Onur Altuntaş

This study investigates the high strain rate behavior of 100Cr6 bearing steel bullet core material following austempering (300 °C for 1 or 5 h) and subsequent sub-zero cryogenic processing ( -80 °C for 1 h). Microstructural features, phase transformations, and crystallographic parameters were evaluated via SEM and XRD, while mechanical properties were quantified through HV1 microhardness and Split Hopkinson Pressure Bar testing. Cryogenic treatment after prolonged austempering systematically enhanced microhardness and dynamic yield strength while reducing strain capability. The maximum microhardness reached 780 HV1 in the 5-h austempered and cryogenically treated specimen, representing a ∼250% increase over the initial state. XRD analysis revealed maximum pre- and post-impact dislocation densities of 77.86 x 10 ¹⁴ lines/m ² (1-h austempered + cryogenic) and 154.06 x 10 ¹⁴ lines/m ² (1-h austempered), respectively. Overall, post-austempering cryogenic processing effectively optimizes the dynamic mechanical response of 100Cr6 steel.

Lightweight ceramic-based armors are increasingly adopted in defense applications due to their superior hardness, low density, and effectiveness against ballistic threats. Evaluating their protective capability commonly relies on the measurement of depth of penetration (DOP) in a backing material after impact. While traditional analytical models and ballistic testing have provided important insights, they are constrained by high experimental costs and the inability to fully capture the nonlinear fracture mechanisms of ceramics. Recent progress in machine learning (ML) enables accurate prediction of ballistic responses by identifying complex relationships between input parameters and DOP outcomes. This study focuses on predicting residual DOP for three projectile types—7.62x54R B32, 12.7x99 mm AP, and 14.5x114 mm B32 API—after impact on ceramic targets backed by materials with varied density and hardness. The performance of different ML algorithms, including Random Forest Regressor, Decision Tree Regressor, and Artificial Neural Networks, is compared. The results showed that ANN and RF showed better performance than DT regressor with R2 values of 0.94 and 0.92 respectively. Results also highlight the potential of ML approaches to improve prediction accuracy, reduce testing requirements, and accelerate the design of more efficient protective systems.

In this study, AISI O2 tool steel was subjected to conventional quenching, conventional quenching followed by tempering, double quenching, and double quenching followed by tempering heat treatments. The effects of microstructural changes caused by these treatments on hardness, impact toughness, and wear resistance were examined. Microstructures were analyzed using a scanning electron microscope. Martensite lath and carbide sizes were measured with ImageJ software, and phase analysis was performed via X-ray diffraction. Hardness was measured using the Rockwell C scale. Charpy impact and dry sliding wear tests were conducted. Double quenching decreased martensite lath size, carbide size, and retained austenite. It improved hardness, impact toughness, and wear resistance by approximately 30 percent. Tempering increased impact toughness but slightly reduced hardness and wear resistance.

1. Machine elements go through many processes during their production from casting to machining. The essential purpose of these processes is to make machine components of higher quality and functio...

The effects of graphene nanoplatelets added to pure Fe powders at different rates on microstructure, hardness and electrical conductivity were investigated. Prepared mixed powders were compressed at 700 MPa pressure and sintered at 1250°C. Densities of samples were measured Archimedes technique. Scanning electron microscope was used for microstructure investigations. The hardness was measured according to ASTM E384. Phase types, average crystallite size and dislocation density calculations in sintered samples were made. Electrical resistance measurements were made with the four-point probe measurement technique. Powder mixtures containing ≤5 wt-% graphene nanoplatelets were sintered into compact samples. The smallest grain size, highest dislocation density, highest hardness and electrical conductivity values were obtained in samples containing 2 wt-% of graphene nanoplatelets.

In this study, life time calculation of aluminum 7075 alloy with cryogenic and natural aging processes was performed by thermal analysis. The aluminum alloy was quenched after solid solution treatment at 480°C and naturally aged for 10-100 days at room temperature (25°C). Other samples were cryogenically treated at -40°C and -80°C for 2 hours after solid solution treatment at 480°C. After the cryogenic treatment, natural aging was done at room temperature for 10-100 days. At the end of each period determined for the samples, the hardness values were measured. It was observed that there was no significant change in hardness values at the end of 10 and 100 days at -80 °C. Thus, it was determined that the natural aging process does not start after cryogenic treatment at -80 °C. It was observed that the hardness value of naturally aged samples after cryogenic treatment at -40 °C increased more than the natural aged samples only. This showed that -40 °C improved the mechanism by creating a driving force in the material. Life time calculations between 30 °C and 320 °C also showed that -40 °C cryogenic treatment + natural aging increased life time by approximately 20% compared to natural aging alone.

In this study, metal matrix hybrid composites obtained by adding B4C and graphene nanoplatelets (GNPs) powders as reinforcement elements to Al7075 powders were produced by powder metallurgy (P/M) method. Mixture powders prepared by supplementing Al7075 powders with different weight ratios of B4C (2-5-10%) and GNPs (0.5%) are compressed at room temperature and at different pressing pressures (600-700-800 MPa) and then at different temperatures (500-550- 600C) was sintered in vacuum atmosphere and samples were produced in accordance with ASTM G99 standard. Density and hardness analyzes were carried out depending on the changing production parameters of hybrid composites. It was determined that the pressing pressure had a direct effect on the condensation rate of the samples and the highest density value was 2.6764 g/cm3 in the Al7075+5% B4C+0.5% GNPs sample pressed under 800MPa pressure. The hardness of the samples, on the other hand, generally increased due to the increasing amount of reinforcement element, while it also increased with the increase of pressing pressure and sintering temperature.

Bu çalışmada farklı karbon oranlarına sahip Toz Metalurjisi (TM) çeliklere nikel elementi ilave edilerek darbe tokluk özellikleri incelenmiştir. Bu amaçla saf demir tozuna sırasıyla ağırlıkça % 0,4 - 0,8 - 1,2 oranlarında doğal grafit , % 2 oranında nikel ve yağlayıcı olarak ise % 0,5 çinko stearat ilave edilmiştir. Hazırlanan toz karışımları, oda sıcaklığında ve tek etkili kalıpta 700 MPa presleme basıncında şekillendirilerek darbe test numuneleri üretilmiştir. Ardından 1200 ºC sıcaklıkta 5x10-2 Pa vakum ortamında 20 dk süre sinterlenmiştir. Sinterlenme işlemi sonrasında toz metal çeliklere farklı sıcaklıklarda darbe testi uygulanarak darbe tokluk değerleri belirlenmiştir. Alaşımın kimyasal bileşimine bağlı olarak SEM mikroyapı ve kırık yüzey analizleri yapılarak, makrosertlik değerleri ile darbe toklukları arasındaki ilişkiler incelenmiştir. Nikel ilaveli TM çeliklerin mikroyapılarında nikelce zengin ferritik katı çözeltinin oluşarak perlit çökelmesini baskıladığı belirlenmiştir. -20 °C’de tüm numuneler çok düşük darbe tokluğuna sahipken, +40 °C’de yapılan darbe testi değerleri artan karbon oranıyla azalmıştır.