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Advanced Manufacturing for Industry 4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 1202

Special Issue Editor


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Guest Editor
School of Mechanical Engineering, Dalian University of Technology, Dalian 116023, China
Interests: high-performance manufacturing; digital twin; assembly technology; numerical simulation; intelligent equipment; top-down design

Special Issue Information

Dear Colleagues,

Advanced manufacturing technology in the era of Industry 4.0 relies on breakthrough technologies such as the Internet of Things, cloud computing, artificial intelligence, virtual reality, and robotics, and integrates mechanical engineering, electronic technology, automation technology, and information technology to create an "intelligent factory" with high flexibility and resource utilization. Using artificial intelligence algorithms and combining production processes and systems to achieve intelligent production ultimately improves the product quality and efficiency and reduces production costs.

The purpose of this Special Issue is to collect and promote the latest research results related to Industry 4.0 and advanced manufacturing technology, with a focus on intelligent manufacturing technology throughout the entire process of product design, processing, and assembly. The focus is on advanced methods, theories, and application cases that combine mechanical manufacturing technology, computer science, information technology, artificial intelligence, control technology, and production processes. We are launching this Special Issue with the aim of seeking original articles from scientific and technological perspectives to further enhance the capabilities of advanced manufacturing technology, while also playing a critical role in educating students and young researchers.

Dr. Xiaokai Mu
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • advanced manufacturing technology
  • Industry 4.0
  • processing technology
  • information technology
  • digitalization technology
  • artificial intelligence
  • digital twin
  • robot technology
  • automation technology
  • smart factory

Published Papers (1 paper)

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Research

15 pages, 19605 KiB  
Article
FiMa-Reader: A Cost-Effective Fiducial Marker Reader System for Autonomous Mobile Robot Docking in Manufacturing Environments
by Xu Bian, Wenzhao Chen, Donglai Ran, Zhimou Liang and Xuesong Mei
Appl. Sci. 2023, 13(24), 13079; https://doi.org/10.3390/app132413079 - 7 Dec 2023
Viewed by 911
Abstract
Accurately docking mobile robots to various workstations on the factory floor is a common and essential task. The existing docking methods face three major challenges: intricate deployment procedures, susceptibility to ambient lighting, and incapacity to recognize product information during the docking process. This [...] Read more.
Accurately docking mobile robots to various workstations on the factory floor is a common and essential task. The existing docking methods face three major challenges: intricate deployment procedures, susceptibility to ambient lighting, and incapacity to recognize product information during the docking process. This paper devises a novel approach that combines the features of ArUco and Data Matrix to form a composite marker termed “DataMatrix-ArUco-Hybrid” (DAH). The DAH pattern serves as a fiducial marker capable of concurrently providing both target pose information and product information. Detection of the DAH pattern is conducted by a cost-effective fiducial marker reader system, called “FiMa-Reader”, which comprises an embedded processing unit and an infrared camera equipped with a 940 nm fill-light to overcome lighting issues. The FiMa-Reader system effectively detects the DAH pattern under both well-lit and dimly lit conditions. Additionally, the implementation of the FiMa-Reader system leads to significant improvements in positioning accuracy, including an 86.42% improvement on the x-axis, a 44.7% improvement on the y-axis, and an 84.21% improvement in angular orientation when compared to traditional navigation methods. The utilization of FiMa-Reader presents an economically viable system capable of guiding mobile robots’ positioning with high precision in various indoor lighting conditions. Full article
(This article belongs to the Special Issue Advanced Manufacturing for Industry 4.0)
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