PROCESS CAPABILITY ESTIMATION FOR NON-NORMALLY DISTRIBUTED DATA USING ROBUST METHODS - A COMPARATIVE STUDY

Abstract

Process capability indices are very important process quality assessment tools in automotive industries. The common process capability indices (PCIs) Cp, Cpk, Cpm are widely used in practice. The use of these PCIs based on the assumption that process is in control and its output is normally distributed. In practice, normality is not always fulfilled. Indices developed based on normality assumption are very sensitive to non- normal processes. When distribution of a product quality characteristic is non-normal, Cp and Cpk indices calculated using conventional methods often lead to erroneous interpretation of process capability. In the literature, various methods have been proposed for surrogate process capability indices under non normality but few literature sources offer their comprehensive evaluation and comparison of their ability to capture true capability in non-normal situation. In this paper, five methods have been reviewed and capability evaluation is carried out for the data pertaining to resistivity of silicon wafer. The final results revealed that the Burr based percentile method is better than Clements method. Modelling of non-normal data and Box-Cox transformation method using statistical software (Minitab 14) provides reasonably good result as they are very promising methods for non - normal and moderately skewed data (Skewness <= 1.5).

Keywords

• Process capability indices
• Non - normal process
• Clements method
• Box - Cox transformation
• Burr distribution
• probability plots