Laser cutting (LC) has become increasingly relevant for manufacturing structural steel components, where precision, geometric flexibility, and minimized post-processing are essential. However, in the case of elements subjected to high-cycle fatigue (HCF), a deeper investigation is essential to evaluate the effects of edge roughness, microstructural transformations, and residual stresses induced by the thermal process on the structural response. In addition, when LC is used to fabricate complex or dense layouts, such as circular holes, sharp-corner transitions, and metastructural patterns, heat accumulation and incomplete melt ejection may compromise manufacturability and reliability. Addressing these issues, a multidisciplinary study was carried out to investigate the complex interaction between the thermal process of LC and mild structural steel. Along this vein, the thesis develops an experimental, numerical, and data-driven framework linking thermal process phenomena to HCF performance in laser-cut structural steel joints. In the first part of the thesis, temperature histories and residual stress distributions due to the LC process were observed experimentally by means of infrared thermography and X-ray diffraction, enabling the calibration of finite-element heat-transfer and thermo-mechanical models with Gaussian heat-flux representations. Second, considering the geometric aspects, including the capability to realize the selected geometry, the cut-edge quality and near-edge hardness profiles were investigated for straight and complex geometries under different assist-gas regimes. Finally, a probabilistic surrogate-based methodology integrating stochastic polynomial chaos expansion (SPCE) with active learning was proposed to predict HCF performance while minimizing the experimental test burden. This SPCE emulator captures both deterministic trends and variability in fatigue life as a function of LC parameters. The main outcome of this work is to promote the use of laser cutting technology in the field of steel structures, with particular attention to structures subjected to HCF loading. This is achieved through a more accurate use of the process for both structural applications and metastructural systems. Collectively, results support the reliable adoption of laser-cut steel components in fatigue-sensitive environments by explicitly incorporating uncertainty and manufacturability constraints.
INNOVATIVE LASER AND MECHANICAL MACHINING TECHNIQUES APPLIED TO ADVANCED STRUCTURAL APPLICATIONS / Olmez, H.N.. - (2026 Aug 04).
INNOVATIVE LASER AND MECHANICAL MACHINING TECHNIQUES APPLIED TO ADVANCED STRUCTURAL APPLICATIONS
Olmez, Hasan Numan
2026-08-04
Abstract
Laser cutting (LC) has become increasingly relevant for manufacturing structural steel components, where precision, geometric flexibility, and minimized post-processing are essential. However, in the case of elements subjected to high-cycle fatigue (HCF), a deeper investigation is essential to evaluate the effects of edge roughness, microstructural transformations, and residual stresses induced by the thermal process on the structural response. In addition, when LC is used to fabricate complex or dense layouts, such as circular holes, sharp-corner transitions, and metastructural patterns, heat accumulation and incomplete melt ejection may compromise manufacturability and reliability. Addressing these issues, a multidisciplinary study was carried out to investigate the complex interaction between the thermal process of LC and mild structural steel. Along this vein, the thesis develops an experimental, numerical, and data-driven framework linking thermal process phenomena to HCF performance in laser-cut structural steel joints. In the first part of the thesis, temperature histories and residual stress distributions due to the LC process were observed experimentally by means of infrared thermography and X-ray diffraction, enabling the calibration of finite-element heat-transfer and thermo-mechanical models with Gaussian heat-flux representations. Second, considering the geometric aspects, including the capability to realize the selected geometry, the cut-edge quality and near-edge hardness profiles were investigated for straight and complex geometries under different assist-gas regimes. Finally, a probabilistic surrogate-based methodology integrating stochastic polynomial chaos expansion (SPCE) with active learning was proposed to predict HCF performance while minimizing the experimental test burden. This SPCE emulator captures both deterministic trends and variability in fatigue life as a function of LC parameters. The main outcome of this work is to promote the use of laser cutting technology in the field of steel structures, with particular attention to structures subjected to HCF loading. This is achieved through a more accurate use of the process for both structural applications and metastructural systems. Collectively, results support the reliable adoption of laser-cut steel components in fatigue-sensitive environments by explicitly incorporating uncertainty and manufacturability constraints.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione



