Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning
The time-domain dynamic process model is used to generate data and guides the stability criteria for machine learning, saving the experimental costs for a number of required data for the metal process. Fourier transformation of vibration data simulated using a dynamic process model generates the fea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/12/2/298 |
| _version_ | 1797478005830844416 |
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| author | Jeong Hoon Ko |
| author_facet | Jeong Hoon Ko |
| author_sort | Jeong Hoon Ko |
| collection | DOAJ |
| description | The time-domain dynamic process model is used to generate data and guides the stability criteria for machine learning, saving the experimental costs for a number of required data for the metal process. Fourier transformation of vibration data simulated using a dynamic process model generates the feature lists including multiple frequencies and amplitudes at each process condition. The feature lists for milling stability are analyzed for training the machine learning algorithm. The amplitude and frequency distributions may change according to the dynamic pattern of the machining stability. The vibration patterns are grouped into stable, chatter, and boundary conditions by performing data training using support vector machines and gradient tree boosting. In the high-speed milling of Al6061-T6 with 6000 to 18,000 RPM and variations of axial and radial depths of cuts, 2400 data sets of the time domain data were trained and tested. Actual experimental tests are carried out for new process conditions with the range of 9890 to 28,470 RPM and 989 to 2847 mm/min. The experimental stability outcomes are compared with predictions from the algorithms. Stability is accurately predicted over new conditions with around 0.9 prediction accuracy, which means the methodology can be used to predict, categorize, and monitor stability in end milling processes. |
| first_indexed | 2024-03-09T21:26:47Z |
| format | Article |
| id | doaj.art-a063bfbaa27b4358a2a5884e3cc2a330 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T21:26:47Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-a063bfbaa27b4358a2a5884e3cc2a3302023-11-23T21:08:09ZengMDPI AGMetals2075-47012022-02-0112229810.3390/met12020298Machining Stability Categorization and Prediction Using Process Model Guided Machine LearningJeong Hoon Ko0Singapore Institute of Manufacturing Technology, Singapore 138634, SingaporeThe time-domain dynamic process model is used to generate data and guides the stability criteria for machine learning, saving the experimental costs for a number of required data for the metal process. Fourier transformation of vibration data simulated using a dynamic process model generates the feature lists including multiple frequencies and amplitudes at each process condition. The feature lists for milling stability are analyzed for training the machine learning algorithm. The amplitude and frequency distributions may change according to the dynamic pattern of the machining stability. The vibration patterns are grouped into stable, chatter, and boundary conditions by performing data training using support vector machines and gradient tree boosting. In the high-speed milling of Al6061-T6 with 6000 to 18,000 RPM and variations of axial and radial depths of cuts, 2400 data sets of the time domain data were trained and tested. Actual experimental tests are carried out for new process conditions with the range of 9890 to 28,470 RPM and 989 to 2847 mm/min. The experimental stability outcomes are compared with predictions from the algorithms. Stability is accurately predicted over new conditions with around 0.9 prediction accuracy, which means the methodology can be used to predict, categorize, and monitor stability in end milling processes.https://www.mdpi.com/2075-4701/12/2/298stability pattern recognitionmachine learningdynamic modelingchatter |
| spellingShingle | Jeong Hoon Ko Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning Metals stability pattern recognition machine learning dynamic modeling chatter |
| title | Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning |
| title_full | Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning |
| title_fullStr | Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning |
| title_full_unstemmed | Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning |
| title_short | Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning |
| title_sort | machining stability categorization and prediction using process model guided machine learning |
| topic | stability pattern recognition machine learning dynamic modeling chatter |
| url | https://www.mdpi.com/2075-4701/12/2/298 |
| work_keys_str_mv | AT jeonghoonko machiningstabilitycategorizationandpredictionusingprocessmodelguidedmachinelearning |