2009•Quality EngineeringRequires access

A New Multivariate Process Capability Vector

Hamid Shahriari, Mohammadreza Abdollahzadeh

Open publisher page 111 citations

Abstract

Process Capability Indices, such as Cp and Cpk have been widely used as statistical tools to assess the manufacturing process performance. These indices provide numerical measures on process precision, process accuracy and process performance. Extensive researches have been done on the univariate process capability indices in the last two decades. However, process capability indices for processes with multiple quality characteristics have received little attention, comparatively. The multivariate process capability indices, which are used for evaluation of processes with correlated quality characteristics such as weight, height and width, could be investigated, in depth. In this research the concept of process capability and its relevant indices in univariate and multivariate cases are discussed. Based on the previous works, a new multivariate process capability vector (NMPCV) is introduced. This vector is based on a modification of the traditional multivariate process capability indices. The value and the power of this new index are evaluated using field and simulated data. The results of this research show that NMPCV is a better tool for judging the process accuracy, the process precision and the process performance. When the characteristics are highly correlated, the results are much better.

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What this paper is about

Process Capability Indices, such as Cp and Cpk have been widely used as statistical tools to assess the manufacturing process performance. These indices provide numerical measures on process precision, process accuracy and process performance. Extensive researches have been done on the univariate process capability indices in the last two decades. However, process capability indices for processes with multiple quality characteristics have received little attention, comparatively. The multivariate process capability indices, which are used for evaluation of processes with correlated quality characteristics such as weight, height and width, could be investigated, in depth. In this research the concept of process capability and its relevant indices in univariate and multivariate cases are discussed. Based on the previous works, a new multivariate process capability vector (NMPCV) is introduced. This vector is based on a modification of the traditional multivariate process capability indices. The value and the power of this new index are evaluated using field and simulated data. The results of this research show that NMPCV is a better tool for judging the process accuracy, the process precision and the process performance. When the characteristics are highly correlated, the results are much better.

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Available abstract

Process Capability Indices, such as Cp and Cpk have been widely used as statistical tools to assess the manufacturing process performance. These indices provide numerical measures on process precision, process accuracy and process performance. Extensive researches have been done on the univariate process capability indices in the last two decades. However, process capability indices for processes with multiple quality characteristics have received little attention, comparatively. The multivariate process capability indices, which are used for evaluation of processes with correlated quality characteristics such as weight, height and width, could be investigated, in depth. In this research the concept of process capability and its relevant indices in univariate and multivariate cases are discussed. Based on the previous works, a new multivariate process capability vector (NMPCV) is introduced. This vector is based on a modification of the traditional multivariate process capability indices. The value and the power of this new index are evaluated using field and simulated data. The results of this research show that NMPCV is a better tool for judging the process accuracy, the process precision and the process performance. When the characteristics are highly correlated, the results are much better.

Key concepts: Process capability, Univariate, Process capability index, Multivariate statistics, Process (computing), Multivariate analysis, Statistical process control, Computer science

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