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Unformatted text preview: أي اﻧﺤﺮاﻓﺎت ﻗﺪ ﻧﻼﺡﻈﻬﺎ.‬ ‫اﻟﻌﺪد اﻻﺝﻤﺎﻟﻲ ﻟﻠﻮﺡﺪات ﻓﻲ آﻞ ﻋﻴﻨﺔ‬ ‫=‪p‬‬ ‫‪p Chart‬‬ ‫ﺣﺴﺎب ﺣﺪود اﻟﻀﺒﻂ‬ ‫اﻟﺤﺪ اﻷﻋﻠﻰ ﻟﻠﻀﺒﻂ‬ ‫‪Upper Control Limit‬‬ ‫اﻟﺤﺪ اﻷدﻧﻰ ﻟﻠﻀﺒﻂ‬ ‫‪Lower Control Limit‬‬ ‫اﻻﻧﺤﺮاف اﻟﻤﻌﻴﺎري ﻟﻨﺴﺒﺔ اﻟﻤﻌﻴﺐ ‪σp‬‬ ‫‪Control Limits‬‬ ‫) ‪p (1 − p‬‬ ‫‪n‬‬ ‫‪= p+z‬‬ ‫) ‪p (1 − p‬‬ ‫‪n‬‬ ‫‪= p−z‬‬ ‫‪p‬‬ ‫‪p‬‬ ‫‪UCL‬‬ ‫‪LCL‬‬ ‫ﻣﺘﻮﺳﻂ ﻧﺴﺒﺔ اﻟﻤﻌﻴﺐ ﻓﻲ اﻟﻌﻴﻨﺎت ‪p‬‬ ‫‪s‬‬ ‫ﻳﻤﺜﻞ ‪ z‬ﻡﻌﺎﻡﻞ ﺽﺮب ﻧﺴﺘﻌﻤﻠﻪ آﺎﻟﺘﺎﻟﻲ:‬ ‫‪∑ xi‬‬ ‫;‪• z = 2 for 95.5% limits‬‬ ‫‪∑ ni‬‬ ‫1= ‪i‬‬ ‫‪s‬‬ ‫=‪p‬‬ ‫1= ‪i‬‬ ‫‪• z = 3 for 99.7% limits‬‬ ‫٥‬ ‫ﻡﺜﺎل ﻋﻤﻠﻲ ﻟﺨﺮﻳﻄﺔ ﻧﺴﺒﺔ اﻟﻤﻌﻴﺐ‬ ‫‪p chart‬‬ ‫ﺵﺮآﺔ ﺹﻨﺎﻋﻴﺔ ﺕﺼﻨﻊ ﻗﻄﻊ ﻣﻴﻜﺎﻧﻴﻜﻴﺔ ﻟﻤﺤﺮآﺎت اﻟﺪیﺰل. أﺧﺬت‬ ‫٠١ ﻋﻴﻨﺎت ﻣﻦ ﺧﻂ اﻻﻧﺘﺎج، ﺕﺤﺘﻮي آﻞ واﺡﺪة ﻋﻠﻰ ٠٠١‬ ‫ﻗﻄﻌﺔ و ﺕﻢ اﻟﺘﻔﺘﻴﺶ ﻋﻨﻬﺎ ﺡﺴﺐ ﻣﻮاﺹﻔﺎت ﻣﻌﻴﻨﺔ و رﺹﺪت‬ ‫أﻋﺪاد اﻟﻘﻄﻊ اﻟﻤﻌﻴﺒﺔ ﻋﻠﻰ اﻟﺠﺪول اﻟﺘﺎﻟﻲ:‬ ‫هﻞ ﻧﻈﺎم اﻟﺘﺼﻨﻴﻊ ﻣﻨﻀﺒﻂ اﺡﺼﺎﺋﻴﺎ أم ﻻ ؟‬ ‫01‬ ‫4‬ ‫٦‬ ‫٣‬ ‫9‬ ‫8‬ ‫7‬ ‫6‬ ‫5‬ ‫4‬ ‫3‬ ‫2‬ ‫1‬ ‫اﻟﻌﻴﻨﺔ‬ ‫3‬ ‫6‬ ‫2‬ ‫1‬ ‫4‬ ‫8‬ ‫3‬ ‫2‬ ‫5‬ ‫ﻋﺪد اﻟﻘﻄﻊ‬ ‫اﻟﻤﻌﻴﺒﺔ‬ ‫ﻡﺜﺎل ﻋﻤﻠﻲ‬ ‫‪p chart‬‬ ‫٢‬ ‫01 = ‪m‬‬ ‫ﻋﺪد اﻟﻌﻴﻨﺎت‬ ‫ﻋﺪد اﻟﻘﻄﻊ ﻓﻲ آﻞ ﻋﻴﻨﺔ 001 = ‪n‬‬ ‫01‬ ‫9‬ ‫8‬ ‫7‬ ‫6‬ ‫5‬ ‫4‬ ‫3‬ ‫2‬ ‫1‬ ‫اﻟﻌﻴﻨﺔ‬ ‫4‬ ‫3‬ ‫6‬ ‫2‬ ‫1‬ ‫4‬ ‫8‬ ‫3‬ ‫2‬ ‫5‬ ‫ﻋﺪد اﻟﻤﻌﻴﺐ‬ ‫40.0 30.0 60.0 20.0 10.0 40.0 80.0 30.0 20.0 50.0 ﻧﺴﺒﺔ اﻟﻤﻌﻴﺐ‬ ‫‪m‬‬ ‫ﻣﺘﻮﺳﻂ ﻧﺴﺒﺔ اﻟﻤﻌﻴﺐ ﻓﻲ آﻞ اﻟﻌﻴﻨﺎت‬ ‫830 . 0 =‬ ‫ˆ‬ ‫‪∑ pi‬‬ ‫1=‪i‬‬ ‫‪m‬‬ ‫=‪p‬‬ ‫٧‬ ‫ﻡﺜﺎل ﻋﻤﻠﻲ‬ ‫‪p chart‬‬ ‫٣‬ ‫) ‪p (1 − p‬‬ ‫‪n‬‬ ‫‪= p+z‬‬ ‫‪p‬‬ ‫‪UCL‬‬ ‫) ‪p (1 − p‬‬ ‫‪n‬‬ ‫‪= p−z‬‬ ‫‪p‬‬ ‫‪LCL‬‬ ‫1=‪i‬‬ ‫‪s‬‬ ‫=‪p‬‬ ‫3=‪z‬‬ ‫ﺣﺴﺎب ﺣﺪود اﻟﻀﺒﻂ‬ ‫‪s‬‬ ‫‪i‬‬ ‫‪∑x‬‬ ‫‪i‬‬ ‫‪∑n‬‬ ‫1=‪i...
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This document was uploaded on 09/27/2013.

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