Martin Appiah Kesse

Mechanical Engineering

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Research Summary

(inferred from publications by AI)

The researcher has made significant contributions across diverse fields, with their work spanning welding techniques and residual stresses in physical sciences, social acceptance of renewable energy in social sciences, biochemistry in physical sciences, hydrogen embrittlement behaviors in physical sciences, electric vehicle policies in engineering, and online education systems in social sciences. Their research integrates advancements in various domains to address challenges in each area, showcasing an interdisciplinary approach aimed at pushing boundaries within multiple scientific fields.

Research Themes

All (11 Papers)

Physical Sciences (9 Papers)

Social Sciences (2 Papers)

All Papers

Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning(2020)

Modeling of an artificial intelligence system to predict structural integrity in robotic GMAW of UHSS fillet welded joints(2017)

Study of Adaptive Automated GMAW Process for Full Penetration Fillet Welds in Offshore Steel Structures(2017)

Laser-TIG hybrid welding process(2013)

Usability of Laser-TIG Hybrid Welding Processes(2017)

Can Artificial Intelligence Assist Project Developers in Long-Term Management of Energy Projects? The Case of CO2 Capture and Storage(2020)

Engine performance and emissions of fuel produced from palm kernel oil(2019)

Improvement of strength and toughness: The effect on the weldability of high-strength steels used in offshore structures(2015)

Realistic assessment of electric vehicle policies in Africa: a case study of Ghana(2025)

Planning the Implementation of Moodle eLearning system in some African institutions(2010)

Submerged Arc Welding Process Productivity in Welding Thick High Strength Steel Plates Used for Arctic Applications(2017)

Collaboration Network

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Research Collaboration Map

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About This Profile

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.