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         "url": "https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12408/2653604/Advanced-laser-processingand-its-optimizationwith-machine-learning/10.1117/12.2653604.full", 
         
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            	{"first" : "Andreas",	"last" : "Michalowski"},
            	{"first" : "Alexander",	"last" : "Ilin"},
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         "abstract": "The flexibility of new laser sources and process-monitoring enables new possibilities in laser-based production technology, for instance the combination of different laser processes with many adjustable parameters. The fusion of domain knowledge and probabilistic models in the form of hybrid models allows an efficient optimization of these processes with machine learning. This can be a key technology to realize self-learning laser-based universal machines in the future. The article discusses some examples where algorithm-based optimization, partly supported by hybrid models, can already greatly reduce the effort required to find suitable process parameters.",
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         "editor": [ 
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            	{"first" : "Aiko",	"last" : "Narazaki"},
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         "volume": "Laser Applications in Microelectronic and Optoelectronic Manufacturing (LAMOM) XXVIII","abstract": "The flexibility of new laser sources and process-monitoring enables new possibilities in laser-based production technology, for instance the combination of different laser processes with many adjustable parameters. The fusion of domain knowledge and probabilistic models in the form of hybrid models allows an efficient optimization of these processes with machine learning. This can be a key technology to realize self-learning laser-based universal machines in the future. The article discusses some examples where algorithm-based optimization, partly supported by hybrid models, can already greatly reduce the effort required to find suitable process parameters.",
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