Effects of tool wear indicators on delamination and surface roughness during CFRP edge trimming

Carbon fiber-reinforced polymers (CFRP) are prone to inducing rapid tool wear during edge trimming, which concurrently blunts the cutting edge and aggravates delamination and surface roughness. To overcome the limitation that flank wear (VB) cannot fully capture geometric changes of edge blunting, this study introduces the wedge angle (θ) as a new geometry-based wear indicator. CFRP edge trimming experiments were conducted for synchronous in-situ tracking of θ, cutting edge radius (Re) and VB, with polynomial regression adopted to analyze their correlations with delamination and surface roughness. Results reveal θ and Re predominantly govern delamination depth (h) at 45° fiber orientation. When θ increases from 67.8° to 81.9°, and Re increases from 7.72 µm to 38.65 µm, h explodes from 107 µm to 598 µm. The polynomial model (R2 = 0.91–0.92) further discloses a negative θ-Re interaction: the sensitivity of h to θ is amplified as Re increases. Regarding surface roughness, VB remains the most predictor of (Sa), yet θ dominates the maximum valley depth (Sv) at 45° fiber orientation, with Sv escalating from 84 µm to 210 µm as wear progresses. The established θ-Re-h and VB-Sa relationships provide a quantitative basis for predicting delamination and surface roughness and offer experimental evidence and quantitative insights for high-precision CFRP edge trimming.

Authors

Institutions

Publication Details

Journal
Machining Science and Technology
Published
2026-11-06
DOI
https://doi.org/10.1080/10910344.2026.2752568
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Effects of tool wear indicators on delamination and surface roughness during CFRP edge trimming

Liangtao Hu, Ning Qian, Xiaoyu Wang, Dexiong Chen et al.
1 links
Machining Science and Technology
Advanced machining processes and optimization
article

Effects of tool wear indicators on delamination and surface roughness during CFRP edge trimming

Liangtao Hu, Ning Qian, Xiaoyu Wang, Dexiong Chen, Tao Chen, Yan Chen
article en

Abstract

Carbon fiber-reinforced polymers (CFRP) are prone to inducing rapid tool wear during edge trimming, which concurrently blunts the cutting edge and aggravates delamination and surface roughness. To overcome the limitation that flank wear (VB) cannot fully capture geometric changes of edge blunting, this study introduces the wedge angle (θ) as a new geometry-based wear indicator. CFRP edge trimming experiments were conducted for synchronous in-situ tracking of θ, cutting edge radius (Re) and VB, with polynomial regression adopted to analyze their correlations with delamination and surface roughness. Results reveal θ and Re predominantly govern delamination depth (h) at 45° fiber orientation. When θ increases from 67.8° to 81.9°, and Re increases from 7.72 µm to 38.65 µm, h explodes from 107 µm to 598 µm. The polynomial model (R2 = 0.91–0.92) further discloses a negative θ-Re interaction: the sensitivity of h to θ is amplified as Re increases. Regarding surface roughness, VB remains the most predictor of (Sa), yet θ dominates the maximum valley depth (Sv) at 45° fiber orientation, with Sv escalating from 84 µm to 210 µm as wear progresses. The established θ-Re-h and VB-Sa relationships provide a quantitative basis for predicting delamination and surface roughness and offer experimental evidence and quantitative insights for high-precision CFRP edge trimming.

Machining Science and Technology
Nanjing University of Aeronautics and Astronautics (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 24%
Advanced machining processes and optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon