Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm
The key characteristics of test and measurement (T&M) manufacturing are short production runs, multi-product families and testing at multi-stations. Classical Shewhart control charts, namely x̄ chart and R chart have been widely used in statistical process control (SPC). Short production runs in...
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| Format: | Thesis |
| Language: | English |
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2018
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| Online Access: | http://eprints.usm.my/47816/1/Modified%20Statistical%20Process%20Control%20For%20Short%20Runs%20Test%20And%20Measurement%20Process%20To%20Reduce%20False%20Alarm.pdf |
| _version_ | 1797011835745992704 |
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| author | Koh, Chin Kok |
| author_facet | Koh, Chin Kok |
| author_sort | Koh, Chin Kok |
| collection | USM |
| description | The key characteristics of test and measurement (T&M) manufacturing are short production runs, multi-product families and testing at multi-stations. Classical Shewhart control charts, namely x̄ chart and R chart have been widely used in statistical process control (SPC). Short production runs in T&M render these charts inefficacious as inherent meager data do not warrant meaningful control limits. Measurement errors increase the risks of false acceptance and rejection, thereby leading to consequences such as unnecessary process adjustment and loss of confidence in SPC. Industry practice allows the installation of Guard band, e.g., through Guide to the Expression of Uncertainty in Measurement (GUM) to reduce the width of acceptance limit, as an indirect way to compensate the measurement errors. Past related works which presented standardized observations technique is highly recommended due to its simplicity and practicality. However, the concern is that this technique requires sufficient data to calculate the control limits and it does not deal with the effect of measurement errors. Based on this premise, the research objective is to develop a modified SPC model by considering measurement uncertainty in modified control charts (Z chart and W chart) for short runs T&M process in multi-stations. The implementation of this model involves two phases. Phase I retrospective analysis computes the input parameters, such as the standard deviation of the measurement uncertainty, measurement target and estimate of the population standard deviation. Thereafter, Five-band setting and S–factor are proposed to estimate process standard deviation to maximize the the opportunity to detect assignable causes with low false-reject rate. Lastly, the modified Z chart and W chart are generated in Phase II using standardized observations technique that considers the measurement target and the estimated process standard deviations. Run tests based on Nelson’s rules to interpret the control charts. In terms of validation, three case studies, labeled as Case I, Case II and Case III were conducted with different ratios of standard deviations in measurement uncertainty and population to demonstrate the effectiveness of the proposed model. A complete year’s data samples were collected from products tested at multi-stations in a T&M manufacturing facility at Bayan Lepas, Penang. For Case I with the measurement error is negligible and does not affect the process standard deviation; the results indicate that there were no false alarm points found in all methods. In Case II with the measurement error may noticeably affect the process standard deviation, and the results show that the model with Five-band setting and S-factor reduced the false alarm rate by 100% in comparison to the classical Shewhart method, except for the Five-band setting which has a smaller sustained shift (25% false alarm) was falsely detected in station WH05. In Case III with the measurement error is relatively larger and appeared to be more significantly affecting the process standard deviation; the results reveal that both proposed methods performed well in modified Z and W charts, which reduced false alarm rate by 50% for station WH05, 0% for station WH06 and 37.5% for station WH07. As a conclusion, the research has proposed and demonstrated the modified SPC model can address the understudied issues caused by short production runs and measurement errors. The model is practical for T&M manufacturing to reduce false alarms and to prevent unnecessary process adjustment. |
| first_indexed | 2024-03-06T15:40:08Z |
| format | Thesis |
| id | usm.eprints-47816 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:40:08Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | usm.eprints-478162021-11-17T03:42:13Z http://eprints.usm.my/47816/ Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm Koh, Chin Kok T Technology TJ1-1570 Mechanical engineering and machinery The key characteristics of test and measurement (T&M) manufacturing are short production runs, multi-product families and testing at multi-stations. Classical Shewhart control charts, namely x̄ chart and R chart have been widely used in statistical process control (SPC). Short production runs in T&M render these charts inefficacious as inherent meager data do not warrant meaningful control limits. Measurement errors increase the risks of false acceptance and rejection, thereby leading to consequences such as unnecessary process adjustment and loss of confidence in SPC. Industry practice allows the installation of Guard band, e.g., through Guide to the Expression of Uncertainty in Measurement (GUM) to reduce the width of acceptance limit, as an indirect way to compensate the measurement errors. Past related works which presented standardized observations technique is highly recommended due to its simplicity and practicality. However, the concern is that this technique requires sufficient data to calculate the control limits and it does not deal with the effect of measurement errors. Based on this premise, the research objective is to develop a modified SPC model by considering measurement uncertainty in modified control charts (Z chart and W chart) for short runs T&M process in multi-stations. The implementation of this model involves two phases. Phase I retrospective analysis computes the input parameters, such as the standard deviation of the measurement uncertainty, measurement target and estimate of the population standard deviation. Thereafter, Five-band setting and S–factor are proposed to estimate process standard deviation to maximize the the opportunity to detect assignable causes with low false-reject rate. Lastly, the modified Z chart and W chart are generated in Phase II using standardized observations technique that considers the measurement target and the estimated process standard deviations. Run tests based on Nelson’s rules to interpret the control charts. In terms of validation, three case studies, labeled as Case I, Case II and Case III were conducted with different ratios of standard deviations in measurement uncertainty and population to demonstrate the effectiveness of the proposed model. A complete year’s data samples were collected from products tested at multi-stations in a T&M manufacturing facility at Bayan Lepas, Penang. For Case I with the measurement error is negligible and does not affect the process standard deviation; the results indicate that there were no false alarm points found in all methods. In Case II with the measurement error may noticeably affect the process standard deviation, and the results show that the model with Five-band setting and S-factor reduced the false alarm rate by 100% in comparison to the classical Shewhart method, except for the Five-band setting which has a smaller sustained shift (25% false alarm) was falsely detected in station WH05. In Case III with the measurement error is relatively larger and appeared to be more significantly affecting the process standard deviation; the results reveal that both proposed methods performed well in modified Z and W charts, which reduced false alarm rate by 50% for station WH05, 0% for station WH06 and 37.5% for station WH07. As a conclusion, the research has proposed and demonstrated the modified SPC model can address the understudied issues caused by short production runs and measurement errors. The model is practical for T&M manufacturing to reduce false alarms and to prevent unnecessary process adjustment. 2018-09-01 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/47816/1/Modified%20Statistical%20Process%20Control%20For%20Short%20Runs%20Test%20And%20Measurement%20Process%20To%20Reduce%20False%20Alarm.pdf Koh, Chin Kok (2018) Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | T Technology TJ1-1570 Mechanical engineering and machinery Koh, Chin Kok Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm |
| title | Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm |
| title_full | Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm |
| title_fullStr | Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm |
| title_full_unstemmed | Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm |
| title_short | Modified Statistical Process Control For Short Runs Test And Measurement Process To Reduce False Alarm |
| title_sort | modified statistical process control for short runs test and measurement process to reduce false alarm |
| topic | T Technology TJ1-1570 Mechanical engineering and machinery |
| url | http://eprints.usm.my/47816/1/Modified%20Statistical%20Process%20Control%20For%20Short%20Runs%20Test%20And%20Measurement%20Process%20To%20Reduce%20False%20Alarm.pdf |
| work_keys_str_mv | AT kohchinkok modifiedstatisticalprocesscontrolforshortrunstestandmeasurementprocesstoreducefalsealarm |