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Recent Advances in Autonomous Systems and Robotics, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 30 October 2024 | Viewed by 1250

Special Issue Editor


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Guest Editor
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligence manufacturing and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To cope with non-programmed or non-preset situations, autonomous technology can use multi-source sensors and complex software to make systems with limited or no communication endure for long periods, and these systems can automatically adjust to an unknown environment, independently complete tasks, and maintain good performance. Autonomous systems and robotics are interdisciplinary fields involving real-time detection, information processing, comprehensive analysis, intelligent judgment, robust control, etc. With the continuous improvement of technical complexity, the possibility of system failure, vulnerability, and overall loss of function will also increase. Most applications still require the combination of human and autonomous systems to complete different tasks, so intelligent and unmanned systems are still challenging in current research.

The aim of this Special Issue is to celebrate the recent advances in autonomous systems and robotics and to promote the exchange and development of modern technologies, methods, and theories. We welcome authors to submit original research papers, perspectives, reviews, and mini-reviews. Areas to be covered in this Special Issue may include, but are not limited to, machine vision; machine learning and deep learning; artificial intelligence technology; fault detection and diagnosis; and intelligent robots.

Prof. Dr. Xinhua 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 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

  • machine learning
  • artificial intelligence
  • intelligent manufacturing
  • theoretical model
  • data processing
  • performance optimization
  • experimental analysis
  • robotics

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Published Papers (1 paper)

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Research

15 pages, 8494 KiB  
Article
Study on L-Bending Springback of 45 Steel Leather Cutting Tool Coupled with Local Induction Heating
by Yuan Cheng, Heran Geng, Chao Cao, Abul Fazal M. Arif, Xinhua Liu and Junfeng Yuan
Appl. Sci. 2024, 14(14), 6253; https://doi.org/10.3390/app14146253 - 18 Jul 2024
Viewed by 552
Abstract
Springback error is a major obstacle in L-bending sheets via cold working. Although thermal processing can effectively reduce the springback phenomenon, it is challenging to heat the sheet globally due to the influence of working conditions. Therefore, this study applied local induction heating [...] Read more.
Springback error is a major obstacle in L-bending sheets via cold working. Although thermal processing can effectively reduce the springback phenomenon, it is challenging to heat the sheet globally due to the influence of working conditions. Therefore, this study applied local induction heating to reduce the springback of 45 steel sheets and investigated the effects of bending parameters on springback behavior. Initially, a thermodynamic coupling model and a static model were created utilizing the VOCE hardening model and the von Mises yield criterion in the ANSYS workbench 2022 R2 software. The springback behavior and stress distribution of the sheets were then investigated under different temperatures (room temperature and 800 °C) and bending angles (15°, 30°, 45°, 60°, 75°, and 90°). Simultaneously, the experiments were performed to investigate springback behavior and guarantee the accuracy of the model. The results indicate that the springback reached a minimum value at the 45° bending angle at room temperature while increasing with the increasing bending angle under 800 °C local heating. The springback under local heating can be decreased by 75.2% and the error of the bending angle can improve by 2–7° compared with samples processed in room temperature. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Systems and Robotics, 2nd Edition)
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