Reducing process variability is essential to many organisations. According to the pertinent literature, a quality system that utilizes quality techniques to reduce process variability is necessary. Quality programs that respond to measurement precision are central to quality systems, and the most common method of assessing the precision of a measurement system is repeatability and reproducibility (R&R). Few studies have investigated R&R using attribute data. In modern manufacturing environments, automated manufacturing is becoming increasingly common; however, a measurement resolution problem exists in automatic inspection equipment, resulting in clusters and product defects. It is vital to monitor effectively these bivariate quality characteristics. This study presents a novel model for calculating R&R for bivariate attribute data. An alloy manufacturing case is utilized to illustrate the process and potential of the proposed model. Findings can be employed to evaluate and improve measurement systems with bivariate attribute data.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty