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Advanced and Smart Manufacturing Processes and Machine Tool Technologies: 2nd Edition

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Mechanical Engineering".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 1002

Editors


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Guest Editor
Department of Manufacturing Techniques and Automation, Rzeszow University of Technology, 35-959 Rzeszów, Poland
Interests: multi-axis precision machining; monitoring and modelling of tool wear; difficult-to-cut materials; machining process optimisation; surface integrity; CAD/CAM systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies with a Seat in Prešov, Technical University of Košice, Prešov, Slovakia
Interests: industrial robotics; automation; collaborative robotics (cobots); cognitive robotics; process automation; AI in robotics; machine learning; predictive maintenance; autonomous decision-making; digital twins; cyber-physical systems; smart factories; IIoT integration; advanced sensors; machine vision; interoperability; sustainability; energy efficiency; Industry 4.0; Industry 5.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The improvement of existing and the development of new advanced and intelligent manufacturing processes, including the wide-ranging modernization of production, are now essential conditions for economic and social development. Advanced manufacturing processes include, in particular, multi-axis machining and the processing of difficult-to-cut materials, with applications across different industries. This contributes to the development of increasingly precise machinery and manufacturing processes, including technology. This makes the accuracy of modern products ever higher, and the use and refinement of automation promote continuous improvements in manufacturing efficiency. The next stage of innovation in industry, science, and technology fundamentally affects the progress of the manufacturing industry. Smart manufacturing is characterized by the accelerated integration of information technology and manufacturing, including modeling and AI, and has become a major trend in the development of today's industry.

This Special Issue aims to provide an overview of the latest developments in precision machining, with a particular focus on multi-axis milling and intelligent manufacturing. This issue aims to contribute to scientific and technological advances that will underpin improvements in the precision, efficiency, and reliability of machining and machine technology.

Potential topics include, but are not limited to: digital twin in a smart production line, CAD/CAM design and manufacturing, analysis of CNC machine tools and part manufacturing systems, including technology, precision machining (especially multi-axis machining), tool wear, and precision assembly.

Dr. Michał Gdula
Dr. Lucia Knapčíková
Dr. Jozef Husar
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-anonymized 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

  • precision machining
  • difficult-to-cut materials
  • multi-axis milling
  • tool wear
  • industrial engineering
  • Industry 5.0
  • composites
  • manufacturing engineering
  • engineering management
  • smart manufacturing systems

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Related Special Issue

Published Papers (2 papers)

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Research

16 pages, 1533 KB  
Article
Data-Driven Calibration Scheduling for Measuring Instruments—A Smart Management Architecture
by Marcel Behún, Gabriel Galgóci, Mária Kozlovská, Matúš Pohorenec and Annamária Behúnová
Appl. Sci. 2026, 16(15), 7675; https://doi.org/10.3390/app16157675 - 2 Aug 2026
Viewed by 330
Abstract
Reliable calibration of measuring instruments is a foundational yet under-reported layer of the smart-manufacturing stack: without traceable calibrations, no downstream process can guarantee the metrological quality of the data it consumes. This research asks whether an administratively managed calibration record base is sufficient [...] Read more.
Reliable calibration of measuring instruments is a foundational yet under-reported layer of the smart-manufacturing stack: without traceable calibrations, no downstream process can guarantee the metrological quality of the data it consumes. This research asks whether an administratively managed calibration record base is sufficient to support data-driven recalibration scheduling, and diagnoses the data-governance conditions such scheduling requires. A production multi-faculty university calibration database (3552 registered instruments, 371 calibration events, 2009–2025) is analyzed to characterize fleet inventory and compliance, quantify the historical recalibration late-rate with bootstrap and analytical 95% confidence intervals stratified by faculty and device type, benchmark supervised models against explicit baselines, and propose a transparent rule-based scheduler with an empirical faculty-level safety buffer. The historical late-rate is 47.8% (95% CI 34.1–61.9%), with exploratory inter-faculty heterogeneity, while supervised learning yields only a weak, uncertain signal and no useful deviation regression. Because 86.7% of the fleet lacks the baseline records needed to schedule it, the practical message for instrument managers is direct: completing the missing last-calibration and calibration-period fields is the highest-value first step, after which a transparent, data-governance-informed scheduler—not a black-box predictor—is the defensible deliverable, transferable to comparable research and manufacturing infrastructures. Full article
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18 pages, 4641 KB  
Article
Digital Transformation of Data Collection and Archiving in Manufacturing Processes Under Industry 4.0
by Rebeka Tauberová, Lucia Knapčíková and Peter Lazorík
Appl. Sci. 2026, 16(11), 5542; https://doi.org/10.3390/app16115542 - 2 Jun 2026
Viewed by 364
Abstract
The submitted paper focuses on linking recycled material processing with digital technologies for monitoring and managing production processes in the context of Industry 4.0 principles. Despite the rapid development of additive manufacturing and Industry 4.0 technologies, limited attention has been devoted to the [...] Read more.
The submitted paper focuses on linking recycled material processing with digital technologies for monitoring and managing production processes in the context of Industry 4.0 principles. Despite the rapid development of additive manufacturing and Industry 4.0 technologies, limited attention has been devoted to the integration of sustainable recycled materials with real-time digital monitoring and structured manufacturing data management. Existing studies often address either recycled materials or digital process monitoring separately, while their combined implementation in additive manufacturing environments remains insufficiently explored. The introductory part highlights polyvinyl butyral (PVB) recovered from post-consumer laminated glass and its potential application in additive manufacturing. The theoretical section provides an overview of current knowledge in the fields of additive manufacturing, circular economy, production, and digitization, forming a foundation for the practical part of the research. The practical section focuses on the design and implementation of a data collection system for additive manufacturing processes, enabling the real-time digital monitoring and evaluation of selected technological parameters. Previous research conducted by the authors addressed the preparation of recycled PVB filament; however, commercially available PVB filament was used in the present experimental study due to the limited laboratory-scale production capacity of recycled filament. Full article
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