No abstract

Optimization of color laser marking process mostly depends on effective identification of optimal values of laser marking parameters. This is a difficult combinatorial optimization problem, which is still essential for companies that use laser marking systems. The study proposes a new approach to the process optimization through the use of genetic algorithms, carrying out preliminary experimental investigation, analyzing the laser marking results, and presenting possible improvements to the current implementation of genetic algorithms. >

Method allows for modelling of the complex process of laser pulse energy distribution over flat work surface. The process of calculating the correct result does not use common lasing formulas but instead employs the mathematical model of matrix multiplication of three input matrices representing a pulse model, a line model, and a plane model. The pulse model represents the distribution of planar energy densities within the laser pulse. The line model represents the distribution of pulses within the line. The plane model represents the distribution of lines within the plane. Because mathematical model is implemented within a spreadsheet processor, its size can be adjusted as needed and it can be instantiated multiple times for simultaneous modelling of different input parameters.

No abstract

Method allows for modelling of the anticipatory results of colour laser marking experiments. The process of calculating expected results takes into consideration the construction specifics of laser system being used and displays the results in compact form of a set of parameter matrices that have their values conditionally formatted as colour maps for easy identification of complex patterns. The complete set of all the related parameter matrices, both technical and derived, as well as the specific relations between them form the mathematical model of forecasting laser marking experiment results. Because the mathematical model is implemented within spreadsheet processor, it can be instantiated multiple times for any number of experiments.