Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear
The high-performance alloy, 304 stainless steel, is widely used in various industries. However, its material properties lead to severe tool wear during milling processes, significantly increasing milling force and adversely impacting machining quality and efficiency. Consequently, an accurate millin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/72 |
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| author | Changxu Wang Yan Li Feng Gao Kejun Wu Kan Yin Peng He Yunjiao Xu |
| author_facet | Changxu Wang Yan Li Feng Gao Kejun Wu Kan Yin Peng He Yunjiao Xu |
| author_sort | Changxu Wang |
| collection | DOAJ |
| description | The high-performance alloy, 304 stainless steel, is widely used in various industries. However, its material properties lead to severe tool wear during milling processes, significantly increasing milling force and adversely impacting machining quality and efficiency. Consequently, an accurate milling-force model is crucial for guiding the formulation and optimization of machining parameters. This paper presents a milling-force prediction model for 304 stainless steel that incorporates the effect of tool wear, based on the mechanistic modeling approach. Side-milling experiments on 304 stainless steel were conducted to analyze the relationship between milling force and tool wear, identify the model coefficients, and validate the prediction accuracy of the milling-force model. The results demonstrate that the model accurately predicts the milling forces of worn tools while side milling 304 stainless steel under various machining parameters and tool wear conditions. |
| format | Article |
| id | doaj-art-0335c7e5a2f24378af8d504f1b71d2b2 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-0335c7e5a2f24378af8d504f1b71d2b22025-01-24T13:39:22ZengMDPI AGMachines2075-17022025-01-011317210.3390/machines13010072Milling-Force Prediction Model for 304 Stainless Steel Considering Tool WearChangxu Wang0Yan Li1Feng Gao2Kejun Wu3Kan Yin4Peng He5Yunjiao Xu6School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Machinery and Automation, Weifang University, Weifang 262799, ChinaThe high-performance alloy, 304 stainless steel, is widely used in various industries. However, its material properties lead to severe tool wear during milling processes, significantly increasing milling force and adversely impacting machining quality and efficiency. Consequently, an accurate milling-force model is crucial for guiding the formulation and optimization of machining parameters. This paper presents a milling-force prediction model for 304 stainless steel that incorporates the effect of tool wear, based on the mechanistic modeling approach. Side-milling experiments on 304 stainless steel were conducted to analyze the relationship between milling force and tool wear, identify the model coefficients, and validate the prediction accuracy of the milling-force model. The results demonstrate that the model accurately predicts the milling forces of worn tools while side milling 304 stainless steel under various machining parameters and tool wear conditions.https://www.mdpi.com/2075-1702/13/1/72304 stainless steeltool wearmodel coefficient identificationmilling-force prediction |
| spellingShingle | Changxu Wang Yan Li Feng Gao Kejun Wu Kan Yin Peng He Yunjiao Xu Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear Machines 304 stainless steel tool wear model coefficient identification milling-force prediction |
| title | Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear |
| title_full | Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear |
| title_fullStr | Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear |
| title_full_unstemmed | Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear |
| title_short | Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear |
| title_sort | milling force prediction model for 304 stainless steel considering tool wear |
| topic | 304 stainless steel tool wear model coefficient identification milling-force prediction |
| url | https://www.mdpi.com/2075-1702/13/1/72 |
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