Drilling process monitoring: a framework for data gathering and feature extraction techniques
| Autor: | Aitor Duo, Roberto Teti, Rosa Basagoiti, Tiziana Segreto, Pedro José Arrazola, Alessandra Caggiano |
|---|---|
| Přispěvatelé: | Duo, A., Segreto, T., Caggiano, A., Basagoiti, R., Teti, R., Arrazola, P. J. |
| Rok vydání: | 2021 |
| Předmět: |
Signal processing
0209 industrial biotechnology Data collection Process (engineering) Computer science media_common.quotation_subject Feature extraction Drilling 02 engineering and technology 010501 environmental sciences 01 natural sciences Manufacturing engineering 020901 industrial engineering & automation Data acquisition Machining Information and Communications Technology Feature selection General Earth and Planetary Sciences Quality (business) Sensor 0105 earth and related environmental sciences General Environmental Science media_common |
| Zdroj: | Procedia CIRP. 99:189-195 |
| ISSN: | 2212-8271 |
| DOI: | 10.1016/j.procir.2021.03.123 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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