Abstract

Manufacturing environment relies on fulfillment of product specifications and customer satisfaction. Quality characteristics of products are measured for assessing its accordance to requirements, but measurement error might mask true process performance. Measurement system variability must be small with respect to product specifications as well to process variation. Total variation in a manufacturing process is a combination of part-to-part variation and measurement variation. Prior to endeavor in process analysis and enhancement it is highly recommended to perform a measurement system capability study. The analysis of the measurement system has to take into account three basic components, appraiser, equipment and product. Measurement system analysis (MSA) relies on statistical tools, such as Gauge R&R study, to ensure that the measurement system is in acceptable conditions to monitor the manufacturing process. Main objective of the thesis is to analyze how sensitive are the confidence intervals of variability components and capability criteria to the design of a Gauge R&R study. We conducted extensive numerical experiments using simulation. Findings will be of help as guidance to practitioners when dealing with parameter allocation decisions in a Gauge R&R study.