Machine learning can improve the use of process capability data to predict tolerances in blanking and piercing manufacturing processes
Machine Learning (ML) models can be used during the design process to simplify and improve the accuracy of the prediction of manufacturing variation using existing process measurement data stored in a Process Capability DataBase (PCDB). Process Capability Data (PCD) relating to the blanking and pier...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023006503 |
| _version_ | 1797385187360768000 |
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| author | Kevin D. Delaney |
| author_facet | Kevin D. Delaney |
| author_sort | Kevin D. Delaney |
| collection | DOAJ |
| description | Machine Learning (ML) models can be used during the design process to simplify and improve the accuracy of the prediction of manufacturing variation using existing process measurement data stored in a Process Capability DataBase (PCDB). Process Capability Data (PCD) relating to the blanking and piercing of metals using progressive stamping dies is used to demonstrate the technique. Predicted variation values are compared with actual measured variation. |
| first_indexed | 2024-03-08T21:50:31Z |
| format | Article |
| id | doaj.art-f1a2302a57a74ed0bc9d01a52f6c8410 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-08T21:50:31Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-f1a2302a57a74ed0bc9d01a52f6c84102023-12-20T07:36:04ZengElsevierResults in Engineering2590-12302023-12-0120101523Machine learning can improve the use of process capability data to predict tolerances in blanking and piercing manufacturing processesKevin D. Delaney0Mechanical Engineering Discipline, School of Mechanical Engineering, Technological University Dublin, Bolton Street, Dublin 1, IrelandMachine Learning (ML) models can be used during the design process to simplify and improve the accuracy of the prediction of manufacturing variation using existing process measurement data stored in a Process Capability DataBase (PCDB). Process Capability Data (PCD) relating to the blanking and piercing of metals using progressive stamping dies is used to demonstrate the technique. Predicted variation values are compared with actual measured variation.http://www.sciencedirect.com/science/article/pii/S2590123023006503Variation managementProcess capability dataPCDBMachine learning |
| spellingShingle | Kevin D. Delaney Machine learning can improve the use of process capability data to predict tolerances in blanking and piercing manufacturing processes Results in Engineering Variation management Process capability data PCDB Machine learning |
| title | Machine learning can improve the use of process capability data to predict tolerances in blanking and piercing manufacturing processes |
| title_full | Machine learning can improve the use of process capability data to predict tolerances in blanking and piercing manufacturing processes |
| title_fullStr | Machine learning can improve the use of process capability data to predict tolerances in blanking and piercing manufacturing processes |
| title_full_unstemmed | Machine learning can improve the use of process capability data to predict tolerances in blanking and piercing manufacturing processes |
| title_short | Machine learning can improve the use of process capability data to predict tolerances in blanking and piercing manufacturing processes |
| title_sort | machine learning can improve the use of process capability data to predict tolerances in blanking and piercing manufacturing processes |
| topic | Variation management Process capability data PCDB Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023006503 |
| work_keys_str_mv | AT kevinddelaney machinelearningcanimprovetheuseofprocesscapabilitydatatopredicttolerancesinblankingandpiercingmanufacturingprocesses |