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Estimación cualitativa de la rugosidad mediante algoritmos de aprendizaje automático en una operación de taladradoQualitative estimation of the roughness using automatic learning algorithms in a drilling operation
(Federación de Asociaciones de Ingenieros Industriales de España, 2020)
The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process
(Springer Verlag, 2019)
Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes.Through this, it is intended to improve the management of resources, decision-making, ...
Active Power Optimization of a Turning Process by Cutting Conditions Selection: A Q-Learning Approach
(IEEE, 2022)
In the context of Industry 4.0, the optimization of manufacturing processes is a challenge. Although in recent years many of the efforts have been in this direction, there is still improvement opportunities in these ...
Drilling test data from new and worn bits
(2019)
This directory contains the raw data acquired by Mondragon Unibertsitatea during the execution of drilling tests. These data were used to obtain the results presented in the article "The capacity of statistical features ...
