A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel
This paper presents an experimental investigation on cutting temperature during hard turning of EN 24 steel (50 HRC) using TiN coated carbide insert under dry environment. The prediction model is developed using response surface methodology and optimization of process parameter is performed by desir...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2014-06-01
|
| Series: | International Journal of Industrial Engineering Computations |
| Subjects: | |
| Online Access: | http://www.growingscience.com/ijiec/Vol5/IJIEC_2014_11.pdf |
| _version_ | 1811317555861651456 |
|---|---|
| author | Ashok Kumar Sahoo Purna Chandra Mishra |
| author_facet | Ashok Kumar Sahoo Purna Chandra Mishra |
| author_sort | Ashok Kumar Sahoo |
| collection | DOAJ |
| description | This paper presents an experimental investigation on cutting temperature during hard turning of EN 24 steel (50 HRC) using TiN coated carbide insert under dry environment. The prediction model is developed using response surface methodology and optimization of process parameter is performed by desirability approach. A stiff rise in cutting temperature is noticed when feed and cutting speed are elevated. The effect of depth of cut on cutting temperature is not that much significant compared with cutting speed and feed as observed from main effects plot. The response surface second order model presented high correlation coefficient (R2 = 0.992) explaining 99.2 % of the variability in the cutting temperature which indicates the goodness of fit for the model to the actual data and high statistical significance of the model. The experimental and predicted values are very close to each other. The calculated error for cutting temperature lies between 1.88-3.19 % during confirmation trial. Therefore, the developed second order model correlates the relationship of the cutting temperature with the process parameters with good degree of approximation. The optimal combination for process parameter is depth of cut at 0.2mm, feed of 0.1597 mm/rev and cutting speed of 70m/min. Based on these combination, the value of cutting temperature is 302.950C whose desirability is one. |
| first_indexed | 2024-04-13T12:10:18Z |
| format | Article |
| id | doaj.art-34c92ebd4c934569946d2441a0142eda |
| institution | Directory Open Access Journal |
| issn | 1923-2926 1923-2934 |
| language | English |
| last_indexed | 2024-04-13T12:10:18Z |
| publishDate | 2014-06-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Industrial Engineering Computations |
| spelling | doaj.art-34c92ebd4c934569946d2441a0142eda2022-12-22T02:47:30ZengGrowing ScienceInternational Journal of Industrial Engineering Computations1923-29261923-29342014-06-015340741610.5267/j.ijiec.2014.4.002A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steelAshok Kumar SahooPurna Chandra Mishra This paper presents an experimental investigation on cutting temperature during hard turning of EN 24 steel (50 HRC) using TiN coated carbide insert under dry environment. The prediction model is developed using response surface methodology and optimization of process parameter is performed by desirability approach. A stiff rise in cutting temperature is noticed when feed and cutting speed are elevated. The effect of depth of cut on cutting temperature is not that much significant compared with cutting speed and feed as observed from main effects plot. The response surface second order model presented high correlation coefficient (R2 = 0.992) explaining 99.2 % of the variability in the cutting temperature which indicates the goodness of fit for the model to the actual data and high statistical significance of the model. The experimental and predicted values are very close to each other. The calculated error for cutting temperature lies between 1.88-3.19 % during confirmation trial. Therefore, the developed second order model correlates the relationship of the cutting temperature with the process parameters with good degree of approximation. The optimal combination for process parameter is depth of cut at 0.2mm, feed of 0.1597 mm/rev and cutting speed of 70m/min. Based on these combination, the value of cutting temperature is 302.950C whose desirability is one.http://www.growingscience.com/ijiec/Vol5/IJIEC_2014_11.pdfCutting temperatureHard turningCoated carbideResponse surface methodologyDesirability approach |
| spellingShingle | Ashok Kumar Sahoo Purna Chandra Mishra A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel International Journal of Industrial Engineering Computations Cutting temperature Hard turning Coated carbide Response surface methodology Desirability approach |
| title | A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel |
| title_full | A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel |
| title_fullStr | A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel |
| title_full_unstemmed | A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel |
| title_short | A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel |
| title_sort | response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel |
| topic | Cutting temperature Hard turning Coated carbide Response surface methodology Desirability approach |
| url | http://www.growingscience.com/ijiec/Vol5/IJIEC_2014_11.pdf |
| work_keys_str_mv | AT ashokkumarsahoo aresponsesurfacemethodologyanddesirabilityapproachforpredictivemodelingandoptimizationofcuttingtemperatureinmachininghardenedsteel AT purnachandramishra aresponsesurfacemethodologyanddesirabilityapproachforpredictivemodelingandoptimizationofcuttingtemperatureinmachininghardenedsteel AT ashokkumarsahoo responsesurfacemethodologyanddesirabilityapproachforpredictivemodelingandoptimizationofcuttingtemperatureinmachininghardenedsteel AT purnachandramishra responsesurfacemethodologyanddesirabilityapproachforpredictivemodelingandoptimizationofcuttingtemperatureinmachininghardenedsteel |