This work aims at selecting optimal operating variables to obtain the minimum specific energy(SE) in sawing of rocks.A particular granite was sampled and sawn by a fully automated circular diamond sawblades.The periph...This work aims at selecting optimal operating variables to obtain the minimum specific energy(SE) in sawing of rocks.A particular granite was sampled and sawn by a fully automated circular diamond sawblades.The peripheral speed,the traverse speed,the cut depth and the flow rate of cooling fluid were selected as the operating variables.Taguchi approach was adopted as a statistical design of experimental technique for optimization studies.The results were evaluated based on the analysis of variance and signal-to-noise ratio(S/N ratio).Statistically significant operating variables and their percentage contribution to the process were also determined.Additionally,a statistical model was developed to demonstrate the relationship between SE and operating variables using regression analysis and the model was then verified.It was found that the optimal combination of operating variables for minimum SE is the peripheral speed of 25 m/s,the traverse speed of 70 cm/min,the cut depth of 2 cm and the flow rate of cooling fluid of 100 mL/s.The cut depth and traverse speed were statistically determined as the significant operating variables affecting the SE,respectively.Furthermore,the regression model results reveal that the predictive model has a high applicability for practical applications.展开更多
文摘This work aims at selecting optimal operating variables to obtain the minimum specific energy(SE) in sawing of rocks.A particular granite was sampled and sawn by a fully automated circular diamond sawblades.The peripheral speed,the traverse speed,the cut depth and the flow rate of cooling fluid were selected as the operating variables.Taguchi approach was adopted as a statistical design of experimental technique for optimization studies.The results were evaluated based on the analysis of variance and signal-to-noise ratio(S/N ratio).Statistically significant operating variables and their percentage contribution to the process were also determined.Additionally,a statistical model was developed to demonstrate the relationship between SE and operating variables using regression analysis and the model was then verified.It was found that the optimal combination of operating variables for minimum SE is the peripheral speed of 25 m/s,the traverse speed of 70 cm/min,the cut depth of 2 cm and the flow rate of cooling fluid of 100 mL/s.The cut depth and traverse speed were statistically determined as the significant operating variables affecting the SE,respectively.Furthermore,the regression model results reveal that the predictive model has a high applicability for practical applications.
