Model inteligentnega nadzora obrabe in poškodb rezalnih orodij z uporabo termografije : doktorska disertacija

slovenian

doktorska dela

2021

artificial intelligence   cutting   cutting tool   deep learning   industry 4.0   thermography   tool wear   turning

Univerza v Mariboru, Fakulteta za strojništvo

In turning, wear control of a cutting tool serves to increase product quality, optimise tool-related costs, and avoid undesirable events. In small series and single item production, the machine operator decides when a cutting tool needs to be changed based on his experience. Incorrect decisions often lead to higher costs, production downtime and scrap. In this dissertation, a control system for a tool condition monitoring system that automatically detects tool wear during turning is presented. For process control an infrared camera was used, which - in contrast to conventional cameras - detects not only the visual but also the thermographic condition. Despite difficult environmental conditions (e.g. hot chips), we protected the camera and placed it right up to the cutting knife, so that the machining could be observed closely. To create a dataset of 18,486 images, we machined on a lathe with tool inserts of different wear levels. Using a Deep Learning and a Convolutional Neural Network (CNN), we developed a model for tool wear and tool damage prediction. The image database was divided into two groups: images created during the turning process and images created after the turning process (thermographic images of a cutting tool). The model is successful on both image databases. Based on the thermographic images, the learned model automatically determines the condition of the cutting tool in terms of its suitability for further use in machining (no wear, low wear, high wear). The accuracy of classification for the combined set of all images is 99.92%, which confirms the suitability of the proposed method. The model was tested on unknown images (changed machining conditions), confirming the robustness of the intelligent system for the classification of unknown materials (more than 98% accuracy of tool wear classification for unknown material). Such a system allows immediate action in case of cutting tool wear or breakage, regardless of the operator's knowledge and competence.%

08.08.2023