Monitoring and Control of Machining Process
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 66
Special Issue Editors
Interests: machining process monitoring; control systems; computational intelligence; medical engineering
Interests: machine tools; machine vision; control systems; machine process monitoring; medical engineering
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 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
- 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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