Integrated Neural Network Analysis of Machining Characteristics in Dry-Turned Al7075/FA0.9SiC0.9 Hybrid Composite Using PCD Inserts
This study presents a comprehensive analysis of the machinability characteristics of an aluminum-based hybrid nanocomposite (Al7075 reinforced with 0.9 wt.% fly ash and 0.9 wt.% SiC) fabricated using an ultrasonically assisted stir-casting technique. Dry turning operations were performed using polyc...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/je/8816146 |
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| author | Sunil Setia Jarnail Singh Sant Ram Chauhan Nikunj Rachchh Raman Kumar Abhijit Bhowmik |
| author_facet | Sunil Setia Jarnail Singh Sant Ram Chauhan Nikunj Rachchh Raman Kumar Abhijit Bhowmik |
| author_sort | Sunil Setia |
| collection | DOAJ |
| description | This study presents a comprehensive analysis of the machinability characteristics of an aluminum-based hybrid nanocomposite (Al7075 reinforced with 0.9 wt.% fly ash and 0.9 wt.% SiC) fabricated using an ultrasonically assisted stir-casting technique. Dry turning operations were performed using polycrystalline diamond (PCD) inserts to evaluate the influence of key input parameters—cutting speed, feed rate, depth of cut, and tool nose radius—on output machining regimes, namely, cutting force components and tool tip temperature. The results revealed that increasing cutting speed reduces the cutting force while elevating the tool tip temperature. Conversely, an increase in feed rate, depth of cut, and nose radius leads to a rise in both force components and temperature. A full factorial artificial neural network (ANN) model was developed to predict these output responses accurately. The ANN model demonstrated high prediction accuracies with errors of 11.50%, 15.88%, and 9.89% for the main cutting force, thrust force, and tool tip temperature, respectively, in the validation set. These findings confirm the model’s reliability in forecasting machining behavior, offering valuable insights for optimizing the dry machining of hybrid composites. |
| format | Article |
| id | doaj-art-28dafc7386914ff89cb00b0ec88944e0 |
| institution | Kabale University |
| issn | 2314-4912 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-28dafc7386914ff89cb00b0ec88944e02025-08-20T03:28:43ZengWileyJournal of Engineering2314-49122025-01-01202510.1155/je/8816146Integrated Neural Network Analysis of Machining Characteristics in Dry-Turned Al7075/FA0.9SiC0.9 Hybrid Composite Using PCD InsertsSunil Setia0Jarnail Singh1Sant Ram Chauhan2Nikunj Rachchh3Raman Kumar4Abhijit Bhowmik5School of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringUniversity School of Mechanical EngineeringDepartment of Additive ManufacturingThis study presents a comprehensive analysis of the machinability characteristics of an aluminum-based hybrid nanocomposite (Al7075 reinforced with 0.9 wt.% fly ash and 0.9 wt.% SiC) fabricated using an ultrasonically assisted stir-casting technique. Dry turning operations were performed using polycrystalline diamond (PCD) inserts to evaluate the influence of key input parameters—cutting speed, feed rate, depth of cut, and tool nose radius—on output machining regimes, namely, cutting force components and tool tip temperature. The results revealed that increasing cutting speed reduces the cutting force while elevating the tool tip temperature. Conversely, an increase in feed rate, depth of cut, and nose radius leads to a rise in both force components and temperature. A full factorial artificial neural network (ANN) model was developed to predict these output responses accurately. The ANN model demonstrated high prediction accuracies with errors of 11.50%, 15.88%, and 9.89% for the main cutting force, thrust force, and tool tip temperature, respectively, in the validation set. These findings confirm the model’s reliability in forecasting machining behavior, offering valuable insights for optimizing the dry machining of hybrid composites.http://dx.doi.org/10.1155/je/8816146 |
| spellingShingle | Sunil Setia Jarnail Singh Sant Ram Chauhan Nikunj Rachchh Raman Kumar Abhijit Bhowmik Integrated Neural Network Analysis of Machining Characteristics in Dry-Turned Al7075/FA0.9SiC0.9 Hybrid Composite Using PCD Inserts Journal of Engineering |
| title | Integrated Neural Network Analysis of Machining Characteristics in Dry-Turned Al7075/FA0.9SiC0.9 Hybrid Composite Using PCD Inserts |
| title_full | Integrated Neural Network Analysis of Machining Characteristics in Dry-Turned Al7075/FA0.9SiC0.9 Hybrid Composite Using PCD Inserts |
| title_fullStr | Integrated Neural Network Analysis of Machining Characteristics in Dry-Turned Al7075/FA0.9SiC0.9 Hybrid Composite Using PCD Inserts |
| title_full_unstemmed | Integrated Neural Network Analysis of Machining Characteristics in Dry-Turned Al7075/FA0.9SiC0.9 Hybrid Composite Using PCD Inserts |
| title_short | Integrated Neural Network Analysis of Machining Characteristics in Dry-Turned Al7075/FA0.9SiC0.9 Hybrid Composite Using PCD Inserts |
| title_sort | integrated neural network analysis of machining characteristics in dry turned al7075 fa0 9sic0 9 hybrid composite using pcd inserts |
| url | http://dx.doi.org/10.1155/je/8816146 |
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