Abstract:In this study, microstructure, mechanical, and conductivity characteristics of Hardox 400 steel were changed with different heat treatments and effect of such characteristics on its machinability with Wire Electrical Discharge Machining (WEDM). Optical microscope examinations were performed to determine various characteristics, and additionally microhardness and conductivity measurements were conducted hereof. L18 Taquchi test design was conducted with three levels and four different parameters to determine th… Show more
“…When the effects of the samples on the kerf were examined according to the process type, the lowest kerf values were obtained in the heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. There are similar results in some studies in literature 2 , 47 – 51 . Figure 10 Main effect plot for kerf.…”
Section: Resultssupporting
confidence: 90%
“…After the heat treatment, the α-ferrite phase volume increased in the sample, while the pearlite phase volume decreased 2 . Observable decreases were detected in Ra and kerf values due to the relative decrease in hardness.…”
Section: Resultsmentioning
confidence: 98%
“…These hardox steels are produced thoroughly hardened and presented as such 2 . Similar results were also encountered in literatüre 2 , 46 . Figure 6 Microhardness result.…”
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 r2 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 r2 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 r2 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 r2 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.
“…When the effects of the samples on the kerf were examined according to the process type, the lowest kerf values were obtained in the heat-treated, cold forged, master sample, plasma welded and mig-mag welded processes, respectively. There are similar results in some studies in literature 2 , 47 – 51 . Figure 10 Main effect plot for kerf.…”
Section: Resultssupporting
confidence: 90%
“…After the heat treatment, the α-ferrite phase volume increased in the sample, while the pearlite phase volume decreased 2 . Observable decreases were detected in Ra and kerf values due to the relative decrease in hardness.…”
Section: Resultsmentioning
confidence: 98%
“…These hardox steels are produced thoroughly hardened and presented as such 2 . Similar results were also encountered in literatüre 2 , 46 . Figure 6 Microhardness result.…”
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 r2 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 r2 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 r2 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 r2 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.
“…The lowest values for almost all evaluated parameters (except for the parameter Sz) were obtained for Sample 30, which was machined with the machine parameters setup: U = 70 V, T on = 6 µs, T off = 30 µs, h = 14 m·min −1 , and I = 35 A. This sample had a Ra value of 2.08 µm, which is significantly lower than that of Atlug research [21], but with different heat treatments for this steel. In contrast, the worst surface quality was evaluated for Sample 25, where all parameters except for parameter Sz reached the highest values.…”
Section: Analysis Of the Machined Surface And Discussionmentioning
The unconventional technology wire electrical discharge machining is a highly used technology for producing precise and indented shaped parts of all materials that are at least electrically conductive. Its wide use makes this technology applicable in almost all branches of industry, even in the automotive industry, where the abrasion resistant material under investigation Hardox 400 steel is widely used for the manufacturing of truck bodies. The aim of this study was a comprehensive analysis of the machinability of this material using WEDM employing a 33−round experiment. Based on the change in machine parameters (pulse off time, gap voltage, discharge current, pulse on time, and wire feed), the cutting speed, the topography of machined surfaces, and the chemical composition of the workpiece surface, the morphology and condition of the subsurface layer including lamella production and a subsequent determination of the distribution of individual elements in the given area were analyzed. It has been found that during the machining of this steel, many defects occur in the subsurface layer of the material in the form of cracks with a depth of up to 22 µm and burned cavities. However, by appropriately adjusting the machine parameters, it was possible to completely remove these cracks.
“…Nevertheless, it is confirmed by research works that practically they cannot be joined by welding. The most frequently observed problems with the weldability of the Hardox 500 are its susceptibility to the brittle cracking of the welded joints and the wide zones of lower hardness in comparison to the base material [49].…”
This paper deals with the effects of three low-carbon steel filler metals consisting of ferritic and austenitic phases on the weld joints of the tungsten inert gas (TIG) welding of Hardox 500 steel. The correlation between the microstructure and mechanical properties of the weld joints was investigated. For this purpose, macro and microstructure were examined, and then microhardness, tensile, impact, and fracture toughness tests were carried out to analyze the mechanical properties of joints. The results of optical microscopy (OM) images showed that the weld zones (WZ) of all three welds were composed of different ferritic morphologies, including allotriomorphic ferrite, Widmanstätten ferrite, and acicular ferrite, whereas the morphology of the heat-affected zone (HAZ) showed the various microstructures containing mostly ferrite and pearlite phases. Further, based on mechanical tests, the second filler with ferritic microstructure represented better elongation, yield strength, ultimate tensile strength, impact toughness, and fracture toughness due to having a higher amount of acicular ferrite phase compared to the weld joints concerning the other fillers consisting of austenitic and ferritic-austenitic. However, scanning electron microscopy (SEM) images on the fracture surfaces of the tensile test showed a ductile-type fracture with a large number of deep and shallow voids while on the fracture surfaces resulting from the Charpy impact tests and both ductile and cleavage modes of fracture took place, indicating the initiation and propagation of cracks, respectively. The presence of acicular ferrite as a soft phase that impedes the dislocation pile-up brings about the ductile mode of fracture while inclusions may cause stress concentration, thus producing cleavage surfaces.
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