Several conditions—including optics, work material and the environment—can influence the results of laser processing. The physical model of laser-processing phenomena is represented as a complex function; thus, the development of a prediction model using machine learning (ML) may be an effective approach. In this study, the quantity of ablation under femtosecond ultrashort pulse laser processing was predicted using an ML model. For work materials, polycrystalline diamond was used as a composite material, cold rolled steel and aluminum were used as metals, silicon was used as a semiconductor and glass was used as an insulator. A total of 340 datasets were prepared for each material. The neuralnetwork algorithm was used to develop the prediction model. We also explored the challenges from the viewpoint of materials informatics. By applying the algorithm to each material dataset, prediction models with ±20 % precision were developed. When the algorithm was applied to all material datasets, the resulting prediction models had ±40 % precision for the single materials, whereas the prediction function was far from ideal in the case of the composite material. It is necessary to tune the input datasets (particularly the physical conditions of the composite material) for the ML model.
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