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1、Statistical Process Control,Statistical Process Control,Variability exists in everything - people, machines and nature Purpose - to be sure that processes are performing in an acceptable manner monitor over time forecast if will still be well in future determine factors that make it out of control,S
2、tatistical Process Control,Design quality checks into the process - eliminate need for inspection build better product during design make product withstand variation - Taguchi Method,History,Prior to IR craftsmen were responsible for entire process - pride of workmanship During 1920s W. Shewart of B
3、ell Labs build Control Charts - ASQ formed During 1950s Deming introduced QC techniques to Japanese 1961 Feigenbaum wrote Total Quality Control,Statistical Process Control,Two main subjects Acceptance Sampling - test random samples of finished goods and accept or reject whole lot Process Control - s
4、ample process output at different points in time - see if process is in control - determine cause of out-of-control behavior,Statistical Process Control,Monitor the production process in one of three places - before, during or after Physically examine some of the units Basic issues; where do we insp
5、ect how much and how often do we inspect do we inspect variables or attributes Balance two costs: inspection and service,Rules of thumb for where to inspect;,1. Inputs - do not pay for goods that do not meet standards 2. Finished Goods - protect your reputation 3. Before a costly operation - do not
6、waste time and labor 4. Before an irreversible operation - rework only up to a certain point 5. Before a covering operation - painting or assembly,Types of Sampling,Defn: Attribute Sampling - check for the presence or absence of a product characteristic Defn: Variable Sampling - product characterist
7、ics that can be measured on a continuous scale,Statistical Process Control,used to evaluate the output of a process most processes exhibit some kind of variation random variation - natural variation, uncontrollable assignable variation - can identify cause determine whether non-random sources of var
8、iation are responsible for problems,Control Charts,Prevent production of excessive number of defects - look for causes of variability A time-ordered plot of sample statistics Control limits - determine the difference between random and assignable variation - usually 3 Each new sample statistic is pl
9、otted and compared to the control limits,StatisticalProcessControl,Types of Control Charts,1. Variable Control charts A. Means Chart - monitors central tendency B. Range Chart - monitors dispersion - both can be used to monitor same process - use 20 - 25 samples to build initial limits - take sample
10、s of 5 - 9 observations - reasonable amount of time and costs less,Types of Control Charts (cont),2. Attributes Control Charts A. P-Chart - count number of defects in sample B. C-Chart - count number of defects per unit -building control charts adds to the time and cost of production - attribute sam
11、pling is easier than variable - need larger sample sizes,Process Capability,Specify ranges for certain product dimensions during design Tolerances Max and min acceptable level of output Bearing for rotating shaft - diameter is 1.25 inches 0.005 Process has = 0.02 inches - a 3- control chart limits a
12、re wider than tolerances,Capability Index,Shows how well parts fit into the range specified by design limits If design limits are wider than process limits, the process can shift and still produce good parts Cpk =,Capability Index,Example - light bulbs are produced with an average life of 90 hours.
13、The target design life is 100 hours. The UTL = 120 and the LTL = 80 hours. If = 4.8 what is the Cpk?,4,Acceptance Sampling,Purposes Determine policy for accepting or rejecting a production lot of materials or components Sample quality is used to make decision Advantages Less handling damage Fewer in
14、spectors Applicability to destructive testing Entire lot rejection (motivation for improvement),5,Acceptance Sampling,Disadvantages Risks of accepting “bad” lots and rejecting “good” lots Added planning and documentation Sample provides less information than 100-percent inspection,Acceptance Plans,1
15、. Single-Sample Plans - only one sample define n and c Operating Characteristic Curve - graphs the performance of plan Acceptable Quality Level - defines good lots Lot Tolerance Percent Defective - defines bad lots Average Outgoing Quality - the percent defects in lots leaving inspection,Acceptance
16、Plans (cont),- Producers Risk ( ) - probability of rejecting good lot - Consumers Risk ( ) - probability of accepting a bad lot - can reduce errors by not biasing the sample and taking a larger one - use the Poisson distribution to compute probability of acceptance,9,Operating Characteristic Curve,Acceptance Plans,Plans considers the interaction among the above criteria - it considers the objectives of the supplier and consumer The producer would like low probability of rejecting a good lot
