Special Issue: Sensors in Machine Vision of Automated Systems for Intelligent Manufacturing

Machine vision is a disruptive technology that is transforming manufacturing and production lines across the globe. Over the years machine vision has assisted in building seamless quality control systems, particularly in the manufacturing sector. A lot of progress has been made ever since to select and develop an ideal and customised approach for every application. Through its integration with technologies like embedded vision, image processing systems, data transmission systems, deep learning, machine learning, CMOS sensors etc., machine vision can be leveraged to optimise the manufacturing processes at various levels. Industry 4.0 focuses on intelligent manufacturing systems. To meet this rising level of complexity in industrial automated systems, machine vision technologies are being upgraded and innovated. For instance, the chemical composition of the materials can be easily obtained with the help of hyperspectral imaging. Conventional imaging systems cannot reveal specific details. However computational imaging can be used to combine a series of images in different ways to provide information on various details that cannot be obtained through conventional methods. Overall sensors in machine vision can greatly enhance the intelligent manufacturing process in terms of automation, performance, and integration.

Machine vision cameras and sensors can be used to aid in manual assembly, add vision to the production line, use vision-guided robots, and enable embedded vision. Machine vision can be used to prevent errors in assembling objects, A stored image will be used by the machine vision system to analyse and check the assembled products for correctness. Errors or deviations will be notified to the operator immediately. The data collected can aid in traceability and further analysis. With the help of either single point, self-contained smart cameras or personal computer-based machine vision systems, the existing lines can be retrofitted or new ones can be built to meet the level of sophistication required. Vision-guided robots can effectively be used for pick and drop/place operations in an intelligent manufacturing unit. In addition, vision systems can be embedded into various equipment and manufacturing processes. This is possible with the help of ARM architecture and the availability of minute embedded processing boards.

List of topics to be covered in this special issue:

  • Novel multiresolution techniques for machine vision in a smart manufacturing facility.
  • Design and development of a biomimetic machine vision for automated systems in intelligent manufacturing.
  • Machine vision systems for smart orbital operations in space.
  • Innovative techniques to improve the predictive maintenance in intelligent manufacturing using machine vision.
  • Machine vision-based systems and solutions for bar code scanning.
  • New methods for enhancing the track and trace operations in an intelligent manufacturing unit with the help of machine vision.
  • Automated problem identification in assembly lines using sensors in machine vision.
  • Automated product classification system using machine vision for smartphone manufacturing.
  • Development of a scalable image coding system methodology for intelligent manufacturing.
  • A machine vision algorithm for quality control of 3D printed products.

Tentative Timeline for this special issue:

  • Manuscript Submission Date - April 1, 2025
  • Author Notification Date - Jun 25, 2025
  • Revised paper submission Date - Aug 20, 2025
  • Final acceptance Date - Sep 25, 2025

Guest Editor Credentials:

Dr. Muhammad Zakarya (Lead GE),

Assistant Professor,

Faculty of Computing and Information Technology,

Sohar University, Oman.

Dr. Santosh Tirunagari,

Assistant Professor,

Department of Computer Science,

School of Science and Technology,

Middlesex University, UK

Prof. Jinguang Han,

Professor,

School of Cyber Science and Engineering,

Southeast University, China

Dr. Peiying Zhang,

Associate Professor,

China University of Petroleum (East China), China