A neural network applied to estimate process capability of non-normal processes |
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| Authors: | Babak Abbasi |
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| Affiliation: | 1. Department of Medical Biometry, Institute for Quality and Efficiency in Health Care, Im Mediapark 8, D–50670, Cologne, Germany;2. Faculty of Medicine, University of Cologne, Joseph-Stelzmann-Str. 20, D-50931 Cologne, Germany;1. Graduate School of Logistics, Incheon National University, Incheon 406-772, South Korea;2. Division of Infrastructure Systems and Maritime Studies, Nanyang Technological University, Singapore, Singapore;1. Maritime Institute@NTU, Nanyang Technological University, Nanyang Avenue, Singapore;2. School of Civil & Environmental Engineering, Nanyang Technological University, Singapore;3. School of Business IT & Logistics, RMIT University, Australia;1. Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan;2. Department of Statistics, Tamkang University, Tamsui, Taiwan;3. LPMC and Department of Statistics, Nankai University, Tianjin, China |
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| Abstract: | It is always crucial to estimate process capability index (PCI) when the quality characteristic does not follow normal distribution, however skewed distributions come about in many processes. The classical method to estimate process capability is not applicable for non-normal processes. In the existing methods for non-normal processes, probability density function (pdf) of the process or an estimate of it is required. Estimating pdf of the process is a hard work and resulted PCI by estimated pdf may be far from real value of it. In this paper an artificial neural network is proposed to estimate PCI for right skewed distributions without appeal to pdf of the process. The proposed neural network estimates PCI using skewness, kurtosis and upper specification limit as input variables. Performance of proposed method is validated by simulation study for different non-normal distributions. Finally, a case study using the actual data from a manufacturing process is presented. |
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