Improved Measurement-System Assessment for Processes with 100% Inspection

Abstract

We consider the assessment of an automated continuous measurement system used for 100% inspection in a high- volume manufacturing process. Because of the automation, we assume that there are no operator effects. If the system stores the measured values, we effectively know the current process mean and standard deviation. Because of the high volume, we have parts available with values spread across the whole distribution.

The standard plan for measurement-system assessment is to select k parts at random from the process and measure each of the selected parts n times. We then estimate the repeatability of the system using ANOVA. We propose two improvements. First, we demonstrate the substantial value of using the known process characteristics in the analysis. Second, we describe an alternative sampling plan where we deliberately select parts with extreme values from the population of measured parts to remeasure. We call this selection leveraging. We discuss the analysis of the leveraged plan and show that it is more efficient than the standard plan. We also discuss the planning and implementation of a leveraged assessment study and some associated issues and extensions.

• Components of Variation
• Gauge R&R
• Leveraging
• Measurement System Assessment
• Sample Size Determination

Additional information

Notes on contributors

Ryan P. Browne

Mr. Browne is a PhD candidate in the Department of Statistics and Actuarial Science at the University of Waterloo. His email address is [email protected].

R. Jock MacKay

Dr. MacKay is an Associate Professor in the Department of Statistics and Actuarial Science and Director of the Business and Industrial Statistics Research Group. He is a member of ASQ. His email address is [email protected].

Stefan H. Steiner

Dr. Steiner is an Associate Professor in the Department of Statistics and Actuarial Science. He is a senior member of ASQ. His email address is [email protected].