SPC-Training-material(very-good).ppt

Click to edit Master title style,*,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,Basic SPC,SPC Training,Contents,SPC introduction,Control Charts,Variable Control Charts,Attribute Control Charts,Advanced Control Charts,Support Material,Learning Objectives,Myths and Misconceptions of SPC,Product Control Model,vs,Process Control Model,Two Basic Causes of Variability,General Form of SPC Charts,Designing Control Charts,Implementing SPC Charts,What is“SPC”?,You Think You Know.But Do You Really?,Some discussion questions to further check your,understanding of SPC:,Two important underlying principles,of X-R chart?,Single purpose,of SPC charts?,Process control,vs,process capability,?,Rational,subgrouping,concept,?,a,and,b,?,In-control ARL,?,Out-of-control ARL,?,Power,of a test?,Basic Concepts of SPC,Myths and Misconceptions of SPC Charts,Lets check our understanding by looking at the most common control chart,X-chart.,LSL,USL,Putting USL&LSL on X-chart helps ensure that parts are meeting,Cpk,requirements.,Myth No.1,It is alright to shrink USL&LSL down to,say,70%or 80%,to establish UCL and LCL.,Myth No.2,More myths and misconceptions on the X-chart.,LSL,USL,Fortunately,parts still meet customer spec.although process is out of control,Myth No.3,Process in control:therefore parts meet customer spec.as well as,Cpk,requirements,Myth No.4,Myths and Misconceptions of SPC Charts,LSL,USL,More myths and misconceptions of the X-chart.,How did we establish the sampling plan?,-Gut feel?,-Passed-down figures?,-Statistical calculation?,-Technical judgement?,5,pcs,/2 hr,Myth No.6,Myth No.5,A,2,R,We know A,2,R=3,s,.But which,s,?,s,x,s,x,s,within,s,overall,?,Myths and Misconceptions of SPC Charts,Variability,The Devil is in the Deviations,.No two things can ever be made exactly alike,just like no two things are alike in nature.,Variation cannot be avoided in life!Every process has variation.Every measurement.Every sample!,LSL,USL,T,Time 1,Time 2,Time 3,Time 4,Sources of Variation,Variability can come about due to changes in:,Material quality,Machine settings or conditions,Manpower standards,Methods of processing,Measurement,Environment,Types of Variation,One way of classifying variation is:,within unit(,positional variation,),between units(,unit-unit variation,),between lots(,lot-lot variation,),between lines(,line-line variation,),across time(,time-time variation,),measurement(,gage repeatability&reproducibility,),Quality and Variability,Quality is fitness for use,What is“Quality”?,Product Control,Model for Quality Control,Raw Material,Components&Sub-Assemblies,Process,Product,Inspection,Pass,Ship,Fail,Rework,Scrap,Ship,Recycle,Disposal,Process Control,Model for Quality Control,Raw Material,Components&Sub-Assemblies,Process,Product,Observation:,Data Collection,Evaluation:,Data Analysis,Diagnosis:,Fault Discovery,Decision:,Formulate Action,Implementation:,Take Action,Uncontrollable Inputs,Controllable Inputs,Statistical Process Control,The,process control,model shifts focus to the home front,i.e.the,manufacturing process,taking a preventive instead of reactive mode.,It also has something which the old concept of product control lacked-,statistics,.This allows use of samples to understand the entire process.,The new emphasis had to have a name-,Statistical Process Control(SPC),.,We owe the application of statistics as a tool for manufacturing to,Dr Walter A.,Shewhart,.,Dr Walter A.,Shewhart,Father of Control Charts,Physicist at Bell Telephone Labs.,specializing in the Brownian movement.,Asked to help in the war effort to design standard radio headset for army troops.,Developed important descriptive statistics,to aid in manufacturing,the most important of which was the,X-R chart,(invented in 1924).,Presented to the outside world in a series of lectures at Stevens Institute of Technology.The lecture material became his well-known book,Economic Control of Quality of Manufactured Product,(1931).,Success in Manufacturing,The key to success in manufacturing is an effective SPC program that continuously finds and eliminates problems.,Central to an SPC program are the following:,Understand the causes of variability:,Shewhart,found two basic causes of variability:,Chance causes of variability,Assignable causes of variability,Develop methods of recognizing these causes:,SPC charts,Introduction to SPC Charts,Concepts and Principles of Control Charts,Lets dive into them now.,Two Basic Causes of Variability,Chance Causes,unavoidable sources,Assignable Causes,can be controlled,Two Basic Causes of Variability,Chance Causes of Variation,Due to the cumulative effect of many small unavoidable sources of variation.,Also known as:,common variation,random variation,inherent variation,natural variation,A process operating with only chance causes of variation present is said to be“,in statistical control,”.,Two Basic Causes of Variability,Assignable(or Special)Causes of Variation,Variation in a process that is