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Applied Sciences
Special Issues
Smart Condition Monitoring and Maintenance in Mechanical Systems
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Smart Condition Monitoring and Maintenance in Mechanical Systems

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1185

Special Issue Editors

Prof. Dr. Haifeng Ma

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shandong University, Jinan 250061, China
Interests: vibration control; machining monitoring; high-precision motion control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhanqiang Liu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shandong University, Jinan 250061, China
Interests: structural vibration control; machining theory; tool technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qinghua Song

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shandong University, Jinan 250061, China
Interests: high-performance manufacturing; intelligent monitoring for machining process; structural vibration and control; biomedical machinery
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Liu

E-Mail Website
Guest Editor Assistant
School of Mechanical Engineering, Shandong University, Jinan 250061, China
Interests: signal processing; machine learning; complex nonlinear system control

Special Issue Information

Dear Colleagues,

New challenges and opportunities are posed in the advancement of signal and vision techniques, which benefits the intelligent diagnosis of mechanical systems. Therefore, this Special Issue is intended for the presentation of new ideas and experimental results in the field of smart condition monitoring and maintenance of mechanical systems from the design, service, and theory to their practical use.

Areas relevant to high smart monitoring and maintenance include, but are not limited to, signal and vision processing, novel algorithms and applications, artificial intelligence, machine learning, deep learning, and feature engineering. The mechanical systems include, but are not limited to, radar, rotor, gear, bearing, cutting tool, solar energy, and large rotary machinery.

This Special Issue will publish high-quality, original research papers, in the overlapping fields of:

  • Artificial intelligence, machine learning, and deep learning;
  • Stationary and nonstationary signal processing;
  • Image processing and machine vision;
  • Big data applications, algorithms, and systems;
  • Feature engineering;
  • Data mining.

Prof. Dr. Haifeng Ma
Prof. Dr. Zhanqiang Liu
Prof. Dr. Qinghua Song
Guest Editors

Dr. Yang Liu
Guest Editor Assistant

Certificates and awards:

When the Special Issue is closed, the Editorial Office will provide official certificates for all of the mentors. The young scholars involved in the program will be prioritized as candidates for Electronics Young Investigator Awards in the future.

If you are interested in this opportunity, please send your Special Issue proposal to the Applied Sciences Editorial Office ([email protected]), and we will discuss the process (mentor collaboration, Special Issue topic feasibility analysis, etc.) in further detail.

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

  • signal processing
  • machine learning
  • feature engineering
  • image processing
  • machine vision
  • deep learning

Published Papers (1 paper)

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Research

13 pages, 2891 KiB  
Article
A Novel Method for Predicting Residual Stress in GH4169 Machined Surfaces through Micro-Hardness Measurement
by Gonghou Yao, Zhanqiang Liu and Haifeng Ma
Appl. Sci. 2023, 13(24), 13257; https://doi.org/10.3390/app132413257 - 14 Dec 2023
Viewed by 769
Abstract
The presence of residual stress seriously affects the mechanical performance and reliability of engineering components. Here, the authors propose a novel method to determine corresponding residual stress through micro-hardness measurements of machined surfaces. In this study, a mathematical model with equal biaxial stress [...] Read more.
The presence of residual stress seriously affects the mechanical performance and reliability of engineering components. Here, the authors propose a novel method to determine corresponding residual stress through micro-hardness measurements of machined surfaces. In this study, a mathematical model with equal biaxial stress indentation is established. Then, the correlation of micro-hardness with indentation and residual stress is used to determine the prediction equation of residual stress. The material applied in this study is the nickel-based Superalloy GH4169. The residual stress prediction formula for Superalloy GH4169 is ultimately determined through the finite element method by subjecting the indentation to residual stress and fitting the experimental test data. The relationship between the indentation modulus and indentation depth is given quantitatively. The relationship between residual stress and hardness is given quantitatively. The prediction results show that the compressive residual stress can enhance the material hardness and make the contact deformation only require a low indentation depth to achieve complete plastic deformation. Conversely, the tensile residual stress can result in a deeper depth and less hardness at the initial stage of the fully plastic state. For the materials that yield more easily (small ratio of elastic modulus to yield strength), the effect is more evident. The model presented in this paper can accurately forecast corresponding residual stress through measurements of the micro-hardness of machined surfaces. Full article
(This article belongs to the Special Issue Smart Condition Monitoring and Maintenance in Mechanical Systems)
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