A Simplistic Regression-Based Genetic Algorithm Optimization of Tool-Work Interface Temperature
This work aims to investigate the average tool-work interface temperature for the HSS tool and AISI 1040 steel pair. A tool-work thermocouple is proposed for the measurement of temperature because of its simple construction in addition to the low cost. The machining process of AISI 1040 steel is con...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Fundamental Technological Research
2022-06-01
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| Series: | Engineering Transactions |
| Subjects: | |
| Online Access: | https://et.ippt.pan.pl/index.php/et/article/view/1822 |
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| Summary: | This work aims to investigate the average tool-work interface temperature for the HSS tool and AISI 1040 steel pair. A tool-work thermocouple is proposed for the measurement of temperature because of its simple construction in addition to the low cost. The machining process of AISI 1040 steel is considered due to its extensive application, including industry usage. The changes in cutting temperature are studied for combinations of cutting speed, feed and the depth of cut during turning operation. The orthogonal array L9 by Taguchi is adopted for designing the experiments within a restricted set of runs. The average cutting temperature shows an increasing curve with functions of speed versus depth of cut and speed versus feed. But no clear trend is observed for a combination of feed versus depth of cut. A second-order regression equation with reasonable accuracy (R2 = 0:99) is fitted using the data. Analysis of variance (ANOVA) reveals the highest contribution from cutting speed, which influences average temperature at the interface of tool and work. Further, the genetic algorithm predicts an optimal combination of parameters, which is 82.542 m/min cutting speed, 0.276 mm/rev feed rate and 0.2 mm depth. |
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| ISSN: | 0867-888X 2450-8071 |