Intelligent Design and Manufacturing of Mechanical Equipment

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 147

Special Issue Editors


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Guest Editor
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Interests: intelligent design and manufacturing; industrial software; manufacturing equipment research and development

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Guest Editor
Institute for Steel Construction, Leibniz University Hannover, Hannover, Germany
Interests: welding technologies; residual stresses; digital twins for welding; large-scale metal additive manufacturing
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Special Issue Information

Dear Colleagues,

The new generations of information technologies such as 6G and the Internet of Things and artificial intelligence technologies such as deep learning and LLMs are experiencing explosive growth. Intelligent technologies are widely used in various aspects of equipment design and manufacturing. Among them, mechanical equipment, as the core of production and manufacturing, is continuously developing integrated innovation in its design and manufacturing systems, forming the main driving force of a new phase of industrial revolution. In particular, the new generation of intelligent manufacturing driven by artificial intelligence, as the core technology of the new industrial revolution, is triggering significant and profound changes in the design concept and manufacturing modes of the mechanical equipment industry, as well as in other aspects. It is reshaping the development paths, technological systems, and industrial format of mechanical equipment design and manufacturing, driving a new stage of development for the global mechanical equipment industry.

We invite you to submit original research or reviews on the "Intelligent Design and Manufacturing of Mechanical Equipment". Topics can include, but are not limited to, the following:

  • Intelligent Manufacturing Modes of Mechanical Equipment;
  • Intelligent Design Approaches and Applications of Mechanical Equipment;
  • Equipment and Systems for Intelligent Manufacturing of Mechanical Equipment;
  • Intelligent Manufacturing Processes of Mechanical Equipment;
  • AI-driven design;
  • AI CAD/CAM/CAE.

Dr. Bo Huang
Dr. Hessamoddin Moshayedi
Guest Editors

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. 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

  • intelligent manufacturing
  • AI-driven design
  • AI CAD/CAM/CAE

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Published Papers (1 paper)

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Research

21 pages, 3645 KB  
Article
A Novel Mechanism Analysis Method for the Robotic Grinding of a TC4 Workpiece Using Acoustic Emission Based on an Improved CCEEMD Algorithm
by Xiangye Zhu, Qi Liu, Liang Liang, Xiaohu Xu and Sijie Yan
Machines 2026, 14(5), 501; https://doi.org/10.3390/machines14050501 - 30 Apr 2026
Abstract
The instantaneous contact zone in robotic abrasive belt grinding involves highly coupled thermo-mechanical interactions between abrasive grains and the workpiece material. Acoustic Emission (AE) signals generated during this process are inherently nonlinear and nonstationary, posing challenges for accurate process monitoring and mechanistic understanding. [...] Read more.
The instantaneous contact zone in robotic abrasive belt grinding involves highly coupled thermo-mechanical interactions between abrasive grains and the workpiece material. Acoustic Emission (AE) signals generated during this process are inherently nonlinear and nonstationary, posing challenges for accurate process monitoring and mechanistic understanding. To address this, this study introduces an innovative AE signal processing framework designed to elucidate the robotic grinding mechanism for Ti-6Al-4V (TC4) titanium alloy. An improved Completely Complementary Ensemble Empirical Mode Decomposition (CCEEMD) algorithm, building upon Empirical Mode Decomposition (EMD), is developed to precisely extract intrinsic mode functions (IMFs) from raw AE data. Subsequently, a novel denoising algorithm utilizing noise statistical characteristics effectively removes invalid noise from the robotic machining system. Validation through robotic grinding experiments on TC4 workpieces successfully established quantifiable relationships between extracted AE features and the underlying grinding mechanism. Significantly, implementing this methodology contributed to extending the effective service life of a structured abrasive belt by approximately 20% while increasing machining efficiency by approximately 12%. This work presents a novel methodology combining improved CCEEMD and statistical denoising for AE analysis in robotic grinding, providing a robust link between AE signatures and material removal mechanisms, ultimately enabling quantitative process optimization. Full article
(This article belongs to the Special Issue Intelligent Design and Manufacturing of Mechanical Equipment)
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