Today’s industrial transformation is taking advantage of the benefits of information and communication technologies (ICT) to evolve into a more decision-making environment in manufacturing. Efficiency, agility, innovation, quality and cost savings are the goals of this transformation in one of the most employed manufacturing processes as is the case of machining. Drilling processes are among the last operations in the different manufacturing stages of machined parts, where an undetected problem can lead to the production of a defective part. Data analysis of sensor signals gathered during drilling processes provides information related to the cutting process that can anticipate non-desired phenomena. This work illustrates the experimental setup for sensorial data acquisition in drilling processes, signal processing techniques and feature extraction methodologies for faster and more robust decision-making paradigms.
Duo, A., Segreto, T., Caggiano, A., Basagoiti, R., Teti, R., Arrazola, P.J. (2021). Drilling process monitoring: a framework for data gathering and feature extraction techniques. In 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 15-17 July 2020 (pp.189-195) [10.1016/j.procir.2021.03.123].
Drilling process monitoring: a framework for data gathering and feature extraction techniques
Caggiano, Alessandra;
2021-01-01
Abstract
Today’s industrial transformation is taking advantage of the benefits of information and communication technologies (ICT) to evolve into a more decision-making environment in manufacturing. Efficiency, agility, innovation, quality and cost savings are the goals of this transformation in one of the most employed manufacturing processes as is the case of machining. Drilling processes are among the last operations in the different manufacturing stages of machined parts, where an undetected problem can lead to the production of a defective part. Data analysis of sensor signals gathered during drilling processes provides information related to the cutting process that can anticipate non-desired phenomena. This work illustrates the experimental setup for sensorial data acquisition in drilling processes, signal processing techniques and feature extraction methodologies for faster and more robust decision-making paradigms.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
