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Machines
Special Issues
Monitoring and Control of Machining Processes
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Monitoring and Control of Machining Processes

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1634

Special Issue Editors

Prof. Dr. Danko Brezak

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Guest Editor
Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Vana Lučića 5, 10002 Zagreb, Croatia
Interests: machining process monitoring; control systems; computational intelligence; medical engineering
Dr. Tomislav Staroveški

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Vana Lučića 5, 10002 Zagreb, Croatia
Interests: machine tools; machine vision; control systems; machine process monitoring; medical engineering
Dr. Miho Klaić

E-Mail Website
Guest Editor
The Faculty of Maritime Studies, University of Dubrovnik, Branitelja, Dubrovnika 41, 20000 Dubrovnik, Croatia
Interests: machine tools; machine process monitoring; stone processing

Special Issue Information

Dear Colleagues,

The pursuit of greater productivity, efficiency, and quality in manufacturing has made real-time monitoring and adaptive control indispensable for optimizing machining processes. While traditional methods rely on operator experience and periodic inspections, advancements in sensor technologies, data analytics, AI, and automation have revolutionized machining process monitoring and control, making them integral to smart manufacturing systems.

In these systems, multi-sensor networks and data acquisition tools continuously measure and analyze key process signals such as temperature, vibration, acoustic emission, cutting forces, or servomotor currents. When processed through AI-driven algorithms, these data enable real-time process monitoring. Adaptive control systems further refine operations by dynamically adjusting parameters, improving output quality while maximizing productivity.

Beyond performance gains, predictive analytics powered by machine learning and IIoT help anticipate equipment failures, minimizing downtime and reinforcing production reliability. These capabilities are pivotal for fully automated or dark factories, where human involvement is minimal. Sustainability is also a critical aspect of modern machining. As industries prioritize eco-conscious practices, energy consumption monitoring and green machining techniques have become essential for reducing environmental impact.

This Special Issue explores cutting-edge advancements in machining monitoring and control, with a focus on the following:

  • Sensor fusion and intelligent data acquisition;
  • Real-time tool and surface condition monitoring;
  • Predictive maintenance and fault diagnostics;
  • AI-driven process optimization;
  • Energy-efficient and sustainable machining;
  • Digital twins and Industry 4.0 integration.

By bridging innovation with industrial application, this collection aims to shape the future of intelligent, resilient, and sustainable manufacturing.

Prof. Dr. Danko Brezak
Dr. Tomislav Staroveški
Dr. Miho Klaić
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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

  • smart manufacturing
  • real-time process monitoring
  • adaptive control systems
  • predictive maintenance (Industry 4.0)
  • AI-driven manufacturing
  • sustainable machining
  • digital twin technology
  • sensor fusion (IIoT)

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

Published Papers (2 papers)

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24 pages, 67497 KB  
Article
A Physics-Guided Dual-Stream Vibration Feature Fusion Network for Chatter-Induced Surface Mark Diagnosis in Wafer Thinning
by Heng Li, Hua Liu, Liang Zhu, Xiangyu Zhao, Lemiao Qiu and Shuyou Zhang
Machines 2026, 14(4), 404; https://doi.org/10.3390/machines14040404 - 7 Apr 2026
Viewed by 445
Abstract
Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided [...] Read more.
Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided dual-stream attention fusion transfer network (PG-AFNet). First, a physics-guided signal preprocessing method was developed. Using variational mode decomposition (VMD) and continuous wavelet transform (CWT) masking, one-dimensional dynamic features and high-frequency regions of interest (ROIs) rich in transient impact features were extracted. Second, the PG-AFNet architecture was designed. By introducing an attention mechanism, it achieves deep integration of one-dimensional purely dynamic sequences with two-dimensional spatiotemporal visual textures to capture surface damage features caused by subtle vibrations. Finally, systematic validations were conducted using a real silicon wafer thinning dataset with 197 real samples. By overcoming small-sample limitations via physical augmentation, PG-AFNet achieved an 82.45% (86.64% after data augmentation) diagnostic accuracy, significantly outperforming traditional baselines. Furthermore, a large-scale cross-load validation on the diverse CWRU dataset yielded an exceptional 99.68% accuracy under mixed-load conditions, conclusively verifying the model’s robust domain generalization. Lastly, a rigorous ablation study explicitly quantified the indispensable contributions of the physics-guided dual-stream architecture and attention fusion. This research provides a feasible theoretical foundation for intelligent surface quality monitoring in semiconductor hard-brittle material processing. Full article
(This article belongs to the Special Issue Monitoring and Control of Machining Processes)
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23 pages, 3020 KB  
Article
Evaluation of Regression Models for Predicting Cutting Forces Based on Spindle Speed, Feed Speed and Milling Strategy During MDF Board Milling
by Tomáš Čuchor, Peter Koleda, Ján Šustek, Lukáš Štefančin, Richard Kminiak, Pavol Koleda and Zuzana Vyhnáliková
Machines 2026, 14(4), 359; https://doi.org/10.3390/machines14040359 - 25 Mar 2026
Viewed by 485
Abstract
This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. Unlike previous studies that focus primarily on force measurement, this work integrates experimental analysis with machine learning-based predictive modelling to [...] Read more.
This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. Unlike previous studies that focus primarily on force measurement, this work integrates experimental analysis with machine learning-based predictive modelling to improve process understanding and prediction accuracy. The main objective was to experimentally measure orthogonal cutting force components (Fx, Fy, Fz) and electrical power consumption under varying spindle speeds (14,000, 16,000 and 18,000 rpm), feed speed (6, 8 and 10 m/min), and milling strategies (conventional and climb), and to evaluate the suitability of the obtained data for predictive modelling. Cutting forces were measured using a Kistler 9257B piezoelectric dynamometer, and power consumption was recorded by a three-phase power quality analyser. Statistical analysis confirmed significant effects of machining parameters on force components, total cutting force, and power consumption. Spindle speed showed the strongest influence on total cutting force and power consumption, while milling strategy predominantly affected Fx and Fy components. Power consumption increased with increasing spindle speed. Based on the measured data, several machine learning models were developed to predict the total cutting force. The Fine Tree algorithm demonstrated the best performance, achieving validation metrics of R2 = 0.9 and RMSE = 0.60 (MSE = 0.36, MAE = 0.48), and improved testing performance with R2 = 0.95 and RMSE = 0.44 (MSE = 0.20, MAE = 0.36). After model comparison using RMSE, R2, training time, and model size, a Fine Tree model was identified as the most suitable, achieving high prediction accuracy without signs of overfitting. The results confirm that experimentally obtained data on cutting force and electrical energy consumption are suitable for reliable predictive modelling in CNC milling of MDF boards. However, it is necessary to work with those components that have the greatest dependence on speed, feed, or type of milling, and these are the force components measured on the x and y axes. Full article
(This article belongs to the Special Issue Monitoring and Control of Machining Processes)
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