A study on the identification of the work piece being laser machined is conducted with the goal of developing a strategy to automatically adjust the processing parameters. The identification tech-nique is based on the spectroscopic analysis of the plasma emission generated during laser ablation. Radiation emitted by the plasma is characteristic of the ablated material and thus can be used as a spectral “fingerprint” for identification purposes. Linear correlation and artificial neural networks are implemented as classification algorithms. Both methods present efficient identification of sam-ples with recognizably different emission spectra. Neural networks outperform linear correlation for the identification of work pieces with very similar spectra. Additionally, the influence of processing and acquisition conditions on the performance of both algorithms is investigated. Finally, a strategy for customized processing based on the identification of each studied work piece is proposed. Linear correlation is used for general identification. Meanwhile, artificial neural networks are solely em-ployed to identify materials for which classification performance of linear correlation decreases. Subsequently, machining parameters are adjusted according to the identified work piece.
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