Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy
Surface integrity has a very significant effect on surface roughness and surface microhardness. These are the main characteristics of surface integrity. The present study investigated the influence of the cutting depth (ap), the cutting speed (vc), and the feed rate (f) on the surface roughness (Ra)...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/2979858 |
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| author | Zhibo Deng Zhe Wang Xuehong Shen |
| author_facet | Zhibo Deng Zhe Wang Xuehong Shen |
| author_sort | Zhibo Deng |
| collection | DOAJ |
| description | Surface integrity has a very significant effect on surface roughness and surface microhardness. These are the main characteristics of surface integrity. The present study investigated the influence of the cutting depth (ap), the cutting speed (vc), and the feed rate (f) on the surface roughness (Ra) and surface microhardness (HV) in turning TC17 titanium alloy. Data obtained from the Box-Behnken design experiments were used to develop the response surface methodology (RSM) and artificial neural network (ANN) models. Through analysis of variance (ANOVA), the relative effects of each cutting parameter on the responses have been determined. To examine the interaction effects of cutting parameters, 3D surface plots were generated. The desirability function approach (DFA) was used to optimize cutting parameters to achieve the lowest surface roughness and highest surface microhardness. The results show that ANN response prediction models have higher prediction accuracy and lower error than RSM prediction models. The optimization parameters are 60 m/min cutting speed, 0.06 mm/r feed rate, and 0.2 mm cutting depth for the minimum surface roughness and maximum surface microhardness with a maximum error of 2.83%. |
| format | Article |
| id | doaj-art-fcfcf929714a4024b4ba04270c3ad5d5 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-fcfcf929714a4024b4ba04270c3ad5d52025-02-03T06:13:34ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/2979858Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium AlloyZhibo Deng0Zhe Wang1Xuehong Shen2Party and Government OfficeAviation Manufacturing Engineering SchoolSchool of Mechanical EngineeringSurface integrity has a very significant effect on surface roughness and surface microhardness. These are the main characteristics of surface integrity. The present study investigated the influence of the cutting depth (ap), the cutting speed (vc), and the feed rate (f) on the surface roughness (Ra) and surface microhardness (HV) in turning TC17 titanium alloy. Data obtained from the Box-Behnken design experiments were used to develop the response surface methodology (RSM) and artificial neural network (ANN) models. Through analysis of variance (ANOVA), the relative effects of each cutting parameter on the responses have been determined. To examine the interaction effects of cutting parameters, 3D surface plots were generated. The desirability function approach (DFA) was used to optimize cutting parameters to achieve the lowest surface roughness and highest surface microhardness. The results show that ANN response prediction models have higher prediction accuracy and lower error than RSM prediction models. The optimization parameters are 60 m/min cutting speed, 0.06 mm/r feed rate, and 0.2 mm cutting depth for the minimum surface roughness and maximum surface microhardness with a maximum error of 2.83%.http://dx.doi.org/10.1155/2022/2979858 |
| spellingShingle | Zhibo Deng Zhe Wang Xuehong Shen Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy International Transactions on Electrical Energy Systems |
| title | Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy |
| title_full | Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy |
| title_fullStr | Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy |
| title_full_unstemmed | Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy |
| title_short | Surface Feature Prediction Modeling and Parameter Optimization for Turning TC17 Titanium Alloy |
| title_sort | surface feature prediction modeling and parameter optimization for turning tc17 titanium alloy |
| url | http://dx.doi.org/10.1155/2022/2979858 |
| work_keys_str_mv | AT zhibodeng surfacefeaturepredictionmodelingandparameteroptimizationforturningtc17titaniumalloy AT zhewang surfacefeaturepredictionmodelingandparameteroptimizationforturningtc17titaniumalloy AT xuehongshen surfacefeaturepredictionmodelingandparameteroptimizationforturningtc17titaniumalloy |