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Remaining Machining Tool Life Prediction Using Machine Learning
The machinery and equipment manufacturing industry is decisive in achieving a sustainable economy with a savings potential of 37 % of global CO2 emissions. Machining production is a significant factor, accounting for over 15 % of global product development costs. As a result of technological innovation in its application areas, the demands on machining continue to increase, particularly in terms of product quality, flexibility, and component complexity. Examples are the aerospace or tool- and die-making industries, where computer-aided manufacturing of free-form components based on multi-axis machining is standard. At the same time, manufacturing companies are facing the challenges of increasing competition and cost pressure. In order to manufacture at consistently high quality and minimal costs, process and tool monitoring and the subsequent derivation of remaining tool life is of interest. However, due to the increasing customization of production, the prediction of remaining tool life is currently not applicable in the abovementioned areas. Previous process and tool monitoring approaches are too rigid for flexible manufacturing scenarios as they are mainly designed for series production. Accordingly, a methodology for small-batch and single-part production requirements opens up optimization potentials that could not be used so far. Process and tool monitoring methods generally consist of the four components of sensor technology, signal processing and feature extraction, inference of tool and process condition, and prediction of remaining tool life. This work first analyzes the influence of small-batch and single-part production conditions on process and tool monitoring. Mechanical vibration is identified as a particularly suitable monitoring variable. It allows a permanent and process-independent sensor integration without being affected by tool or workpiece adaptations. Based on a physical vibration source model of the machine tool, it is possible to demonstrate the machine independence of the acceleration ...
Remaining Machining Tool Life Prediction Using Machine Learning
The machinery and equipment manufacturing industry is decisive in achieving a sustainable economy with a savings potential of 37 % of global CO2 emissions. Machining production is a significant factor, accounting for over 15 % of global product development costs. As a result of technological innovation in its application areas, the demands on machining continue to increase, particularly in terms of product quality, flexibility, and component complexity. Examples are the aerospace or tool- and die-making industries, where computer-aided manufacturing of free-form components based on multi-axis machining is standard. At the same time, manufacturing companies are facing the challenges of increasing competition and cost pressure. In order to manufacture at consistently high quality and minimal costs, process and tool monitoring and the subsequent derivation of remaining tool life is of interest. However, due to the increasing customization of production, the prediction of remaining tool life is currently not applicable in the abovementioned areas. Previous process and tool monitoring approaches are too rigid for flexible manufacturing scenarios as they are mainly designed for series production. Accordingly, a methodology for small-batch and single-part production requirements opens up optimization potentials that could not be used so far. Process and tool monitoring methods generally consist of the four components of sensor technology, signal processing and feature extraction, inference of tool and process condition, and prediction of remaining tool life. This work first analyzes the influence of small-batch and single-part production conditions on process and tool monitoring. Mechanical vibration is identified as a particularly suitable monitoring variable. It allows a permanent and process-independent sensor integration without being affected by tool or workpiece adaptations. Based on a physical vibration source model of the machine tool, it is possible to demonstrate the machine independence of the acceleration ...
Remaining Machining Tool Life Prediction Using Machine Learning
Krupp, Lukas (author) / Grabmaier, Anton
2024-01-19
Theses
Electronic Resource
English
Fakultät für Ingenieurwissenschaften » Elektrotechnik und Informationstechnik » Elektronische Bauelemente und Schaltungen , ddc:004 , ddc:670 , Machine Learning -- Automatisiertes maschinelles Lernen -- Erklärbare künstliche Intelligenz -- Freiform-Zerspanung -- Individualisierte Produktion -- Restlebensdauervorhersage -- Werkzeugzustandsüberwachung , ddc:620 , ddc:621.3
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