Evidence map›Paper›PMID 35744458›Full record

ArticleMicromachines2022

Development of a Hybrid Intelligent Process Model for Micro-Electro Discharge Machining Using the TTM-MDS and Gaussian Process Regression.

Yanyan Chen, Xudong Guo, Guojun Zhang, Yang Cao, Dili Shen, Xiaoke Li, Shengfei Zhang, Wuyi Ming

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

8 authors.

Yanyan ChenCollege of Mechanical Engineering, Yellow River Conservancy Technical Institute, Kaifeng 475004, China.
Xudong GuoMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
Guojun ZhangGuangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Guangdong HUST Industrial Technology Research Institute, Dongguan 523808, China.
Yang CaoMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
Dili ShenSchool of Mechanical Electronics and Automobile Engineering, Zhengzhou University of Technology, Zhengzhou 450002, China.
Xiaoke LiMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
Shengfei ZhangSchool of Mechanical Electronics and Automobile Engineering, Zhengzhou University of Technology, Zhengzhou 450002, China.
Wuyi MingMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou 450002, China.

Funding

Henan Provincial key scientific and technological project 222102220011Henan Provincial Youth Backbone University Teacher Training Plan 2021GGJS090Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program 2017BT01G167
6 · The paper itself

Abstract

This paper proposed a hybrid intelligent process model, based on a hybrid model combining the two-temperature model (TTM) and molecular dynamics simulation (MDS) (TTM-MDS). Combined atomistic-continuum modeling of short-pulse laser melting and disintegration of metal films [Physical Review B, 68, (064114):1-22.], and Gaussian process regression (GPR), for micro-electrical discharge machining (micro-EDM) were also used. A model of single-spark micro-EDM process has been constructed based on TTM-MDS model to predict the removed depth (RD) and material removal rate (MRR). Then, a GPR model was proposed to establish the relationship between input process parameters (energy area density and pulse-on duration) and the process responses (RD and MRR) for micro-EDM machining. The GPR model was trained, tested, and tuned using the data generated from the numerical simulations. Through the GPR model, it was found that micro-EDM process responses can be accurately predicted for the chosen process conditions. Therefore, the hybrid intelligent model proposed in this paper can be used for a micro-EDM process to predict the performance.

Indexed as

Gaussian process regression (GPR)micro-EDMmolecular dynamics simulations (MDS)process modeling and simulation

Identifiers

PMID35744458
PMCPMC9227863

What OpenQuestion holds

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Registered trials

None linked

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.