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Machines
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Safety of Machinery: Design, Monitoring, Manufacturing
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Safety of Machinery: Design, Monitoring, Manufacturing

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 8844

Special Issue Editor

Dr. Wennian Yu

E-Mail Website
Guest Editor
State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
Interests: machine dynamics; health monitoring; remaining useful life estimation

Special Issue Information

Dear Colleagues,

The safe operation of mechanical systems especially related to critical equipment is strictly demanded in practical industry applications. Therefore, studies regarding health monitoring, early defect detection, remaining useful life estimation and health management of machinery have received increasing attention both in industry and academia. Moreover, investigation of failure mechanisms and dynamic behavior as a consequence of initial defects or wear can also help to identify the root causes of machine failure and implement preventive actions for the safe operation of machines with high working reliability. This Special Issue is addressed to theoretical analyses/simulations and practical measures/techniques that center on solutions to machine safety problems, including, but not limited to, machine design, failure analysis, dynamics, reliability analysis, defect detection, health monitoring, diagnosis, remaining useful life estimation and health management.

Dr. Wennian Yu
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. 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

  • machine dynamic
  • failure analysis
  • defect detection
  • health monitoring
  • prognosis
  • reliability

Published Papers (3 papers)

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Research

22 pages, 7191 KiB  
Article
A Refined Dynamic Model for the Planetary Gear Set Considering the Time-Varying Nonlinear Support Stiffness of Ball Bearing
by Xiaodong Yang, Chaodong Zhang, Wennian Yu, Wenbin Huang, Zhiliang Xu and Chunhui Nie
Machines 2023, 11(2), 206; https://doi.org/10.3390/machines11020206 - 01 Feb 2023
Cited by 1 | Viewed by 1337
Abstract
Dynamics models of planetary gear sets (PGSs) are usually established to predict their dynamic behavior and load-sharing characteristics. The accurate modeling of bearing support stiffness is essential to study their dynamics. However, in most of the existing PGS dynamic models, the effect of [...] Read more.
Dynamics models of planetary gear sets (PGSs) are usually established to predict their dynamic behavior and load-sharing characteristics. The accurate modeling of bearing support stiffness is essential to study their dynamics. However, in most of the existing PGS dynamic models, the effect of characteristics coupling the rolling bearing time-varying nonlinear stiffness with the translational property of PGSs on the dynamic responses was completely neglected. To investigate this problem, a refined dynamic model for PGSs is proposed considering the coupled relationship between the radial translation of the rotating components and the time-varying nonlinear support stiffness of the ball bearing. The refined dynamic model simultaneously considers the coupled effect of the time-varying characteristic caused by the orbital motion of the rolling elements and the nonlinear characteristic caused by Hertzian contact between the rolling elements and raceways of the ball bearing. Comparisons between the simulations and experimental results are presented, which indicate that the PGS vibration spectrums yielded by the proposed time-varying nonlinear stiffness model are much closer to the actual scenarios than those of traditional models. The analysis results provide theoretical guidance for fault monitoring and diagnosis of the rolling bearings used in the PGS. Full article
(This article belongs to the Special Issue Safety of Machinery: Design, Monitoring, Manufacturing)
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27 pages, 2120 KiB  
Article
Machine Learning in CNC Machining: Best Practices
by Tim von Hahn and Chris K. Mechefske
Machines 2022, 10(12), 1233; https://doi.org/10.3390/machines10121233 - 16 Dec 2022
Cited by 4 | Viewed by 5779
Abstract
Building machine learning (ML) tools, or systems, for use in manufacturing environments is a challenge that extends far beyond the understanding of the ML algorithm. Yet, these challenges, outside of the algorithm, are less discussed in literature. Therefore, the purpose of this work [...] Read more.
Building machine learning (ML) tools, or systems, for use in manufacturing environments is a challenge that extends far beyond the understanding of the ML algorithm. Yet, these challenges, outside of the algorithm, are less discussed in literature. Therefore, the purpose of this work is to practically illustrate several best practices, and challenges, discovered while building an ML system to detect tool wear in metal CNC machining. Namely, one should focus on the data infrastructure first; begin modeling with simple models; be cognizant of data leakage; use open-source software; and leverage advances in computational power. The ML system developed in this work is built upon classical ML algorithms and is applied to a real-world manufacturing CNC dataset. The best-performing random forest model on the CNC dataset achieves a true positive rate (sensitivity) of 90.3% and a true negative rate (specificity) of 98.3%. The results are suitable for deployment in a production environment and demonstrate the practicality of the classical ML algorithms and techniques used. The system is also tested on the publicly available UC Berkeley milling dataset. All the code is available online so others can reproduce and learn from the results. Full article
(This article belongs to the Special Issue Safety of Machinery: Design, Monitoring, Manufacturing)
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13 pages, 4006 KiB  
Article
A Nonlinear Vibration Control of a String Using the Method Based on Its Time-Varying Length
by Jiahui Wang, Jing Liu and Guang Pan
Machines 2022, 10(11), 981; https://doi.org/10.3390/machines10110981 - 26 Oct 2022
Cited by 1 | Viewed by 1161
Abstract
Strings are common components in various mechanical engineering applications, such as transmission lines, infusion pipes, stay cables in bridges, and wire rope of elevators. The string vibrations can affect the stability and accuracy of systems. In this paper, a time-varying string length method [...] Read more.
Strings are common components in various mechanical engineering applications, such as transmission lines, infusion pipes, stay cables in bridges, and wire rope of elevators. The string vibrations can affect the stability and accuracy of systems. In this paper, a time-varying string length method is studied for string vibration suppression. A dynamic model of a string with the time-varying length is formulated. The dimensionless variables are introduced into the nonlinear dynamic model to realize the separation of time and space variables. The finite difference method is used to solve the differential equations of time functions. The vibration characteristics of time-varying length string are analyzed, such as the free vibrations, forced vibrations and damping effect. The influences of the length time-varying frequency and length time-varying range on the suppression performances are discussed. The results show that the time-varying string length method can effectively disperse the resonance peak energy and suppress multimodal resonance at the same time. The suppression performance is better for the time-varying length string with a higher time-varying frequency and a higher time-varying range. Full article
(This article belongs to the Special Issue Safety of Machinery: Design, Monitoring, Manufacturing)
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