different from from chance variation;disturbs a process so that what it produces seems unnatural.,Examples of such causes of variation are:,improperly adjusted machine,excessive tool wear,defective raw material,A process operating in the presence,of assignable causes of variation is,said to be“,out-of-control,”.,Objectives of SPC Charts,All control charts have one primary purpose!,To,detect assignable causes of variation,that cause significant process shift,so that:,investigation and corrective action may be undertaken to rid the process of the assignable causes of variation before too many non-conforming units are produced.,in other words,to keep the process in statistical control.,Objectives of SPC Charts,The following are secondary objectives or direct benefits of the primary objective:,To reduce variability in a process.,To help estimate the parameters of a process and establish its process capability.,Graphical comparison of a quality characteristic against computed control limits.,Usually,its,sample statistic,is plotted over time.Sometimes,the actual value of the quality characteristic is plotted.,Lower Control Limit,Upper Control Limit,Center Line,Sample Number or Time,Sample Quality Characteristic,Each point is usually a sample statistic(such as subgroup average)of the quality characteristic,General Form of SPC Charts,Control charts plot variation over time.,Control limits,Upper Control Limit(UCL),and,Lower Control Limit(LCL),help us distinguish between the two basic causes of variability.,Lower Control Limit,Upper Control Limit,Center Line,Sample Number or Time,Sample Quality Characteristic,General Form of SPC Charts,Center Line represents mean operating level of process,UCL&LCL are vital guidelines for deciding when action should be taken in a process,A point outside of UCL or LCL is evidence that process is,out of control,:,Investigation and corrective action are required to eliminate the assignable cause(s).,Assignable cause(s)may be measuring error,plotting error,special variation from some process input,etc.,Lower Control Limit,Upper Control Limit,Center Line,Sample Number or Time,Sample Quality Characteristic,General Form of SPC Charts,Out-of-control signal:Investigate assignable cause(s).,Process Control,vs,Process Capability,At this juncture,lets distinguish between,process control,and,process capability,.,Process Control,Means that chance causes are the only source of variation present.,Refers to“,voice of the process,”,i.e.we only need data from the process to determine if a process is in control.,Quality characteristic is monitored to verify if it forms a stable distribution over time,using control limits computed from the process data only.,Just because a process is in control,does not necessarily mean,it is a capable process,.,The“,goodness,”of a process is measured by its process capability.,Compares“,voice of the process,”with“,voice of the customer,”,which is given in terms of customer specs.or requirements.,Measures how well a stable distribution(process in control)meets customer requirements by the proportion of products within or out of customer specs.,Process Capability,Control Limits,vs,Spec.Limits,Specification Limits,(USL,LSL),determined by design considerations,represent the tolerable limits of individual values of a product,usually external to variability of the process,Control Limits,(UCL,LCL),derived based on variability of the process,usually apply to sample statistics such as,subgroup average,or,range,rather than individual values,Three Types of Limits,Distribution of,Individual Values,Distribution of,Sample Averages,Spec.limits should,not,be placed on control charts for subgroups:,Spec.limits involve,individual values,of the quality characteristic,while control limits involve,sample statistics,of the quality characteristic.,Spec.limits are imposed by external demands,&thus,do not help identify assignable causes,.,Points plotted within the control limits do not indicate directly that parts are meeting customer specs.or,Cpk,requirements.,Specification Limits has No Business in Control Charts!,Shewhart,control charts are characterized by having control limits set at,k,s,distance from process mean.A usual value of k is 3,giving:,Upper Control Limit=,w,+,3,w,Center Line=,w,Lower Control Limit=,w,3,w,Whether the data is variable or attribute,Shewhart,control charts plot the sample statistic of the quality characteristic of interest.,Shewhart,Control Charts-Overview,Shewhart,Variables Control Charts for Subgroups,Variables Control Charts,for Subgroups,(X-R&X-,S,Charts),Central Limit Theorem and Normal Distribution,Shewhart,variables control charts for subgroups work because of two important principles:,Central Limit Theorem,Normal Distribution,Shewhart,found that when the averages of subgroups from a,constant-cause system,are plotted in the form of a histogram,the normal distribution appears.,Central Limit Theorem and Normal Distribution,The constant-cause system does not itself