Gabriel Awuku Dzukey
The researcher's work focuses on additive manufacturing materials, particularly those formed using SLM (Singly-Lamellar StringBuilder) technology. Their studies extensively examine properties such as porosity, hardness, and friction and wear performance in SLM-affected samples. They also employ Taguchi’s statistical design of experiments for process optimization. Functionally graded materials via interfacial bonding are investigated, focusing on residual stresses and corrosion performance. The researcher uses neural networks to predict tensile strength influenced by heat treatments and process parameters. Additionally, they examine post-process-free fatigue performance in in-situ-heated IN718 samples, highlighting the impact of heat treatment strategies on material durability.
Faculty Biography
Experienced Mechanical Design Engineer with a demonstrated history of working in the Design and Manufacturing industry. Strong engineering professional skilled in , SolidWorks, Autodesk Inventor, Siemens NX, AutoCAD, ANSYS , Python, Minitab, SPSS, PHP and Additive Manufacturing Research & Management.
Additive Manufacturing Materials and Processes
Toward post-process-free fatigue performance: In-situ heating and heat treatment of additively manufactured IN718
Predicting Tensile Strength in Laser Powder Bed Fusion (LPBF) of IN718 Using Neural Networks: the Influence of Heat Treatments and Process Parameters
Functionally graded tungsten–Inconel 718 structures via LPBF: Interfacial bonding, residual stresses, and corrosion performance
Porosity, Hardness, Friction and Wear Performance Analysis of H13 SLM-Formed Samples
Process Parameter Optimization for Selective Laser Melting of 316L Stainless Steel Material using Taguchi’s Statistical Design of Experiment Procedure
Open AccessDepartment of Mechanical Engineering
Kwame Nkrumah University of Science and Technology