Visual Measurement and Intelligent Robotic Manufacturing

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 627

Special Issue Editors


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Guest Editor
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: robotic machining; 3D optical measurement; multi-robot cooperative system

E-Mail Website
Guest Editor
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: the application of vision measurement; optimization method of robotic machining; compensation machining
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Special Issue Information

Dear Colleagues,

Intelligent robotic manufacturing is gradually becoming the new model for processing complex components. Intelligent robotic manufacturing equipment offers several technical advantages compared to CNC machines, including highly flexible movement, variable topological structures, and strong capabilities for multi-machine parallel cooperative operations, making it well-suited to more complex and variable processing environments. With the substantial integration of industrial robots and new-generation technologies such as 3D vision measurement and artificial intelligence algorithms, the flexibility, autonomy, and human–machine collaboration capabilities of robotic intelligent manufacturing systems will be significantly enhanced. This will greatly improve the precision, dexterity, and interactive capabilities of manufacturing systems, which are key directions for the advancement of intelligent manufacturing.

In this Special Issue, we seek recent findings on intelligent robotic manufacturing technologies. Authors should highlight advancements made in solving problems related to intelligent robotic manufacturing technologies. We aim to feature interdisciplinary perspectives and foster dialogue on the latest advancements in robotic machining as part of Machines' commitment to advancing knowledge in the field.

We are interested in contributions that focus on topics such as:

  1. Robotics, mechatronics, and manufacturing automation;
  2. Computer-integrated manufacturing;
  3. Intelligent multi-robot collaborative systems;
  4. Feedback control technology in complex robotic manufacturing;
  5. AI applications in robotic manufacturing;
  6. The calibration and optimization of robotic machining;
  7. Intelligent CNC machine tools combined with robots;
  8. The application of multimodal information in robotic machining systems, e.g., 3D measurement, vibration measurement, processing force measurement, etc.

Prof. Dr. Wenlong Li
Dr. Wei Xu
Guest Editors

Manuscript Submission Information

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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. Machines 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 2400 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

  • robotics
  • artificial intelligence
  • visual measurement
  • intelligent manufacturing
  • automatic machining systems

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Published Papers (1 paper)

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Research

25 pages, 21105 KiB  
Article
A Composite Vision-Based Method for Post-Assembly Dimensional Inspection of Engine Oil Seals
by Yu Li, Jing Zhao, Xingyu Gao, Weiming Li, Rongtong Jin, Guohao Tang, Yang Huang and Shuibiao Chen
Machines 2025, 13(4), 261; https://doi.org/10.3390/machines13040261 - 22 Mar 2025
Viewed by 211
Abstract
Addressing the challenge of manual dependency and the difficulty in automating the online detection of height discrepancies following engine oil seal assembly, this paper proposes a composite vision-based method for the post-assembly size inspection of engine oil seals. The proposed method enables non-contact, [...] Read more.
Addressing the challenge of manual dependency and the difficulty in automating the online detection of height discrepancies following engine oil seal assembly, this paper proposes a composite vision-based method for the post-assembly size inspection of engine oil seals. The proposed method enables non-contact, online three-dimensional measurement of oil seals already installed on the engine. To achieve accurate positioning of the inner and outer ring regions of the oil seals, the process begins with obtaining the center point and the major and minor axes through ellipse fitting, which is performed using progressive template matching and the least squares method. After scaling the ellipse along its axes, the preprocessed image is segmented using the peak–valley thresholding method to generate an annular ROI (region of interest) mask, thereby reducing the complexity of the image. By integrating three-frequency four-step phase-shifting profilometry with an improved RANSAC (random sample consensus)-based plane fitting algorithm, the height difference between the inner and outer rings as well as the press-in depth are accurately calculated, effectively eliminating interference from non-target regions. Experimental results demonstrate that the proposed method significantly outperforms traditional manual measurement in terms of speed, with the relative deviations of the height difference and press-in depth confined within 0.33% and 1.45%, respectively, and a detection success rate of 96.35% over 1415 samples. Compared with existing methods, the proposed approach not only enhances detection accuracy and efficiency but also provides a practical and reliable solution for real-time monitoring of engine oil seal assembly dimensions, highlighting its substantial industrial application potential. Full article
(This article belongs to the Special Issue Visual Measurement and Intelligent Robotic Manufacturing)
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