Intelligent Monitoring, Control and Manufacturing in Coating Technologies

A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Functional Polymer Coatings and Films".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 752

Special Issue Editor


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Guest Editor
Packaging Engneering and Digital MediaTechnology, Xi'an University of Technology, Xi'an 710048, China
Interests: decoupling control of complex electromechanical systems; intelligent control and robotics technology; cloud resource optimization configuration technology; coatings technology

Special Issue Information

Dear Colleagues,

Coating technologies are fundamental to modern industry, underpinning lithium-ion battery manufacturing , flexible electronics, functional packaging, and protective layers. As the demand for higher quality, precision, and efficiency intensifies, traditional empirical methods are increasingly inadequate. The next frontier in this field lies in the integration of intelligent systems—leveraging data-driven approaches, advanced sensors, and computational algorithms to achieve unprecedented control over the coating process.

This Special Issue, “Intelligent Monitoring, Control and Manufacturing in Coating Technologies”, aims to compile cutting-edge research and innovative developments at the intersection of coating science and intelligent systems. We invite contributions that explore the application of machine learning, computer vision, real-time sensor data analysis, and advanced control theories to address critical challenges in coating manufacturing.

Topics of interest include, but are not limited to, the following:

  • In-line monitoring and defect detection for coatings using optical or spectral systems;
  • Machine learning and AI-based models for predicting coating quality, thickness, and uniformity;
  • Advanced control strategies (e.g., adaptive, model predictive control) for roll-to-roll (R2R) and slot-die coating systems;
  • Optimization algorithms for process parameters to enhance product quality and reduce waste;
  • Digital twin frameworks for virtual commissioning and real-time simulation of coating processes;
  • Novel sensor technologies and data fusion techniques for comprehensive process characterization;
  • Advanced fault diagnosis technologies for coating machines;
  • Intelligent manufacturing technology for coating machines.

We look forward to receiving your contributions.

Dr. Shanhui Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Coatings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • roll-to-roll process
  • coatings technology
  • intelligent monitoring

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Published Papers (1 paper)

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Research

17 pages, 5035 KB  
Article
An Improved Cascade R-CNN-Based Fastener Detection Method for Coating Workshop Inspection
by Jiaqi Liu, Shanhui Liu, Yuhong Chen, Jiawen Zhao and Jiahao Fu
Coatings 2026, 16(1), 37; https://doi.org/10.3390/coatings16010037 - 30 Dec 2025
Viewed by 515
Abstract
To address the challenges of small fastener targets, complex backgrounds, and the low efficiency of traditional manual inspection in coating workshop scenarios, this paper proposes an improved Cascade R-CNN-based fastener detection method. A VOC-format dataset was constructed covering three target categories—Marking-painted fastener, Fastener, [...] Read more.
To address the challenges of small fastener targets, complex backgrounds, and the low efficiency of traditional manual inspection in coating workshop scenarios, this paper proposes an improved Cascade R-CNN-based fastener detection method. A VOC-format dataset was constructed covering three target categories—Marking-painted fastener, Fastener, and Fallen off—which represents typical inspection scenarios of coating equipment under diverse operating conditions and enhances the adaptability of the model. Within the Cascade R-CNN framework, three improvements were introduced: the Convolutional Block Attention Module (CBAM) was integrated into the ResNet-101 backbone to enhance feature representation of small objects; anchor scales were reduced to better align with the actual size distribution of fasteners; and Soft-NMS was adopted in place of conventional NMS to effectively reduce missed detections in overlapping regions. Experimental results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 96.60% on the self-constructed dataset, with both Precision and Recall exceeding 95%, significantly outperforming Faster R-CNN and the original Cascade R-CNN. The method enables accurate detection and missing-state recognition of fasteners in complex backgrounds and small-object scenarios, providing reliable technical support for the automation and intelligence of printing equipment inspection. Full article
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