ArticleSensors (Basel, Switzerland)2019
Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
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- Machine learning approach to monitor inkjet jetting status based on the piezo self-sensing.Scientific reports · 2023Article
- A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and Classification.Sensors (Basel, Switzerland) · 2022Article
- On Smart Geometric Non-Destructive Evaluation: Inspection Methods, Overview, and Challenges.Materials (Basel, Switzerland) · 2022Review
- Quantum cyber-physical systems.Scientific reports · 2022Article
- Artificial Intelligence-Based Assistance System for Visual Inspection of X-ray Scatter Grids.Sensors (Basel, Switzerland) · 2022Article
- 3D Object Detection with SLS-Fusion Network in Foggy Weather Conditions.Sensors (Basel, Switzerland) · 2021Article
- Quantum Strategic Organizational Design: Alignment in Industry 4.0 Complex-Networked Cyber-Physical Lean Management Systems.Sensors (Basel, Switzerland) · 2020Article
- Towards Customer-Centric Additive Manufacturing: Making Human-Centered 3D Design Tools through a Handheld-Based Multi-Touch User Interface.Sensors (Basel, Switzerland) · 2020Article
- Impact of industry 4.0 to create advancements in orthopaedics.Journal of clinical orthopaedics and trauma · 2020Article
- Image-Processing-Based Low-Cost Fault Detection Solution for End-of-Line ECUs in Automotive Manufacturing.Sensors (Basel, Switzerland) · 2020Article
- Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes.Sensors (Basel, Switzerland) · 2020Article
- Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber-Physical Complex Networks.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully
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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.