Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel
Hard turning has become an attractive method of machining for most manufacturers in the last few years due to its low cost and superior surface quality compared to grinding. In this experimental study, the machinability of hardened steel under dry machining on a CNC lathe is undertaken to optimize t...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2675003 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849305877260009472 |
|---|---|
| author | Dennis Ochengo Li Liang Zhao Wei He Ning |
| author_facet | Dennis Ochengo Li Liang Zhao Wei He Ning |
| author_sort | Dennis Ochengo |
| collection | DOAJ |
| description | Hard turning has become an attractive method of machining for most manufacturers in the last few years due to its low cost and superior surface quality compared to grinding. In this experimental study, the machinability of hardened steel under dry machining on a CNC lathe is undertaken to optimize the cutting parameters for minimum surface roughness and energy consumption with the cutting speed (320, 450, and 575), tool type (coated and uncoated), and feed rate (0.1, 0.18, and 0.26) as the input parameters. The Taguchi method, based on the L18 orthogonal array, the variance analysis, the signal-to-noise ratios, and the response surface methodology have been used to optimize surface roughness (Ra) and cutting power (Cp). Optimum cutting parameters and levels were determined, and the relationship between cutting parameters and output variables was analyzed with the aid of two-dimensional and three-dimensional graphics. The results show that the most effective parameter on the surface roughness was the tool type (78%), while the most effective parameter on energy consumption was the cutting speed (90%). The combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness. Besides, the impact of two-factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appears to be substantial. The linear regression models were validated using confirmation tests. Finally, regression coefficients were determined as a mathematical model, and it was observed that this estimated model yielded results that were very similar to those achieved via real experiment (correlation values: 97.64% for surface roughness and 98.72% for energy consumption). |
| format | Article |
| id | doaj-art-2f84fb471bb94d7985989069cd70fbed |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-2f84fb471bb94d7985989069cd70fbed2025-08-20T03:55:16ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/2675003Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 SteelDennis Ochengo0Li Liang1Zhao Wei2He Ning3College of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringHard turning has become an attractive method of machining for most manufacturers in the last few years due to its low cost and superior surface quality compared to grinding. In this experimental study, the machinability of hardened steel under dry machining on a CNC lathe is undertaken to optimize the cutting parameters for minimum surface roughness and energy consumption with the cutting speed (320, 450, and 575), tool type (coated and uncoated), and feed rate (0.1, 0.18, and 0.26) as the input parameters. The Taguchi method, based on the L18 orthogonal array, the variance analysis, the signal-to-noise ratios, and the response surface methodology have been used to optimize surface roughness (Ra) and cutting power (Cp). Optimum cutting parameters and levels were determined, and the relationship between cutting parameters and output variables was analyzed with the aid of two-dimensional and three-dimensional graphics. The results show that the most effective parameter on the surface roughness was the tool type (78%), while the most effective parameter on energy consumption was the cutting speed (90%). The combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness. Besides, the impact of two-factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appears to be substantial. The linear regression models were validated using confirmation tests. Finally, regression coefficients were determined as a mathematical model, and it was observed that this estimated model yielded results that were very similar to those achieved via real experiment (correlation values: 97.64% for surface roughness and 98.72% for energy consumption).http://dx.doi.org/10.1155/2022/2675003 |
| spellingShingle | Dennis Ochengo Li Liang Zhao Wei He Ning Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel Advances in Materials Science and Engineering |
| title | Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel |
| title_full | Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel |
| title_fullStr | Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel |
| title_full_unstemmed | Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel |
| title_short | Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel |
| title_sort | optimization of surface quality and power consumption in machining hardened aisi 4340 steel |
| url | http://dx.doi.org/10.1155/2022/2675003 |
| work_keys_str_mv | AT dennisochengo optimizationofsurfacequalityandpowerconsumptioninmachininghardenedaisi4340steel AT liliang optimizationofsurfacequalityandpowerconsumptioninmachininghardenedaisi4340steel AT zhaowei optimizationofsurfacequalityandpowerconsumptioninmachininghardenedaisi4340steel AT hening optimizationofsurfacequalityandpowerconsumptioninmachininghardenedaisi4340steel |