Evidence map›Paper›PMID 33649693›Full record

ArticleJournal of manufacturing systems2021

A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19.

Qibing Lv, Rong Zhang, Xuemin Sun, Yuqian Lu, Jinsong Bao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of manufacturing systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Review
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  5. Review
  6. Digital Twin for a Collaborative Painting Robot.Sensors (Basel, Switzerland) · 2022
    Article
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  8. Article
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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.

Qibing LvCollege of Mechanical Engineering, Donghua University, Shanghai, 201620, China.
Rong ZhangCollege of Mechanical Engineering, Donghua University, Shanghai, 201620, China.
Xuemin SunCollege of Mechanical Engineering, Donghua University, Shanghai, 201620, China.
Yuqian LuDepartment of Mechanical Engineering, The University of Auckland, 0632, New Zealand.
Jinsong BaoCollege of Mechanical Engineering, Donghua University, Shanghai, 201620, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the wake of COVID-19, the production demand of medical equipment is increasing rapidly. This type of products is mainly assembled by hand or fixed program with complex and flexible structure. However, the low efficiency and adaptability in current assembly mode are unable to meet the assembly requirements. So in this paper, a new framework of human-robot collaborative (HRC) assembly based on digital twin (DT) is proposed. The data management system of proposed framework integrates all kinds of data from digital twin spaces. In order to obtain the HRC strategy and action sequence in dynamic environment, the double deep deterministic policy gradient (D-DDPG) is applied as optimization model in DT. During assembly, the performance model is adopted to evaluate the quality of resilience assembly. The proposed framework is finally validated by an alternator assembly case, which proves that DT-based HRC assembly has a significant effect on improving assembly efficiency and safety.

Indexed as

AssemblyCOVID-19Digital twinDouble deep deterministic policy gradientHuman-robot collaboration

Identifiers

PMID33649693
PMCPMC7904497

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.