A5077386411- Metallurgy and Material Forming
- Titanium Alloys Microstructure and Properties
- Microstructure and Mechanical Properties of Steels
- Advancements in Battery Materials
- Advanced machining processes and optimization
- Additive Manufacturing Materials and Processes
- Advanced Battery Materials and Technologies
- Advanced Machining and Optimization Techniques
- Hydrogen embrittlement and corrosion behaviors in metals
- Metallurgical Processes and Thermodynamics
- Intermetallics and Advanced Alloy Properties
- Supercapacitor Materials and Fabrication
- High Entropy Alloys Studies
- Adsorption and biosorption for pollutant removal
- Advanced Surface Polishing Techniques
- Advanced Battery Technologies Research
- Non-Destructive Testing Techniques
- Welding Techniques and Residual Stresses
- MXene and MAX Phase Materials
- Aluminum Alloys Composites Properties
- Fuel Cells and Related Materials
- Metal and Thin Film Mechanics
- Aluminum Alloy Microstructure Properties
- Additive Manufacturing and 3D Printing Technologies
- Advanced Sensor and Energy Harvesting Materials
Gyeongsang National University
2016-2025
Government of the Republic of Korea
2016
Pohang University of Science and Technology
2006-2010
Indian Institute of Technology Kharagpur
2004
The mechanical properties of steel are intricately connected to their composition and service temperature. Predicting these across different work temperatures using traditional statistical methods, algorithms, equations is highly challenging due complex interdependencies. To address this, we developed an artificial-neural-network (ANN) model elucidate the relationships between composition, temperature, 5Cr-0.5Mo steels. Our demonstrated high accuracy, with minimal percentage errors in...
Martensite start (Ms) temperature is a critical parameter in the production of parts and structural steels plays vital role heat treatment processes to achieve desired properties. However, it often challenging estimate accurately through experience alone. This study introduces model that predicts Ms medium-carbon based on their chemical compositions using artificial neural network (ANN) method compares results with those from previous empirical formulae. The indicate ANN surpasses...
The heat treatment process of Ti-6Al-4V alloy alters its microstructural features such as prior-β grain size, Widmanstatten α lath thickness, volume fraction, boundary total colony and platelet length. These affect the material's mechanical properties (UTS, YS, %EL). relationship between is very complex non-linear. To understand these relationships, we developed an artificial neural network (ANN) model using experimental datasets. are used input parameters to feed %EL) output parameters....
The creep rupture life of 5Cr-0.5Mo steels used in high-temperature applications is significantly influenced by factors such as minor alloying elements, hardness, austenite grain size, non-metallic inclusions, service temperature, and applied stress. relationship these variables with the quite complex. In this study, steel was predicted using various machine learning (ML) models. To achieve higher accuracy, ML techniques, including random forest (RF), gradient boosting (GB), linear...
The current work implements machine learning techniques such as artificial neural network (ANN), support vector (SVM), and genetic algorithm (GA) to model optimize the surface roughness during wire electrical discharge machining (WEDM) of Inconel 718. For this, values were obtained from real-time WEDM experiments conducted under different levels control factors pulse on time, off peak current, servo voltage, feed rate. optimum ANN architecture was identified 5-10-10-1 SVM parameters tuned...
The impact of process factors on wire-cut electrical discharge machining (WEDM) performance is complex and nonlinear. In the present work, initially, WEDM tests were conducted titanium alloy (Ti-6Al-4V) with eight input four machinability parameters. Later, an artificial neural network (ANN) model was established to estimate performance. ANN 8-5-5-4 architecture produced a least mean squared error (MSE) average prediction (AE) for both training test data sets. precision assessed by relating...