have to be normally distributed.It can be skewed,rectangular or even inverted pyramid.,As long as the,sample size is adequately large,the averages of the subgroups will show a central tendency and variation that tend to follow the normal curve.,This is called the,Central Limit Theorem,.,Central Limit Theorem and Normal Distribution,This discovery means that a process can be monitored over time by measuring the averages of a subgroup of parts(basis for X-chart).,If the process is a constant-cause system,these averages would fall within a normal curve.The variability is entirely due to common causes.,When assignable causes appear,they will affect the averages to the point where these averages will probably not fit within the normal curve.,Central Limit Theorem and Normal Distribution,Important Information from Central Limit Theorem:,If k observations of sample size n are taken,the distribution of x,1,x,2,x,k,will approximate a normal distribution N(,x,x,)distribution,with,Construction of X-R Charts,The X-R chart is the most versatile of control charts,and is used in most applications.,Charting of averages and charting of ranges are used to check if a constant-cause system exists.,X-chart measures variability between samples,R-chart measures variability within samples,The control limits are the estimated+/-3 sigma limits for the process.,Tables of constants were developed to make the sigma calculations simple and to reduce error.,Construction of X-R Charts,The Center Line and Control Limits of a X,-,chart:,The Center Line and Control Limits of a R-chart:,Construction of X-R Charts,For sample size n 10,R loses its efficiency in estimating process sigma and R-chart may not be appropriate.,Construction of X-R Charts,Shewhart,Constants,Control Charts Sampling Risks,Since the control limits are the+/-3 sigma limits for the process,the interval between the limits cover 99.73%of the normal distribution,.,Control Charts Sampling Risks,If there is no change in the process,there is still a chance of getting a point out of the 3,s,control limits,.What is the implication?,3,s,3,s,99.73%,Lower Control Limit,Center Line,Upper Control Limit,What does each area of 0.135%mean?,0.135%,0.135%,Control Charts Sampling Risks,Type I Error,Concluding that the process is out of control when it is really in control,=,probability of making Type I error,=commonly known as the,producers risk,=total of 0.27%for control limits of+/-3,s,Lower Control Limit,Upper Control Limit,Center Line,Sample Number or Time,0.135%,0.135%,Is process really out of control?Or is the point outside due to random variation?,Control Charts Sampling Risks,Type I Error and Tampering,If the process is really in control,and process adjustment is made because of Type I error,it is called,tampering,with the process.,Tampering has been shown to actually,increase,the variability of the process!,Control Charts Sampling Risks,Type II Error,Concluding that the process is in control when it is really out of control,=,probability of making Type II error,=commonly known as the,consumers risk,Lower Control Limit,Upper Control Limit,Center Line,Sample Number or Time,0.135%,0.135%,Is process really in control?Or does the point belong to a shifted process and it happens to be within the control limits?,Shifted Process,Control Charts Sampling Risks,The control chart is a test of the hypothesis that the process is in statistical control.,Lower Control Limit,Upper Control Limit,Center Line,Sample Number or Time,Sample Quality Characteristic,Out-of-control signal,Reject H,0,:,-Process has shifted,-Assignable causes,present,In-control signal,Accept H,0,:,-Process remains,unchanged,-No assignable causes,present,Setting Control Limits,Sigma Limits Approach,Control limits are usually set 3,w,units away from the center line for a,Type I error,of 0.27%.,These limits are called,3 control limits,.,Probability Limits Approach,Control limits are set at 3.09,w,units away from the center line for a,Type-I error,of 0.2%.,These limits are called,0.1%probability limits,.,3.09,s,3.09,s,99.8%,0.1%,Lower Probability Limit,Center Line,0.1%,Upper Probability Limit,0.1%,0.1%,Setting Control Limits,a,=0.2%,Probability Limits Approach,More popular in the UK and Western Europe,Control Limits&Sampling Risks,By moving the control limits further from the center line,the risk of a,Type I error,is reduced.,However,widening the control limits will increase the risk of a,Type II error,.,For a given,Type I error,(given control limit interval),the risk of a,Type II error,can be reducedby increasing the sample size,n.,Lower Control Limit,Upper Control Limit,Center Line,Shifted Process,a/2,a/2,b,Average Run Length(ARL),Associated with the,a,and,b,risks are 2 parameters of the control chart:,In-control Average Run Length,(ARL,0,),Out-of-Control Average Run Length,(ARL,1,).,The ARL is the average number of points that must be plotted before a point indicates an,out-of-control,condition.,Control Charts Average Run Length(ARL)