Evidence map›Paper›PMID 40218656›Full record

ArticleSensors (Basel, Switzerland)2025

A Mold Damage Monitoring Algorithm for Power Metallurgy Molding Machines Using Bidirectional Long Short-Term Memory on an Internet of Things Platform.

Hao-Pu Lin, Yuan-Chieh Chen, Chin-Chuan Han, Yu-Chi Wu, Jin-Yuan Lin

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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.

Hao-Pu LinPh. D. Program in Material and Chemical Engineering, National United University, MiaoLi 360302, Taiwan.
Yuan-Chieh ChenDepartment of Computer Science and Information Engineering, National United University, MiaoLi 360302, Taiwan.
Chin-Chuan HanDepartment of Computer Science and Information Engineering, National United University, MiaoLi 360302, Taiwan.
Yu-Chi WuDepartment of Electrical Engineering, National United University, MiaoLi 360302, Taiwan.ORCID 0000-0003-2821-2040
Jin-Yuan LinDepartment of Electrical Engineering, National United University, MiaoLi 360302, Taiwan.

Funding

National Science and Technology Council, TAIWAN 111-2221-E-239-026
6 · The paper itself

Abstract

In this paper, an analysis and monitoring algorithm is proposed for mold health evaluation using vibration data. Two inertial measurement units (IMUs) and an embedded system are first used to acquire vibration data from a powder metallurgy molding machine. These data are collected on an Internet of Things (IoT) platform using the Message Queueing Telemetry Transport (MQTT) protocol. For data analysis, the vibration signal on the

Indexed as

deep learninginertial measurement unit (IMU)intelligence systemInternet of Things (IoT)mean square errorvibration data

Identifiers

PMID40218656
PMCPMC11991532

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.