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This paper reviews machine-learning methods that are nowadays the most frequently used for the supervised classification of spectral signals in laser-induced breakdown spectroscopy (LIBS). We analyze and compare various statistical classification methods, such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), partial least-squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), support vector machine (SVM), naive Bayes method, probabilistic neural networks (PNN), and K-nearest neighbor (KNN) method. The theoretical considerations are supported with experiments conducted for real soft-solder-alloy spectra obtained using LIBS. We consider two decision problems: binary and multiclass classification. The former is used to distinguish overheated soft solders from their normal versions. The latter aims to assign a testing sample to a given group of materials. The measurements are obtained for several laser-energy values, projection masks, and numbers of laser shots. Using cross-validation, we evaluate the above classification methods in terms of their usefulness in solving both classification problems.

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This paper describes the application of laser micromachining to rapid prototyping of printed circuit boards (PCB) using nano-second lasers: the solid-state Nd:YAG (532/1064 nm) laser and the Yb:glass fiber laser (1060 nm). Our investigations included tests for various mask types (synthetic lacquer, light-sensitive emulsion and tin). The purpose of these tests was to determine some of the basic parameters such as the resolution of PCB prototyping, speed of processing and quality of PCB mapping with commonly available laser systems. Optimization of process parameters and the proposed conversion algorithm have allowed us to produce circuit boards with a resolution similar to that of the Laser Direct Imaging (LDI) technology.

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