Tool degradation is a critical issue in milling process design, influencing production efficiency, quality, and cost-effectiveness. Various factors, including tool material properties, machining parameters, and workpiece material, influence tool flank wear. To anticipate and mitigate tool wear, optimizing tool life and overall machining performance, a novel framework is proposed to predict tool wear. A real dataset obtained from experimental observations of a milling process, incorporating different machine parameters, was used to build predictive models. The research aims to overcome tool life limitations, reduce downtime, and minimize production costs, paving the way for more efficient and sustainable machining processes.

Advancing Sustainable Milling: A Novel Framework for Tool Wear Prediction

Ferrisi, Stefania
;
Guido, Rosita;Lofaro, Danilo;Ambrogio, Giuseppina
2026-01-01

Abstract

Tool degradation is a critical issue in milling process design, influencing production efficiency, quality, and cost-effectiveness. Various factors, including tool material properties, machining parameters, and workpiece material, influence tool flank wear. To anticipate and mitigate tool wear, optimizing tool life and overall machining performance, a novel framework is proposed to predict tool wear. A real dataset obtained from experimental observations of a milling process, incorporating different machine parameters, was used to build predictive models. The research aims to overcome tool life limitations, reduce downtime, and minimize production costs, paving the way for more efficient and sustainable machining processes.
2026
machine learning
Tool condition monitoring
tool wear
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/399344
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