Task failure prediction for wafer-handling robotic arms by using various machine learning algorithms
Industries are increasingly adopting automatic and intelligent manufacturing in production lines, such as those of semiconductor wafers, optoelectronic devices, and light-emitting diodes. For example, automatic robot arms have been used for pick-and-place workpiece applications. However, repairing a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2021-05-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940211003938 |
| _version_ | 1818590398241570816 |
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| author | Ping Wun Huang Kuan-Jung Chung |
| author_facet | Ping Wun Huang Kuan-Jung Chung |
| author_sort | Ping Wun Huang |
| collection | DOAJ |
| description | Industries are increasingly adopting automatic and intelligent manufacturing in production lines, such as those of semiconductor wafers, optoelectronic devices, and light-emitting diodes. For example, automatic robot arms have been used for pick-and-place workpiece applications. However, repairing automatic robot arms is time-consuming and increases the downtime of equipment and the cycle time of manufacturing. In this study, various machine learning (ML) models, such as the general linear model (GLM), random forest, extreme gradient boosting, gradient boosting machine, and stacked ensemble, were used to predict the maximum Cartesian positioning shift (i.e. the maximum eccentric distance) in the next handling time period (e.g. 1 min). A charge-coupled-device-based fault diagnostic system was developed to measure the critical positions of the robotic arm when transferring wafers. A novel data augmentation method was used to determine the correlation parameters in the dataset for the ML models. The prediction error for each algorithm was determined using the root mean square error (RMSE). The results revealed that the GLM exhibited the lowest prediction errors. The RMSEs of the GLM were 0.024, 0.032, and 0.046 mm for 3421 pickups, the last 1000 pickups, and 100 pickups, respectively, for the prediction target. Thus, the GLM is a promising model for predicting the task failure of wafer-handling robotic arms. |
| first_indexed | 2024-12-16T09:55:54Z |
| format | Article |
| id | doaj.art-33c527421ee54fa3b37f1452d4f155d8 |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-12-16T09:55:54Z |
| publishDate | 2021-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-33c527421ee54fa3b37f1452d4f155d82022-12-21T22:35:55ZengSAGE PublishingMeasurement + Control0020-29402021-05-015410.1177/00202940211003938Task failure prediction for wafer-handling robotic arms by using various machine learning algorithmsPing Wun HuangKuan-Jung ChungIndustries are increasingly adopting automatic and intelligent manufacturing in production lines, such as those of semiconductor wafers, optoelectronic devices, and light-emitting diodes. For example, automatic robot arms have been used for pick-and-place workpiece applications. However, repairing automatic robot arms is time-consuming and increases the downtime of equipment and the cycle time of manufacturing. In this study, various machine learning (ML) models, such as the general linear model (GLM), random forest, extreme gradient boosting, gradient boosting machine, and stacked ensemble, were used to predict the maximum Cartesian positioning shift (i.e. the maximum eccentric distance) in the next handling time period (e.g. 1 min). A charge-coupled-device-based fault diagnostic system was developed to measure the critical positions of the robotic arm when transferring wafers. A novel data augmentation method was used to determine the correlation parameters in the dataset for the ML models. The prediction error for each algorithm was determined using the root mean square error (RMSE). The results revealed that the GLM exhibited the lowest prediction errors. The RMSEs of the GLM were 0.024, 0.032, and 0.046 mm for 3421 pickups, the last 1000 pickups, and 100 pickups, respectively, for the prediction target. Thus, the GLM is a promising model for predicting the task failure of wafer-handling robotic arms.https://doi.org/10.1177/00202940211003938 |
| spellingShingle | Ping Wun Huang Kuan-Jung Chung Task failure prediction for wafer-handling robotic arms by using various machine learning algorithms Measurement + Control |
| title | Task failure prediction for wafer-handling robotic arms by using various machine learning algorithms |
| title_full | Task failure prediction for wafer-handling robotic arms by using various machine learning algorithms |
| title_fullStr | Task failure prediction for wafer-handling robotic arms by using various machine learning algorithms |
| title_full_unstemmed | Task failure prediction for wafer-handling robotic arms by using various machine learning algorithms |
| title_short | Task failure prediction for wafer-handling robotic arms by using various machine learning algorithms |
| title_sort | task failure prediction for wafer handling robotic arms by using various machine learning algorithms |
| url | https://doi.org/10.1177/00202940211003938 |
| work_keys_str_mv | AT pingwunhuang taskfailurepredictionforwaferhandlingroboticarmsbyusingvariousmachinelearningalgorithms AT kuanjungchung taskfailurepredictionforwaferhandlingroboticarmsbyusingvariousmachinelearningalgorithms |