|Autors: Ivanova, M. S., Tsenev, V. P., Dimitrov B.|
Title: Manufacturing Process Optimization Through Machine Learning and Analytic Prediction
Keywords: big data, deep learning, FMEA, machine learning, optimization, statistics, “smart” manufacturing, robotic line
Abstract: A "smart" production is characterized with collection of a large amount of data and the application of machine and deep learning algorithms for the purposes of analytical prediction. The analysis supports the implementation of intelligent management and rapid response to changes in a manufacturing process. The paper proposes an approach for optimizing a robotic manufacturing line for electronic components through applying the failure mode and effect analysis and algorithm for deep learning. This approach is embedded in a software tool created through C#, Windows Forms technology and open source to assist identification of the potential risks by the responsible engineer.
Вид: пленарен доклад в международен форум, публикация в реферирано издание