The submitted article focuses on a detailed explanation of the average and range method (Automotive Industry Action Group, Measurement System Analysis approach) and of the honest Gauge Repeatability and Reproducibility method (Evaluating the Measurement Process approach). The measured data (thickness of plastic parts) were evaluated by both methods and their results were compared on the basis of numerical evaluation. Both methods were additionally compared and their advantages and disadvantages were discussed. One difference between both methods is the calculation of variation components. The AIAG method calculates the variation components based on standard deviation (then a sum of variation components does not give 100 %) and the honest GRR study calculates the variation components based on variance, where the sum of all variation components (part to part variation, EV & AV) gives the total variation of 100 %. Acceptance of both methods among the professional society, future use, and acceptance by manufacturing industry were also discussed. Nowadays, the AIAG is the leading method in the industry.Keywords: GRR study approach, the average and range method, the honest GRR study.

The article deals with the quality of measured data, which is necessary for effective quality management and successful implementation of the concept Industry 4.0 and the related concept Quality 4.0. The quality of the measured data is determined by the properties of the used measurement system, which are evaluated by measurement system analysis (MSA). Attention is paid to increasing the effectiveness of the repeatability and reproducibility analysis often used in practice. The importance of graphical tools of analysis, which are often neglected in practice, is emphasized in this regard, and new or modified graphical tools are proposed. The proposed graphical tools allow more detailed analysis of the data collected for the study and reveal the causes of the measurement system variability. The information obtained by applying these graphical tools is a valuable basis for proposals of appropriate actions to improve the measurement system. The use of the proposed graphical tools is presented in a real study of the repeatability and reproducibility of the measurement system.

No abstract

The evaluation of the measurement system quality has already become an integral part of quality planning activities in both the automotive and metallurgical industries. An important assumption for obtaining the most relia ble results is compliance with the basic assumptions for evaluating the variability of the measurement system. The main goal of this paper is to analyze, how the failure to meet the basic assumptions influences the evaluation of the measurement system's statistical properties. This goal is achieved by performing a detailed analysis of the latest developments in the field of measurement systems analysis aimed at verifying the assumptions of normality and uniformity. The evaluation of the effect of non-fulfillment of both assumptions on the values of the most important statistical properties of the measurement system is performed using simulated data. Suitable graphical tools are used for practical verification of both assumptions.

Evaluate the quality of this measurement system is possible by using multiple methods, which are described in the methodology guides for evaluation of the measurement system quality. Within these methods, values of different indicators are evaluated. This paper deals with the cross tabulation method and foremost Kappa indicator. More specifically is examined the effect of the number of used nonconforming samples on explanatory power of this indicator. The effect on the kappa values is examined from several aspects that must be taken into account during evaluation of the quality of measurement system for attributive quality characteristics.