ArticleSensors (Basel, Switzerland)2019
Towards Digital Twin Implementation for Assessing Production Line Performance and Balancing.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- Early Notice Pointer, an IoT-like Platform for Point-of-Care Feet and Body Balance Screening.Micromachines · 2022Article
- Digital Twins in the Practice of High-Energy Physics Experiments: A Gas System for the Multipurpose Detector.Sensors (Basel, Switzerland) · 2022Article
- Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning.Computational intelligence and neuroscience · 2022Article
- A Layered Middleware for OT/IT Convergence to Empower Industry 5.0 Applications.Sensors (Basel, Switzerland) · 2021Article
- Digital Twin-Driven Human Robot Collaboration Using a Digital Human.Sensors (Basel, Switzerland) · 2021Article
- Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study.Sensors (Basel, Switzerland) · 2021Article
- The Potential of a Digital Twin in Surgery.Surgical innovation · 2021Article
- A modular computational framework for medical digital twins.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Selection and Optimization of the Parameters of the Robotized Packaging Process of One Type of Product.Sensors (Basel, Switzerland) · 2020Article
- ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The optimization of production processes has always been one of the cornerstones for manufacturing companies, aimed to increase their productivity, minimizing the related costs. In the Industry 4.0 era, some innovative technologies, perceived as far away until a few years ago, have become reachable by everyone. The massive introduction of these technologies directly in the factories allows interconnecting the resources (machines and humans) and the entire production chain to be kept under control, thanks to the collection and the analyses of real production data, supporting the decision making process. This article aims to propose a methodological framework that, thanks to the use of Industrial Internet of Things-IoT devices, in particular the wearable sensors, and simulation tools, supports the analyses of production line performance parameters, by considering both experimental and numerical data, allowing a continuous monitoring of the line balancing and performance at varying of the production demand. A case study, regarding a manual task of a real manufacturing production line, is presented to demonstrate the applicability and the effectiveness of the proposed procedure.
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Registered trials
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.