Thorsten Hermann Becker

About

I am appointed as a Professor in the Department of Mechanical Engineering, while also holding the role of Director for the Centre of Materials Engineering at the University of Cape Town. I am also appointed to an extraordinary position in the Mechanical and Mechatronic Engineering department at Stellenbosch University. My research interests include structural integrity, specifically fatigue, fracture, and creep. My work aims to utilise advanced techniques, including digital image and volume correlation, high-resolution microscopy, and finite element modelling, to measure and extract engineering parameters for structural integrity assessments. One of my keen interests lies in the additive manufacturing of metals and high-temperature applications.

Employment

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Education

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Publications (85)

• A stochastic damage-tolerant framework for fatigue strength prediction in additively manufactured alloys
  Materials & Design 2026 DOI: 10.1016/j.matdes.2026.116162
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• Fracture toughness of laser powder bed fusion-produced AlSi10Mg: Microstructural effects of heat treatment and build orientation
  Materialia 2026 DOI: 10.1016/j.mtla.2026.102885
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• Feature-driven dimensionality reduction for rapid process-window development in the laser powder bed fusion of titanium alloys
  2026 DOI: 10.2139/ssrn.6269944
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• Fatigue strength prediction of additively manufactured Ti-6Al-4V: A defect-based physics-informed neural network
  Materials Today Communications 2026 DOI: 10.1016/j.mtcomm.2026.115035
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• An open-source digital image correlation acquisition system, focusing on temporal synchronization with the Gleeble 3800
  MATEC Web of Conferences 2025 DOI: 10.1051/matecconf/202541708004
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• Characterisation of heat-treated Ti-rich NiTi fabricated by laser powder bed fusion
  MATEC Web of Conferences 2025 DOI: 10.1051/matecconf/202541707002
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• Effect of processing parameters on the microstructure and density of LPBF processed CoCrMo
  MATEC Web of Conferences 2025 DOI: 10.1051/matecconf/202541705009
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• Resource efficiency in high-value material manufacturing with directed energy deposition
  MATEC Web of Conferences 2025 DOI: 10.1051/matecconf/202541708003
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• Machine Learning-Based Prediction of Fatigue Strength in Additively Manufactured Ti-6Al-4V Parts: A Sensitivity Analysis of Input Features
  Journal of Materials Engineering and Performance 2025 DOI: 10.1007/s11665-025-11115-7
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• Fracture toughness of laser-based powder bed fusion produced Ti-6Al-4V
  Materials Science and Engineering: A 2025 DOI: 10.1016/j.msea.2025.148199
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