A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm
<p dir="ltr">Monitoring the machining process is crucial for providing cost-effective, high-quality production and preventing unwanted accidents. This study aims to predict critical machining conditions related to surface roughness and tool breakage in titanium alloy slot milling. Th...
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2023
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| _version_ | 1864513535738904576 |
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| author | Amirsajjad Rahmani (17541453) |
| author2 | Faramarz Hojati (17541456) Mohammadjafar Hadad (17541345) Bahman Azarhoushang (10996172) |
| author2_role | author author author |
| author_facet | Amirsajjad Rahmani (17541453) Faramarz Hojati (17541456) Mohammadjafar Hadad (17541345) Bahman Azarhoushang (10996172) |
| author_role | author |
| dc.creator.none.fl_str_mv | Amirsajjad Rahmani (17541453) Faramarz Hojati (17541456) Mohammadjafar Hadad (17541345) Bahman Azarhoushang (10996172) |
| dc.date.none.fl_str_mv | 2023-08-16T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/machines11080835 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Hybrid_Approach_for_Predicting_Critical_Machining_Conditions_in_Titanium_Alloy_Slot_Milling_Using_Feature_Selection_and_Binary_Whale_Optimization_Algorithm/24717210 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Manufacturing engineering Mechanical engineering Information and computing sciences Artificial intelligence tool monitoring milling Ti6Al4V binary whale optimization algorithm feature selection slot milling edge box unbalance dataset |
| dc.title.none.fl_str_mv | A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Monitoring the machining process is crucial for providing cost-effective, high-quality production and preventing unwanted accidents. This study aims to predict critical machining conditions related to surface roughness and tool breakage in titanium alloy slot milling. The Siemens SINUMERIK EDGE (SE) Box system collects signals from the spindle and axes of a CNC machine tool. In this study, features were extracted from signals in time, frequency, and time–frequency domains. The t-test and the binary whale optimization algorithm (BWOA) were applied to choose the best features and train the support vector machine (SVM) model with validation and training data. The SVM hyperparameters were optimized simultaneously with feature selection, and the model was tested with test data. The proposed model accurately predicted critical machining conditions for unbalanced datasets. The classification model indicates an average recall, precision, and accuracy of 80%, 86%, and 95%, respectively, when predicting workpiece quality and tool breakage.</p><h2>Other Information</h2><p dir="ltr">Published in: Machines<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/machines11080835" target="_blank">https://dx.doi.org/10.3390/machines11080835</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_2cd1db8775b0f2c7b440aed7dc510cf7 |
| identifier_str_mv | 10.3390/machines11080835 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717210 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization AlgorithmAmirsajjad Rahmani (17541453)Faramarz Hojati (17541456)Mohammadjafar Hadad (17541345)Bahman Azarhoushang (10996172)EngineeringControl engineering, mechatronics and roboticsManufacturing engineeringMechanical engineeringInformation and computing sciencesArtificial intelligencetool monitoringmillingTi6Al4Vbinary whale optimization algorithmfeature selectionslot millingedge boxunbalance dataset<p dir="ltr">Monitoring the machining process is crucial for providing cost-effective, high-quality production and preventing unwanted accidents. This study aims to predict critical machining conditions related to surface roughness and tool breakage in titanium alloy slot milling. The Siemens SINUMERIK EDGE (SE) Box system collects signals from the spindle and axes of a CNC machine tool. In this study, features were extracted from signals in time, frequency, and time–frequency domains. The t-test and the binary whale optimization algorithm (BWOA) were applied to choose the best features and train the support vector machine (SVM) model with validation and training data. The SVM hyperparameters were optimized simultaneously with feature selection, and the model was tested with test data. The proposed model accurately predicted critical machining conditions for unbalanced datasets. The classification model indicates an average recall, precision, and accuracy of 80%, 86%, and 95%, respectively, when predicting workpiece quality and tool breakage.</p><h2>Other Information</h2><p dir="ltr">Published in: Machines<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/machines11080835" target="_blank">https://dx.doi.org/10.3390/machines11080835</a></p>2023-08-16T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/machines11080835https://figshare.com/articles/journal_contribution/A_Hybrid_Approach_for_Predicting_Critical_Machining_Conditions_in_Titanium_Alloy_Slot_Milling_Using_Feature_Selection_and_Binary_Whale_Optimization_Algorithm/24717210CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247172102023-08-16T06:00:00Z |
| spellingShingle | A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm Amirsajjad Rahmani (17541453) Engineering Control engineering, mechatronics and robotics Manufacturing engineering Mechanical engineering Information and computing sciences Artificial intelligence tool monitoring milling Ti6Al4V binary whale optimization algorithm feature selection slot milling edge box unbalance dataset |
| status_str | publishedVersion |
| title | A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm |
| title_full | A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm |
| title_fullStr | A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm |
| title_full_unstemmed | A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm |
| title_short | A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm |
| title_sort | A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm |
| topic | Engineering Control engineering, mechatronics and robotics Manufacturing engineering Mechanical engineering Information and computing sciences Artificial intelligence tool monitoring milling Ti6Al4V binary whale optimization algorithm feature selection slot milling edge box unbalance dataset |