Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning

Matthias Wimmer, Roman Hartl, Michael F. Zaeh

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

3 Zitate (Scopus)

Abstract

The residual stress state of the machined sub-surface influences the service quality indicators of a component, such as fatigue life, tribological properties, and distortion. During machining, the radius of the cutting edge changes due to tool wear. The cutting-edge rounding significantly affects the residual stress state in the part and the occurring process forces. This paper presents a tool wear prediction model based on in-process measured cutting forces. The effects of the cutting-edge geometry on the force behavior and the machining-induced residual stresses were examined experimentally. The resulting database was used to realize a Machine Learning algorithm to calculate the hidden value of tool wear. The predictions were validated by milling experiments using rounded cutting edges for different process parameters. The microgeometry of the cutting edge could be determined with a Root Mean Square Error of 8.94 μm.

OriginalspracheEnglisch
Aufsatznummer100
FachzeitschriftJournal of Manufacturing and Materials Processing
Jahrgang7
Ausgabenummer3
DOIs
PublikationsstatusVeröffentlicht - Juni 2023

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