Evidence map›Paper›PMID 42354769›Full record

ArticleMicromachines2026

Stability and Dynamics of Milling Process During Cutter-Workpiece Engagement and Disengagement Stages.

Jiawei Mei, Chengzhu Wu, Ye Jin, Luxuan Sun, Sunyi Liu, Yaoxuan Han, Yuyang Huang

Abstract read
In one paragraph

Article in Micromachines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jiawei MeiSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.
Chengzhu WuSchool of Aerospace, Faculty of Science & Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
Ye JinSchool of Aerospace, Faculty of Science & Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
Luxuan SunSchool of Aerospace, Faculty of Science & Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.ORCID 0009-0001-4275-6012
Sunyi LiuFaculty of Computer and Mathematical Sciences, The Hong Kong Polytechnic University, Hong Kong 999077, China.
Yaoxuan HanSchool of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.
Yuyang HuangSchool of Aerospace, Faculty of Science & Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.

Funding

Fundamental Research Funds for the Central Universities 2026JBMC012
6 · The paper itself

Abstract

In milling operations, cutters entering and exiting workpiece boundaries cause varying radial immersions and chip thicknesses. This generates aperiodic cutting forces that often induce vibrations and degrade surface quality. To address this, this study aims to accurately predict milling forces and surface profiles during these critical engagement and disengagement phases. An analytical approach was developed to estimate the changing distances between the cutting teeth and workpiece boundaries, enabling the precise calculation of the dynamic chip thickness as the cutter transitions through the material. Based on these dynamic calculations, milling forces and system responses were simulated. Experimental validation demonstrated a strong agreement between the simulated cutting forces, machined surface profiles, and real-world results. Notably, findings revealed that even cutting parameters deemed stable by traditional stability lobes can still trigger vibrations during these boundary transitions. Consequently, a novel parameter selection strategy is proposed to effectively prevent these transient vibrations, significantly enhancing the final surface finish. Ultimately, this comprehensive modelling framework provides a deeper understanding of the system dynamics throughout the entire milling process, offering high relevance for broader applications, such as optimising energy consumption, predicting tool wear, and improving machining parameter optimisation.

Indexed as

cutting forcesmilling dynamicstool disengagementtool engagement

Identifiers

PMID42354769
PMCPMC13303168

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.