002) using normal applications, including the manufacturing of time series modeling and regression analysis. Luceno (1996) semiconductor products, head/gimbals assembly for memory suggested a process capability index which seems to be storage systems, jet-turbine engine components, flip-chips and insensitive to departure from the assumption of normal chip-on-board, audio-speaker drivers, wood products, and variability. A detailed discussion and references on dealing many others. with non-normality of the process distribution are given in The control charts are commonly used in many industries for Kotz and Johnson (2002). providing early warning for the shift in the process mean. For example, the cumulative sum chart is known to be effective on 2.3. PCI For Discrete Distribution detecting sustained shifts in the process mean (see e.g. Lucas and Crosier, 2000; Luceno and Puig-Pey, 2002; Lucas, 1976). If The most widely used such indices are Cp,Cpk,Cpm, and Cpmk the control chart detects a process mean shift, then the process or their generalizations for non-normal processes, suggested is not under control. However, for momentary process mean by Clements (1989), Pearn and Kotz (1994), and Pearn and shifts, it may be beyond the control chart detection power. Chen (1995). Often, however, one is faced with processes Consequently, the undetected shifts may result in described by a characteristic whose values are discrete.
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