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Öğe Application of six sigma through deep learning in the production of fasteners(Emerald Group Publishing Ltd, 2023) Altug, MehmetPurposeThe purpose of this study was conducted at an enterprise that produces fasteners and is one of the leading companies in the sector in terms of market share. Possible defects in the coating of bolts and nuts either lead to products being scrapped or all of the coating process being repeated from beginning to end. In both cases, the enterprise faces a waste of time and excessive costs. Through this project, the six sigma theory and its means were effectively used to improve the efficiency and quality management of the company. The selection of the six sigma project has also contributed to the creation of various documents to be used for project screening and evaluation of financial results. Design/methodology/approachSix sigma is an optimization strategy that is used to improve the profitability of businesses, avoid waste, scrap and losses, reduce costs and improve the effectiveness of all activities to meet or exceed customers' needs and expectations. Six sigma's process improvement model, known as Definition-Measurement-Analysis-Improvement-Control, contributes to the economic and technical achievements of businesses. The normal distribution of a process should be within +/- 3 sigma of the mean. This represents a scale of 99.7% certainty. However, improving the process through the utilization of the six sigma rule, which accepts normal variabilities of processes twice as strict, will result in an error rate of 3.4 per million instead of 2,700 per million for each product or service. FindingsUsing six sigma practices to reduce the costs associated with low quality and to increase economic added value became a cultural practice. With this, the continuation of six sigma practices throughout the Company was intended. The annual cost reduction achieved with the utilization of six sigma practices can be up to $21,780. When time savings are also considered, a loss reduction of about $30,000 each year can be achieved. The coating thickness efficiency increased from 85% to 95% after the improvements made through the six sigma project. There is a significant increase in the efficiency of coating thickness. In addition, the coating thickness efficiency is also close to the target value of 95%-97%. Originality/valueThe results of the study were optimized with the help of deep learning. The performance of the model created in deep learning was quite close to the actual performance. This result implicates the validity of the improvement work. The results may act as a guide for the use of deep learning in new projects.Öğe Experimental investigation of kerf of Ti6Al4V exposed to different heat treatment processes in WEDM and optimization of parameters using genetic algorithm(Springer London Ltd, 2015) Altug, Mehmet; Erdem, Mehmet; Ozay, CetinIn this study, microstructure, mechanical, and conductivity characteristics of alloy Ti6Al4V were changed with different heat treatments and the effect of such characteristics on its machinability with wire electrical discharge machining (WEDM). Optical microscope, scanning electron microscope (SEM), and X-ray diffraction (XRD) examinations were performed to determine various characteristics, and additionally microhardness and conductivity measurements were conducted hereof. L18 Taguchi test design was conducted with three levels and six different parameters to determine the effect of such alterations on its machinability with WEDM, and post-processing kerf (cutting width) values were determined. Microchanges were ensured successfully by using applied heat treatments. Results obtained with the optimization technique of genetic algorithm gave minimum kerf. Values obtained by using response surface method along with this equation completely match with those achieved in the experiments. The best kerf value was obtained from sample E which was quenched from dual phase area. The microstructure of this sample was composed of primary alpha and alpha' phases.Öğe Investigation of material removal rate (MRR) and wire wear ratio (WWR) for alloy Ti6Al4 V exposed to heat treatment processing in WEDM and optimization of parameters using Grey relational analysis(Carl Hanser Verlag, 2016) Altug, MehmetThe study examines the changes of the microstructural, mechanical and conductivity characteristics of the titanium alloy Ti6Al4 V as a result of heat treatment using wire electrical discharge machining, and their effect on machinability. By means of optical microscopy and scanning electron microscopy (SEM), analyses have been performed to determine various characteristics and additionally, microhardness and conductivity measurements have been conducted. Material removal rate (MRR) and wire wear ratio (WWR) values have been determined by using L-18 Taguchi test design. The microstructures of the samples have been changed by thermal procedures. Results have been obtained by using the Grey relational analysis (GRA) optimization technique to solve the maximum MRR and minimum WWR values. The best (highest) MRR value is obtained from sample E which was water quenched in dual phase processing. The microstructure of this sample is composed of primary a and alpha' phases. The best (lowest) WWR value is obtained from sample A.Öğe Investigation of mechanical, microstructural, and machining properties of AISI 420 martensitic stainless steel welded by laser welding(Springer London Ltd, 2016) Erdem, Mehmet; Altug, Mehmet; Karabulut, MustafaIn this study, AISI 420 stainless steel sheets of 1.8 mm in thickness were joined by laser welding. To determine the mechanical and microstructural properties of these sheets, microhardness, tensile, optical microscope, and SEM and XRD examinations were made. The following results were obtained: the carbides dissolved in austenite near the weld metal of heat affected zone. The increase in laser pulse frequency was more effective in determining the penetration and width of the weld metal than current and focal diameter. The highest yield and tensile strength values were 269 and 857.34 MPa, respectively, at the welded sheet joined with no.7 parameter. The grain size and microstrain values of untempered welded sample were 229.322 nm and 0.00347, respectively, at the welded sheet joined with no.6 parameter. The narrowest kerf and the lowest microhardness were 161 mu and 293 HV, successively, and these were obtained in the weld metal of sample number 5.