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Machines
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Compensation for Geometric Errors and Improvement in Accuracy Through Innovative...
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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: 31 August 2025 | Viewed by 3108

Special Issue Editor

Dr. Bo-Lin Jian

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

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

  • 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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18 pages, 2132 KiB  
Article
Intelligent Casting Quality Inspection Method Integrating Anomaly Detection and Semantic Segmentation
by Min-Chieh Chen, Shih-Yu Yen, Yue-Feng Lin, Ming-Yi Tsai and Ting-Hsueh Chuang
Machines 2025, 13(4), 317; https://doi.org/10.3390/machines13040317 - 13 Apr 2025
Viewed by 225
Abstract
Wind power generation plays an important role in renewable energy, and the core casting components have extremely high requirements for precision and quality. In actual practice, we found that an insufficient workforce limits traditional manual inspection methods and often creates difficulty in unifying [...] Read more.
Wind power generation plays an important role in renewable energy, and the core casting components have extremely high requirements for precision and quality. In actual practice, we found that an insufficient workforce limits traditional manual inspection methods and often creates difficulty in unifying quality judgment standards. Customized optical path design is often required, especially when conducting internal and external defect inspections, which increases overall operational complexity and reduces inspection efficiency. We developed an automated optical inspection (AOI) system to address these challenges. The system integrates a semantic segmentation neural network to handle external surface detection and an anomaly detection model to detect internal defects. In terms of internal defect detection, the GC-AD-Local model we tested achieved 100% accuracy on experimental images, and the results were relatively stable. In the external detection part, we compared five different semantic segmentation models and found that MobileNetV2 performed the best in terms of average accuracy (65.8%). It was incredibly stable when dealing with surface defects with significant shape variations, and the prediction results were more consistent, making it more suitable for introduction into actual production line applications. Overall, this AOI system boosts inspection efficiency and quality consistency, reduces reliance on manual experience, and is of great assistance in quality control and process intelligence for wind power castings. We look forward to further expanding the amount of data and improving the generalization capabilities of the model in the future, making the system more complete and suitable for practical applications. Full article
(This article belongs to the Special Issue Compensation for Geometric Errors and Improvement in Accuracy Through Innovative Design and Advanced Control Strategies)
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21 pages, 1908 KiB  
Article
Rolling Mill Looper-Tension Control for Suppression of Strip Thickness Deviation by Adaptive PI Controller with Uncertain Forward/Backward Slip
by Yu-Chan Huang and Chao-Chung Peng
Machines 2025, 13(3), 238; https://doi.org/10.3390/machines13030238 - 16 Mar 2025
Viewed by 289
Abstract
The looper-tension control is a crucial aspect of a hot strip finishing mill. It involves a highly nonlinear system with strong states coupling and uncertainty, and the performance directly impacts the thickness deviation, which is the most critical product index. From the system [...] Read more.
The looper-tension control is a crucial aspect of a hot strip finishing mill. It involves a highly nonlinear system with strong states coupling and uncertainty, and the performance directly impacts the thickness deviation, which is the most critical product index. From the system dynamics, it is known that tension is highly sensitive to the strip velocity variation, which is typically unmeasurable. Instead, it needs to be calculated through work roll speed and strip slip which contains uncertainties, negatively affecting tension control performance. First, a feedback linearization-based proportional–integral (PI) controller design approach is proposed for the hot rolling looper-tension system. Second, to reduce the impact of speed uncertainties and enhance thickness response, an adaptive PI controller is introduced. Validation was conducted by numerical simulations; the result indicates that an adaptive PI controller reduces the magnitude of thickness variation and shortens the duration of its impact, verifying the consistency between theoretical derivation. The proposed control method effectively addresses the impact of uncertainties encountered in real-world applications. Additionally, it simplifies control parameter adjustment in practical use, reduces testing time, and improves product quality. Full article
(This article belongs to the Special Issue Compensation for Geometric Errors and Improvement in Accuracy Through Innovative Design and Advanced Control Strategies)
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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 1024
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
(This article belongs to the Special Issue Compensation for Geometric Errors and Improvement in Accuracy Through Innovative Design and Advanced Control Strategies)
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Review

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57 pages, 12798 KiB  
Review
Advances in Computer Numerical Control Geometric Error Compensation: Integrating AI and On-Machine Technologies for Ultra-Precision Manufacturing
by Yassmin Seid Ahmed and Fred Lacerda Amorim
Machines 2025, 13(2), 140; https://doi.org/10.3390/machines13020140 - 12 Feb 2025
Cited by 1 | Viewed by 892
Abstract
Geometric inaccuracies in machine configuration and part specifications are a major source of errors in CNC machining. These discrepancies have long affected the quality of manufactured components and continue to be a key research area in academia and industry. Over the years, significant [...] Read more.
Geometric inaccuracies in machine configuration and part specifications are a major source of errors in CNC machining. These discrepancies have long affected the quality of manufactured components and continue to be a key research area in academia and industry. Over the years, significant efforts have been made to minimize these errors and enhance machining precision. Researchers have explored various methodologies to identify, measure, and compensate for spatial inaccuracies, improving accuracy in modern machining systems. This paper comprehensively reviews recent advancements in geometric error measurement and compensation techniques, particularly in five-axis machine tools. It examines the latest methods for detecting errors and explores volumetric error modeling approaches designed to enhance machining precision. This review highlights the growing role of emerging technologies, including on-machine measurement systems, machine learning algorithms, and digital twin frameworks, in improving real-time error detection and compensation strategies. Furthermore, advanced tools such as laser interferometry and hybrid software–hardware approaches are discussed for their potential to drive innovation in ultra-precision machining. This paper also addresses key challenges in achieving high volumetric accuracy and outlines future opportunities for improving CNC machining performance. Future research can enhance precision and reliability in modern manufacturing by integrating intelligent systems and advanced measurement techniques. Full article
(This article belongs to the Special Issue Compensation for Geometric Errors and Improvement in Accuracy Through Innovative Design and Advanced Control Strategies)
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