An application of Fuzzy ARTMAP neural network to real-time learning and prediction of time-variant machine tool error maps

Autor:	J.C. Ziegert, N. Srinivasa
Rok vydání:	2002
Předmět:	Real time learning business.product_category Observational error Artificial neural network Cutting tool Computer science business.industry Pattern recognition Machine learning computer.software_genre Fuzzy logic Machine tool Duty cycle Artificial intelligence business computer Test data
Zdroj:	Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.
Popis:	The problem of real-time learning of thermal error maps in machine tools is investigated. This problem is treated as an incremental approximation of a functional mapping between thermal sensor readings and the associated positional errors at each location of the cutting tool. The Fuzzy ARTMAP is used as a tool to achieve this approximation in real-time. Experimental measurements of the positional errors for a turning center were performed using a laser ball-bar over two separate thermal duty cycles. The Fuzzy ARTMAP was trained online using the data collected during the first duty cycle. Data from a new duty cycle is used to test the performance of the trained network. Results show that the Fuzzy ARTMAP is not only able to learn thermal errors in real-time but can also make accurate predictions of the test data. >
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2c6f2795a300c3e9b27bcca8aeaf53f5 https://doi.org/10.1109/fuzzy.1994.343596 Zobrazit plný text záznamu