ML Models for Intelligent Tool Replacement

The wear limits of milling tools should not be exceeded with regard to the workpiece quality. Current approaches of relying on fixed service lifetimes for tool replacement overlook the crucial factor of varying wear rates and fail to determine the optimal times for replacement. This article presents an approach that uses ML algorithms in the decision-making process, which enables a tool replacement while considering varying wear rates within a defined range of cutting parameters. © 2023 Walter de Gruyter GmbH, Berlin/Boston, Germany

Subjects

Werkzeugverschleiß

Standzeit

Prozessüberwachung

Maschinelles Lernen

Fräsen

Sensorik

Tool Wear

Tool Service Life

Process Monitoring

Machine Learning

Milling

Sensors

MLE@TUHH

DDC Class

600: Technology

620: Engineering

Options

ML Models for Intelligent Tool Replacement