Laser induced periodic surfaces structures: advances in modelling, processing and monitoring

Michalek, Aleksandra ORCID: 0000-0001-7057-7671 (2022). Laser induced periodic surfaces structures: advances in modelling, processing and monitoring. University of Birmingham. Ph.D.

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Abstract

Laser Induced Periodic Surface Structures (LIPSS) have been known to the scientific world for almost as long as the invention of first laser. The advantages of the ripples structures have been recognized soon after, which resulted in the development of many industrially fit applications because of the flexibility, environmental friendliness and robustness of the technology. Nonetheless, the important reason of the continuous growing interest in LIPSS is the great variety of functional responses that can be ‘imprinted’ on the surfaces, and thus be employed in many engineering fields. For the broader use of LIPSS treatments, specific industrial requirements and challenges still have to be addressed, especially associated with processing, modelling and monitoring. In this context, the research presented in this thesis focuses, firstly, on investigating the synergistic use of LIPSS with coatings for the purpose of combining the valuable surface properties without compromising on the functional performance. Next, the research addresses the issues of applying periodic structures on free form surfaces when the processing conditions vary. In particular, a predictive model is developed to account the effects from 3D processing disturbances hence minimizing the empirical research required to produce optimized structures for any given material and/or geometry. Subsequently, new approaches for ripples monitoring and process quality control are investigated. A light scattering method is proposed to satisfy the technical requirements of inline process monitoring with sufficient sensitivity to detect changes in LIPSS characteristics. Lastly, the use of artificial intelligence methods is considered for predicting the functional responses based on the topography data, also when the generation process is affected by the processing disturbances.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):