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Tool Wear Monitoring Using Multi-sensor Time Series and Machine Learning
Auteur(s)
Date de parution
2023-12-15
In
Progress in Artificial Intelligence
De la page
497
A la page
510
Revu par les pairs
true
Résumé
In the milling process of micro-machining, the optimization process is one of the keys to reduce production cost. By monitoring the tool wear and detecting when it is no longer acceptable, the machining process can be adjusted more accurately. This research explores four approaches using different machine learning models to predict machining tool wear during the milling process. The study is based on a dataset created with a face milling operation on
stainless steel (AISI 303) round material. The machining is divided into a number of stairs and is performed with a 3mm tungsten carbide. Three different types of sensors are used to measure the wearing process, with acoustic emission, accelerometers and axis currents. The better approach achieved a f1-score of 73% on five classes with a Extra Trees Classifier.
stainless steel (AISI 303) round material. The machining is divided into a number of stairs and is performed with a 3mm tungsten carbide. Three different types of sensors are used to measure the wearing process, with acoustic emission, accelerometers and axis currents. The better approach achieved a f1-score of 73% on five classes with a Extra Trees Classifier.
Nom de l'événement
EPIA 2023
Lieu
Faial Island, Portugal
Identifiants
Type de publication
conference paper
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