Öğe Investigation of Melt Flow Index Properties of Polypropylene Reinforced Aluminum Powder(Gazi Univ, 2017) Guldas, Abdulmecit; Temel, Servet; Altug, MehmetIn this study, PP-based composite reinforced with three pressures, three temperatures, three reinforcement rates, and three reinforcement sizes were produced and their Melt Flow Index (MFI) properties were investigated. Moreover, 0.2% maleic anhydride and fenolic based antioksidant in order to prevent oxidation were also added during addition of aluminum powders. According to the results of the study, MFI values degreases with increasing viscosity. On the contrary, MFI values increases with increasing shear rate and shear stress. In addition, values of 1379 kPa, 250 C, 5%, 210-300 mu m and 44-100 mu m were determined as pressure, temperature, reinforcement ratio and particle size respectively for the samples having high MFI values.Öğe Mechanical properties of aluminum powder reinforced polypropylene(Carl Hanser Verlag, 2017) Guldas, Abdulmecit; Altug, Mehmet; Temel, ServetIn this study, PP-based composites reinforced were produced by using three injection pressures IP (50, 55, 60 MPa), three injection temperatures IT (210, 220, 230 degrees C), three holding pressures HP (35, 40, 45 MPa), three reinforcement rates Rr (5, 10, 15 wt.-%) and three reinforcement sizes Rs (44-100, 101-210, 210-300 mu m) and their mechanical properties were investigated. Moreover, 0.2 % maleic anhydride was also added in order to prevent oxidation during addition of aluminum powders. The best results in terms of tensile strength were obtained from the specimen with the code C2, with the parameter values R-s = 100 -210 mu m, R-r = 15 wt.-%, I-T = 230 degrees C, I-P = 50 MPa and H-P = 40 MPa. The best results in the tensile tests were received from the specimen with code A2, having the parameter values R-s = 100-210 mu m, R-r = 5 wt.-%, I-T = 210 degrees C, I-P = 50 MPa and H-P = 35 MPa. On the other hand, the best results in the izod tests were obtained for the specimen with the code C1 with the parameter values R-s = 44-100 mu m, R-r = 15 wt.-%, I-T = 210 degrees C, I-P = 60 MPa and HP = 45 MPa.Öğe A New Optimization Technique in Examining the Machinability of Sverker 21 Steel: Gray Relational Analysis-Based Genetic Algorithm(Springer Heidelberg, 2021) Ozay, Cetin; Altug, Mehmet; Ballikaya, HasanIn this study, to determine the optimum parameters of the WEDM method, Taguchi experiment design, gray relational analysis (GRA) and gray relational analysis-based genetic algorithm (GRABGA) methods were used. In experimental studies, first, microstructure, microhardness and conductivity examinations of, both commercial and heat treated, Sverker 21 cold tool steel were performed. Then, the commercial and heat-treated Sverker 21 cold tool steel was processed with the WEDM method at the determined parameters. The effects of processing parameters on average surface roughness (Ra), kerf, and vibration were evaluated separately using Taguchi test design, GRA and GRABGA methods. It was determined that the homogeneous distribution of the carbides increased, the microhardness increased and the conductivity increased in the microstructure of the heat-treated sample compared to the commercial sample. In the processing of materials with WEDM, while the Taguchi test design method was used to evaluate the effects of processing parameters on the results were evaluated separately, GRA method was used to determine the grade of relationship between the results. In addition, the GRABGA method enabled the optimum values of the processing parameters to be determined not only on the basis of the level, but also on the intermediate values. It was found that the GRA relationship value was 0.8564 whereas the GRABGA relationship value was 0.8977. It was concluded that the GRABGA method provided better results than the GRA method in the correlation analysis of the results obtained in processing D2 cold tool steel with the WEMD method.Öğe Optimization with artificial intelligence of the machinability of Hardox steel, which is exposed to different processes(Nature Portfolio, 2023) Altug, Mehmet; Soyler, HasanIn 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 mu m was obtained in the heat treated sample and in the experiment with a time off of 250 mu s. 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 mu was obtained in the heat treated sample and in the experiment with a time off of 200 mu s. 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 gmin- 1 was obtained in the mig-mag welded sample and in the experiment with a time off of 300 mu s. 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 mu s. 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 intelligencebased 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.Öğe Surface roughness of Ti6Al4V after heat treatment evaluated by artificial neural networks(Carl Hanser Verlag, 2016) Altug, Mehmet; Erdem, Mehmet; Ozay, Cetin; Bozkir, OguzThe study examines how, using wire electrical discharge machining (WEDM), the microstructural, mechanical and conductivity characteristics of the titanium alloy Ti6Al4V are changed as a result of heat treatment and the effect they have on machinability. Scanning electron microscope (SEM), optical microscope and X-ray diffraction (XRD) examinations were performed to determine various characteristics and additionally related microhardness and conductivity measurements were conducted. L-18 Taquchi test design was performed with three levels and six different parameters to determine the effect of such alterations on its machinability using WEDM and post-processing surface roughness (Ra) values were determined. Micro-changes were ensured successfully by using heat treatments. Results obtained with the optimization technique of artificial neural network (ANN) presented minimum surface roughness. Values obtained by using response surface method along with this equation were completely comparable with those achieved in the experiments. The best surface roughness value was obtained from sample D which had a tempered martensite structure.
