A Statistics-Assisted Fuzzy-Nets-Based In-Process Tool Wear Prediction System in Milling Operations
نویسندگان
چکیده
This paper describes a fuzzy-nets approach with the assistance of a statistical model (multiple regression) to create an in-process tool wear prediction (S-FN -ITWP) system, the goal of which is to predict tool wear in milling operations under cutting conditions determined by feed rate and depth of cut with cutting force detected through a dynamometer. After the S-FN-ITWP system had been trained with the 100 experiment training data sets, another nine tool wears predicted from the system were compared to the actual tool wear to test the effectiveness of the system. The results reveal that this system can successfully predict tool wear in milling operations to within error of ± 0.023 mm.
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