Webinar On "Sampling by Variables; How to Use ANSI/ASQ Z1.9 (formerly MIL-STD 414)"

Description : ANSI/ASQ Z1.9 (formerly MIL-STD 414) is a generally accepted sampling standard for products with real-number (variables) data. Acceptance or rejection depends on the sample statistics. The sampling plan itself is a function of the lot size, acceptable quality level (AQL), and sampling level. Switching rules similar to those for ANSI/ASQ Z1.4 (MIL-STD 105) determine whether sampling is at the normal, tightened, or reduced level. Objective : Use ANSI/ASQ Z1.9 to select the appropriate sampling plan and acceptance criteria based on (1) the lot size, (2) the sampling level, and (3) the acceptable quality level (AQL). Know that these plans rely on the assumption that the critical to quality characteristic follows the normal (bell curve) distribution. The required sample size is much lower when the process standard deviation is known (as it might be from a process capability study) than when it must be estimated from the sample statistics. Determine whether to accept o... Read more

Description : ANSI/ASQ Z1.9 (formerly MIL-STD 414) is a generally accepted sampling standard for products with real-number (variables) data. Acceptance or rejection depends on the sample statistics. The sampling plan itself is a function of the lot size, acceptable quality level (AQL), and sampling level. Switching rules similar to those for ANSI/ASQ Z1.4 (MIL-STD 105) determine whether sampling is at the normal, tightened, or reduced level. Objective : Use ANSI/ASQ Z1.9 to select the appropriate sampling plan and acceptance criteria based on (1) the lot size, (2) the sampling level, and (3) the acceptable quality level (AQL). Know that these plans rely on the assumption that the critical to quality characteristic follows the normal (bell curve) distribution. The required sample size is much lower when the process standard deviation is known (as it might be from a process capability study) than when it must be estimated from the sample statistics. Determine whether to accept or reject the lot depending on the sample statistics. Estimate, where necessary, the nonconforming fraction at each specification limit to make the acceptance/rejection decision. Use switching rules to select normal, tightened, or reduced inspection depending on quality performance and consistency of production. Areas Covered in the Session : Practical requirements for drawing the sample Plan considerations: one-sided or two-sided specification? Equal or unequal AQLs for the two specification limits? Process variation known or unknown? Selection of normal, tightened, or reduced inspection as a function of quality performance and consistency of production Definition of the appropriate sample plan (sample size and acceptance criteria) on the basis of the lot size N, sampling level, and AQL Calculation of sample statistics and quality indices, and estimation of the nonconforming fraction (at each specification limit if necessary) to determine whether the lot should be accepted Use of the range method; an important takeaway is that ranges are for groups of 5 parts and not the total sample. If the sample size is 15, the range is the average range for three groups of five parts as opposed to one range for 15 parts. An appendix in the handout (the webinar will not spend time on the material as it is not necessary to actually use the standard) will show how to derive the operating characteristic curves for situations in which the process variation is known, and when only the sample standard deviation is available. The latter requires use of the noncentral t distribution, which is handled by software Who Will Benefit: Manufacturing and quality engineers, technicians, and inspectors with responsibility for inspections that involve quantitative measurements of parts. About Speaker: William A. Levinson, P.E., FASQ, CFPIM, is the owner of Levinson Productivity Systems PC. He holds professional certifications from the American Society for Quality, APICS, and Society of Manufacturing Engineers.