An Approach for the Evaluation of a Measurement System: A Study on the Use of Machine Learning and Predictions
Quality control during the manufacturing process is an important factor in delivering products in electronics according to planned characteristics and properties. It concerns the capability of the chosen measurement system to perform precise and reliable measurement trials, which is evaluated mainly...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2023-12-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6450 |
| _version_ | 1797403590049923072 |
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| author | Malinka Ivanova Valentin Tsenev Vania Mikova |
| author_facet | Malinka Ivanova Valentin Tsenev Vania Mikova |
| author_sort | Malinka Ivanova |
| collection | DOAJ |
| description | Quality control during the manufacturing process is an important factor in delivering products in electronics according to planned characteristics and properties. It concerns the capability of the chosen measurement system to perform precise and reliable measurement trials, which is evaluated mainly through the utilization of measurement system analysis. In order to reduce time effort and to partially automate these operations, a methodology for the prediction of a part of the dataset through applying the Neural Net algorithm is proposed in this paper in two scenarios: (1) when two metrology experts are involved in the measurement in three trials and the data of a third specialist are predicted and (2) when three metrology specialists collect data in two trials and the data of the third trial are predicted. The developed predictive models in these two scenarios are assessed and they are characterized by high accuracy. Gage repeatability and reproducibility analysis are used to evaluate the measurement systems based on original and partially artificial datasets as the comparative results outline the suitability of the proposed approach, due to the proximity of the obtained values.
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| first_indexed | 2024-03-09T02:40:40Z |
| format | Article |
| id | doaj.art-b753c097f5c24a908c08b085c4854bce |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-09T02:40:40Z |
| publishDate | 2023-12-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-b753c097f5c24a908c08b085c4854bce2023-12-06T05:56:34ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362023-12-0113610.48084/etasr.6450An Approach for the Evaluation of a Measurement System: A Study on the Use of Machine Learning and PredictionsMalinka Ivanova0Valentin Tsenev1Vania Mikova2Department of Informatics, Faculty of Applied Mathematics and Informatics, Technical University of Sofia, BulgariaDepartment of Electronics and Energy Engineering, Technical College of Sofia, Technical University of Sofia, BulgariaFaculty of Engineering and Pedagogy – Sliven, Technical University of Sofia, BulgariaQuality control during the manufacturing process is an important factor in delivering products in electronics according to planned characteristics and properties. It concerns the capability of the chosen measurement system to perform precise and reliable measurement trials, which is evaluated mainly through the utilization of measurement system analysis. In order to reduce time effort and to partially automate these operations, a methodology for the prediction of a part of the dataset through applying the Neural Net algorithm is proposed in this paper in two scenarios: (1) when two metrology experts are involved in the measurement in three trials and the data of a third specialist are predicted and (2) when three metrology specialists collect data in two trials and the data of the third trial are predicted. The developed predictive models in these two scenarios are assessed and they are characterized by high accuracy. Gage repeatability and reproducibility analysis are used to evaluate the measurement systems based on original and partially artificial datasets as the comparative results outline the suitability of the proposed approach, due to the proximity of the obtained values. https://etasr.com/index.php/ETASR/article/view/6450machine learningartificial datasetmeasurement systemmeasurement system analysisGage Repeatability and Reproducibilityelectronics manufacturing |
| spellingShingle | Malinka Ivanova Valentin Tsenev Vania Mikova An Approach for the Evaluation of a Measurement System: A Study on the Use of Machine Learning and Predictions Engineering, Technology & Applied Science Research machine learning artificial dataset measurement system measurement system analysis Gage Repeatability and Reproducibility electronics manufacturing |
| title | An Approach for the Evaluation of a Measurement System: A Study on the Use of Machine Learning and Predictions |
| title_full | An Approach for the Evaluation of a Measurement System: A Study on the Use of Machine Learning and Predictions |
| title_fullStr | An Approach for the Evaluation of a Measurement System: A Study on the Use of Machine Learning and Predictions |
| title_full_unstemmed | An Approach for the Evaluation of a Measurement System: A Study on the Use of Machine Learning and Predictions |
| title_short | An Approach for the Evaluation of a Measurement System: A Study on the Use of Machine Learning and Predictions |
| title_sort | approach for the evaluation of a measurement system a study on the use of machine learning and predictions |
| topic | machine learning artificial dataset measurement system measurement system analysis Gage Repeatability and Reproducibility electronics manufacturing |
| url | https://etasr.com/index.php/ETASR/article/view/6450 |
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