@article{fahle_systematic_2020,
	series = {53rd {CIRP} {Conference} on {Manufacturing} {Systems} 2020},
	title = {Systematic review on machine learning ({ML}) methods for manufacturing processes – {Identifying} artificial intelligence ({AI}) methods for field application},
	volume = {93},
	issn = {2212-8271},
	url = {http://www.sciencedirect.com/science/article/pii/S2212827120307435},
	doi = {10.1016/j.procir.2020.04.109},
	abstract = {Artificial Intelligence (AI) and especially machine learning (ML) become increasingly more frequently applicable in factory operations. This paper presents a systematic review of today’s applications of ML techniques in the factory environment. The utilization of ML methods related to manufacturing process planning and control, predictive maintenance, quality control, in situ process control and optimization, logistics, robotics, assistance and learning systems for shopfloor employees are being analyzed. Moreover, an overview of ML training concepts in learning factories is given. Furthermore, these concepts will be analyzed regarding the implemented ML method. Finally, research gaps are identified.},
	language = {en},
	urldate = {2020-09-28},
	journal = {Procedia CIRP},
	author = {Fahle, Simon and Prinz, Christopher and Kuhlenkötter, Bernd},
	month = jan,
	year = {2020},
	keywords = {Artificial Intelligence, factory operation, machine learning, production systems},
	pages = {413--418},
}

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