Abstract
Industrial processes have been progressively incorporating technologies such as web 2.0 communications, increased automation, the use of smart devices, and the incorporation of data obtained from such devices to improve numerous aspects of manufacturing. Recently, data-driven approaches based on Machine Learning algorithms and models have begun to be used increasingly. Given their excellent results in software-related problems, it is unsurprising that many of these technologies are being applied and tested in new products and the industrial environment. Examples can range from utilization as a core technology for self-driving cars to a complete quality assurance system for industrial plants. This study aims to perform a bibliometric analysis to investigate research related to the employment of machine learning in industrial assembly lines between the years 2017 and 2022.
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de Queiroz, D.C.F., Barbalho, S.C.M., Huebser, L., Duarte, K.T.N., de Paiva, P.V.V. (2023). Machine Learning Applied to Industrial Assembly Lines: A Bibliometric Study. In: Gonçalves dos Reis, J.C., Mendonça Freires, F.G., Vieira Junior, M. (eds) Industrial Engineering and Operations Management. IJCIEOM 2023. Springer Proceedings in Mathematics & Statistics, vol 431. Springer, Cham. https://doi.org/10.1007/978-3-031-47058-5_39
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