Optimization with artificial intelligence of the machinability of Hardox steel, which is exposed to different processes

In this study, different process types were processed on Hardox 400 steel. These processes were carried out with five different samples as heat treatment, cold forging, plasma welding, mig-mag welding and commercial sample. The aim here is to determine the changes in properties such as microstructure, microhardness and conductivity that occur in the structure of hardox 400 steel when exposed to different processes. Then, the samples affected by these changes were processed in WEDM with the box-behnken experimental design. Ra, Kerf, MRR and WWR results were analyzed in Minitab 21 program. In the continuation of the study, using these data, a prediction models were created for Ra, Kerf, MRR and WWR with Deep Learning (DL) and Extreme Learning Machine (ELM). Anaconda program Python 3.9 version was used as a program in the optimization study. In addition, a linear regression models are presented to comparison the results. According to the results the lowest Ra values were obtained in heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. The best Ra (surface roughness) value of 1.92 µm was obtained in the heat treated sample and in the experiment with a time off of 250 µs. Model F value in ANOVA analysis for Ra is 86.04. Model for Ra r(2) value was obtained as 0.9534. The lowest kerf values were obtained in heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. The best kerf value of 200 µ was obtained in the heat treated sample and in the experiment with a time off of 200 µs. Model F value in ANOVA analysis for Kerf is 90.21. Model for Kerf r(2) value was obtained as 0.9555. Contrary to Ra and Kerf, it is desirable to have high MRR values. On average, the highest MRR values were obtained in mig-mag welded, plasma welded, cold forged, master sample and heat-treated processes, respectively. The best mrr value of 200 g min(−1) was obtained in the mig-mag welded sample and in the experiment with a time off of 300 µs. Model for MRR r(2) value was obtained as 0.9563. The lowest WWR values were obtained in heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. The best wwr value of 0.098 g was obtained in the heat treated sample and in the experiment with a time off of 200 µs. Model F value in ANOVA analysis for WWR is 92.12. Model for wwr r(2) value was obtained as 0.09561. In the analysis made with artificial intelligence systems; The best test MSE value for Ra was obtained as 0.012 in DL and the r squared value 0.9274. The best test MSE value for kerf was obtained as 248.28 in ELM and r squared value 0.8676. The best MSE value for MRR was obtained as 0.000101 in DL and the r squared value 0.9444. The best MSE value for WWR was obtained as 0.000037 in DL and the r squared value 0.9184. As a result, it was concluded that different optimization methods can be applied according to different outputs (Ra, Kerf, MRR, WWR). It also shows that artificial intelligence-based optimization methods give successful estimation results about Ra, Kerf, MRR, WWR values. According to these results, ideal DL and ELM models have been presented for future studies.