Compensation for Geometric Errors and Improvement in Accuracy through Innovative Design and Advanced Control Strategies

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1281

Special Issue Editor


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Guest Editor
Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung City 41170, Taiwan
Interests: automated optical inspection; signal processing and control system; application of the artificial intelligence and optimization methods; deep learning; machine learning; artificial intelligence; control system
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on cutting-edge approaches to improving machine tool accuracy and precision across various domains. We invite submissions that address geometric error compensation, advanced control systems, high-precision developments in diverse fields, and image processing techniques for accuracy control and recognition.

Key topics of interest include, but are not limited to, the following:

  1. Innovative error compensation strategies for various machine tools.
  2. Design and implementation of advanced control systems for enhanced accuracy and stability.
  3. Novel approaches to high-precision development in manufacturing, metrology, and related fields.
  4. Image processing techniques for precision control and feature recognition.
  5. Artificial intelligence and machine learning applications in abnormal detection and compensation.
  6. Sensor integration and data fusion for improved accuracy.
  7. Thermal error modeling and compensation methods.
  8. Software solutions for real-time error correction.
  9. Precision-enhancing mechanical designs and materials.
  10. Case studies demonstrating significant improvements in machining accuracy.

We welcome original research articles, comprehensive reviews, and technical notes that contribute to the advancement of geometric error compensation and accuracy enhancement. Submissions should emphasize innovative designs, advanced control systems, or cutting-edge methodologies that push the boundaries of precision in their respective domains.

This special issue aims to provide a platform for researchers, engineers, and practitioners to share their latest findings and foster interdisciplinary collaboration in the pursuit of ever-higher levels of accuracy and precision in modern manufacturing and measurement systems.

Dr. Bo-Lin Jian
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. 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

  • geometric error compensation
  • advanced control systems
  • precision engineering
  • error modeling and prediction
  • real-time error correction
  • image processing for accuracy
  • sensor fusion for precision
  • thermal error compensation
  • control system

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Published Papers (1 paper)

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Research

15 pages, 3119 KiB  
Article
Fault Detection in Harmonic Drive Using Multi-Sensor Data Fusion and Gravitational Search Algorithm
by Nan-Kai Hsieh and Tsung-Yu Yu
Machines 2024, 12(12), 831; https://doi.org/10.3390/machines12120831 - 21 Nov 2024
Viewed by 659
Abstract
This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, [...] Read more.
This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions. Full article
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