Applied Sciences

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Special Issue Editor

Prof. Dr. Qiang Cheng

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Guest Editor

1. Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing 100124, China
2. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
Interests: machine tool; robots; intelligent manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

CNC machine tools and robots are basic yet important equipment that support manufacturing, medical treatment, and other industries. With the development of emerging technologies, such as additive manufacturing, Internet of Things, virtual real integration, material engineering, and artificial intelligence, the development of CNC machine tools and robots will also undergo disruptive changes. The innovative development of these emerging technologies has realized collaborative application, which has greatly improved the working accuracy and intelligence of CNC machine tools and robots.

In this Special Issue, we welcome articles that focus on emerging technologies and applications of machine tools and robot systems. Topics covered include digital twin systems for machine tools and robots, the dynamic integration and intelligent control of machine tools, state evaluation and monitoring technology of machine tools and robots, quality prediction and control technology based on machine learning, collision detection for manipulators, intelligent motion planning and trajectory optimization of robotics, intelligent autonomous or adaptive control of robotics, high-performance assembly, reliability design or precision retention design of machine tools, application of intelligent sensor in machine tools and robots, new materials in machine tools and robots, etc. These topics have important research significance for enterprises to improve production quality and efficiency, save energy, and reduce costs. We invite you to contribute research work on emerging technologies and applications of machine tools and robot systems.

Prof. Dr. Qiang Cheng
Guest Editor

Manuscript Submission Information

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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 tool
robots
digital twin system
state evaluation and monitoring technology
quality prediction and control technology
machine learning
intelligent motion planning and trajectory optimization
high-performance assembly
reliability design or precision retention design
intelligent sensor
new materials

Related Special Issue

Published Papers (1 paper)

Research

36 pages, 12510 KiB

Open AccessArticle

A Health Management Technology Based on PHM for Diagnosis, Prediction of Machine Tool Servo System Failures

by Qiang Cheng, Yong Cao, Zhifeng Liu, Lingli Cui, Tao Zhang and Lei Xu

Appl. Sci. 2024, 14(6), 2656; https://doi.org/10.3390/app14062656 - 21 Mar 2024

Viewed by 528

Abstract

The computer numerically controlled (CNC) system is the key functional component of CNC machine tool control systems, and the servo drive system is an important part of CNC systems. The complex working environment will lead to frequent failure of servo drive systems. Taking effective health management measures is the key to ensure the normal operation of CNC machine tools. In this paper, the comprehensive effect of fault prediction and fault diagnosis is considered for the first time, and a health management system for machine tool servo drive systems is proposed and applied to operation and maintenance management. According to the data collected by the system and related indicators, the technology can predict the state trend of equipment operation, identify the hidden fault characteristics in the data, and further diagnose the fault types. A health management system mainly includes fault prediction and fault diagnosis. The core of fault prediction is the gated recurrent unit (GRU). The attention mechanism is introduced into a GRU neural network, which can solve the long-term dependence problem and improve the model performance. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. The core of fault diagnosis is the self-organizing mapping (SOM) neural network, which performs cluster analysis on data with different characteristics, to realize fault diagnosis. In addition, feature standardization and principal component analysis (PCA) are introduced to balance the influence of different feature scales, enhance the feature of fault data, and achieve data dimensionality reduction. Compared with the other two algorithms and their improved versions, the superiority of the health management system with high-dimensional data and the enhancement effect of fault identification are verified. The relative relationship between fault prediction and diagnosis is further revealed, and the adjustment idea of the production plan is provided for decision makers. The rationality and effectiveness of the system in practical application are verified by a series of tests of fault data sets. Full article

(This article belongs to the Special Issue Emerging Technologies and Applications of Machine Tools and Robot Systems, 2nd Edition)

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