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58,80 €ISBN 978-3-8191-0909-6Softcover196 pages63 figures270 g21 x 14,8 cmEnglishThesis
August 2026
Germán González Fernández
Microstructure-Based Finite-Element Prediction of Surface Integrity with Bayesian-Informed Wear States
Application to Longitudinal Turning of AISI 4140
Surface integrity plays a decisive role in the functional performance, fatigue life, and reliability of machined components. This dissertation presents a physics-informed and uncertainty-aware framework for predicting wear-dependent surface integrity during longitudinal turning of AISI 4140 steel. The approach combines tool-integrated thin-film sensors, experimentally validated two- and three-dimensional finite-element simulations, microstructure-based modelling of dynamic recrystallisation, and Bayesian inference for tool-wear estimation. By integrating sensing, physics-based simulation, surrogate modelling, and probabilistic inference, the proposed framework enables uncertainty-aware prediction of thermomechanical loads, microstructural evolution, and ultrafine-grained surface-layer formation. The resulting hybrid soft-sensor architecture establishes a foundation for future process-accompanying monitoring, digital twins, and closed-loop machining applications. The dissertation is intended for researchers and engineers working in manufacturing science, machining, finite-element modelling, surface integrity, and intelligent production systems.
Keywords: Turning; Machining; Surface Integrity; FEM
Forschungsberichte aus dem wbk, Institut für Produktionstechnik, Karlsruher Institut für Technologie (KIT)
Edited by Prof. Dr.-Ing. Jürgen Fleischer, Prof. Dr.-Ing. Gisela Lanza and Prof. Dr.-Ing. habil. Volker Schulze, Karlsruhe
Volume 314
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Electronic publication (PDF): 978-3-8191-0845-7
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