Evidence map›Paper›PMID 42354724›Full record

ArticleMicromachines2026

Real-Time Cutting Temperature Monitoring and Tool Wear Prediction with Integrated Thin-Film Thermocouples and Coupled Simulation.

Yingyuan Luo, Fenghao Zuo, Binghai Lyu, Xueliang Zhang, Xianfan Ge

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

5 authors.

Yingyuan LuoSchool of Intelligent Manufacturing, Hangzhou Polytechnic, Hangzhou 311402, China.
Fenghao ZuoSchool of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Binghai LyuHangzhou Runde Wheel Manufacturing Co., Ltd., Hangzhou 311407, China.ORCID 0000-0002-1628-1084
Xueliang ZhangSchool of Intelligent Manufacturing, Hangzhou Polytechnic, Hangzhou 311402, China.
Xianfan GeShanghai Waigaoqiao Shipbuilding Co., Ltd., Shanghai 200137, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate measurement of the temperature in the cutting zone is essential for closed-loop machining. However, it remains difficult due to the small size of the tool-chip contact area, its partial concealment by chips and the steep thermal gradients present. This study presents an integrated framework that combines a thin-film thermocouple (TFTC) on the rake face of a polycrystalline cubic boron nitride (PCBN) tool with a thermo-mechanical wear-coupled simulation in order to monitor cutting temperature and predict tool wear. The three-dimensional finite-element turning model includes a moving heat source that represents plastic and frictional heat at the tool-chip interface, as well as an Archard-type wear law that is enhanced by a temperature correction factor. The TFTC is fabricated by magnetron sputtering NiCr and NiSi films onto an insulating layer, after which it is embedded in the tool as a minimally intrusive in situ sensor. Turning experiments on AISI 1045 steel were performed at spindle speeds of 1000-3000 rpm, feeds of 0.05-0.20 mm/rev and depths of cut ranging from 0.3 to 1.0 mm under dry, wet (emulsion) and cryogenic (liquid nitrogen) cooling conditions. Simulated temperature fields reveal strong localisation at the tool-chip contact and a nonlinear increase in peak rake-face temperature with spindle speed, which fits a quadratic regression with R

Indexed as

archard modelcryogenic coolingcutting temperaturefinite element simulationthin-film thermocoupletool wear

Identifiers

PMID42354724
PMCPMC13304413

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.