As manufacturing systems become increasingly complex, manufacturing enterprises face the challenging need for precise, effective process capability analyses. However, acquisition of process data, characterized by larger volume and complexity, is much easier, consequently making process capability analysis more difficult. Traditional process capability analysis methods such as Cp or Cpk are not adequate for large volume process data collected over time because these methods assume that process distribution remains unchanged. Therefore, the goal of this paper is to explore the use of sample entropy (SampEn) as it relates to univariate process capability analysis. The proposed method, which alleviates the fixed distribution assumption, can identify changing process variations over time. We proposed a novel method based on Adjusted Sample Entropy (AdSEn) to quantify process variation changes. A study based on simulation data sets showed that the proposed method provides adequate process capability information.

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