Evidence map›Paper›PMID 39001020›Full record

ArticleSensors (Basel, Switzerland)2024

Advanced Monitoring of Manufacturing Process through Video Analytics.

Nisar Hakam, Khaled Benfriha, Vincent Meyrueis, Cyril Liotard

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Nisar HakamArts et Métiers, Institute of Technology (AMIT), 75013 Paris, France.ORCID 0000-0002-7820-3356
Khaled BenfrihaArts et Métiers, Institute of Technology (AMIT), 75013 Paris, France.ORCID 0000-0002-5560-021X
Vincent MeyrueisArts et Métiers, Institute of Technology (AMIT), 75013 Paris, France.ORCID 0000-0002-8713-4946
Cyril LiotardERM Automatismes, 84200 Carpentras, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.

Indexed as

data deviationdetectiondigitizationindustry 4.0machinevideo analyticsvisual

Identifiers

PMID39001020
PMCPMC11244419

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.