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Research Summary
(inferred from publications by AI)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.
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This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.