Eric Rahner, T. Thiele, Heike Voss, Frank A. Müller, Jörn Bonse, Stephan rer. nat. Gräf
• New free software tool ReguΛarity for automated quantification of the regularity of laser-induced periodic surface structures (LIPSS). • Defines a five-parameter regularity tuple R = (R Λ,2D, R Λ , G, δθ, Δ φ ¯ ) for microscopy images. • Combines local and global Fourier analyses with gradient-based orientation assessment. • Enables high-throughput, objective evaluation of LIPSS across various materials and imaging methods. • Tests with artificial patterns and real LIPSS on AISI 316L and AlMg5 alloys validate robustness and sensitivity. The growing demand for precise surface functionalization through laser-generated periodic surface structures highlights the necessity for efficient, reproducible, and objective evaluation methods to evaluate their structural regularity. We introduce ReguΛarity (v.1.2.7) , a freely available, Python-based software with a graphical user interface for the automated, quantitative assessment of the regularity of laser-induced periodic surfaces structures (LIPSS), obtained from optical microscopy, SEM, or AFM. The software integrates image segmentation, one- and two-dimensional Fourier analyses, and gradient-based orientation determination to facilitate a comprehensive regularity analysis of grating-like (quasi-)periodic surface patterns with spatial periods Λ. This is achieved through the proposed regularity tuple R , composed of five key parameters: the normalized spread of the spatial period R Λ,2D (from 2D-FT), the normalized variation of the most frequent spatial period R Λ (from 1D-FT), the Gini coefficient G , the Dispersion of the LIPSS Orientation Angle δθ (DLOA), and the mean phase deviation Δ φ ¯ . To demonstrate its applicability, we compare ideal sinusoidal patterns with SEM images obtained from LIPSS on stainless steel (AISI 316L) and aluminum alloy (AlMg5) surfaces, confirming the software’s ability to objectively distinguish between varying levels of structural regularity. ReguΛarity facilitates high-throughput analysis and data-driven process optimization in surface engineering and laser materials processing.