In the below investigation, the impact of speed, feed, depth of cut, and workpiece hardness on the cutting temperature at tool-workpiece interface on hard-turning of the American Iron and Steel Institute (AISI) H13 tool steel parts will be investigated. It is worth noticing that the inclusion of workpiece hardness as an input variable in discussing cutting temperature wasn’t widely investigated in the literature. Dry cutting experiments were done and the outcomes showed that the cutting temperature is highly influenced by the workpiece hardness. Also, it was noted that though the effect of depth of cut is statistically insignificant, yet it was found that the cutting temperature is an increasing function of the cutting depth. Furthermore, a predictive model for predicting cutting temperature was developed using response surface methodology (RSM) and artificial neural network (ANN) based on the inputs. The mean relative error was employed for testing the adequacy of the created predictive models, and its value was 3.56% and 0.844% for RSM and ANN respectively. Moreover, the new optimization algorithm, cuttlefish algorithm (CFA) was employed for optimizing the cutting temperature and the results were compared with those from the genetic algorithm (GA). The CFA obtained the best results at the least convergence rate. |
