Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Advanced and Smart Manufacturing Processes and Machine Tool Technologies
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced and Smart Manufacturing Processes and Machine Tool Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 6543

Special Issue Editors

Dr. Michał Gdula

E-Mail Website
Guest Editor
Department of Manufacturing Techniques and Automation, The 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
Dr. Lucia Knapčíková

E-Mail Website
Guest Editor
Faculty of Manufacturing Technologies, Technical University of Košice, 08001 Prešov, Slovakia
Interests: industrial engineering; mechanical engineering; industry 4.0; industry 5.0; ICT; composites; waste materials; manufacturing; circular economy
Special Issues, Collections and Topics in MDPI journals
Dr. Jozef Husar

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 development of new advanced and intelligent manufacturing processes, including the wide-ranging modernisation of production, are now essential conditions for economic and social development. Advanced manufacturing processes include, in particular, multi-axis machining and difficult-to-cut materials with various applications in 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 promotes continuous improvements in manufacturing efficiency. The next stage of innovation in industry, science and technology will fundamentally influence the development of the manufacturing industry. Smart manufacturing is characterised by the accelerated integration of information technology and manufacturing, including modelling and AI, and it has become a major trend in the development of today's industry.

This Special Issue will provide an overview of the latest developments in precision machining, with a particular focus on multi-axis milling and intelligent manufacturing. This Special Issue will also 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, the following: digital twin in a smart production line, CAD/CAM design and manufacturing, analysis of CNC machine tools and part manufacturing systems with technology, and precision machining (including 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-blind 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

18 pages, 8247 KB  
Article
Gripper Used in an Educational Mechatronic System Used for Characteristics Analysis of the Post-Cryogenic Treatment
by Edgar Moraru, Robert-Valentin Răbuga, Cristian-Gabriel Alionte, Eugenia Tanasă and Mircea-Iulian Nistor
Appl. Sci. 2026, 16(3), 1385; https://doi.org/10.3390/app16031385 - 29 Jan 2026
Viewed by 292
Abstract
This paper presents a gripper, part of the mechatronic system for positioning a parallelepiped sample from a cryogenic treatment system to the devices for evaluating and investigating the properties arising from the application of heat treatment. The system is part of a complex [...] Read more.
This paper presents a gripper, part of the mechatronic system for positioning a parallelepiped sample from a cryogenic treatment system to the devices for evaluating and investigating the properties arising from the application of heat treatment. The system is part of a complex educational framework that enables students to research the effects of combined treatments, where a component is selectively heat-treated in specific areas using a laser system, followed by cooling the entire component with a cryogenic system. Also, the investigation methodology performed by students is illustrated. The system allows structural changes to be investigated with other methods, and the paper exemplified the analysis of the material using the SEM-EDX and XRD systems. Full article
(This article belongs to the Special Issue Advanced and Smart Manufacturing Processes and Machine Tool Technologies)
►▼ Show Figures

Figure 1

29 pages, 3930 KB  
Article
KAN-Based Tool Wear Modeling with Adaptive Complexity and Symbolic Interpretability in CNC Turning Processes
by Zhongyuan Che, Chong Peng, Jikun Wang, Rui Zhang, Chi Wang and Xinyu Sun
Appl. Sci. 2025, 15(14), 8035; https://doi.org/10.3390/app15148035 - 18 Jul 2025
Viewed by 1560
Abstract
Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the [...] Read more.
Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov–Arnold representation theorem and Cybenko’s theorem, explain KANs’ ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners. Full article
(This article belongs to the Special Issue Advanced and Smart Manufacturing Processes and Machine Tool Technologies)
►▼ Show Figures

Figure 1

23 pages, 6966 KB  
Article
Optimizing Dual-Microstructure Parameters in Ball-End Milling Tools: Synergistic Effects and Parameter Combination Analysis
by Qinghua Li, Qingyu Guan, Yi Ji, Wenyang Xu and Tiantian Xu
Appl. Sci. 2025, 15(11), 6329; https://doi.org/10.3390/app15116329 - 4 Jun 2025
Viewed by 788
Abstract
To address the issues of high cutting speeds and low surface precision during milling, this study investigates the effects of front and back cutting face microstructures on ball-end milling cutters processing 304 stainless steel. Firstly, a theoretical energy model for front and back [...] Read more.
To address the issues of high cutting speeds and low surface precision during milling, this study investigates the effects of front and back cutting face microstructures on ball-end milling cutters processing 304 stainless steel. Firstly, a theoretical energy model for front and back cutting face microstructures is established to verify the feasibility of embedding microstructures. Then, finite element analyses are conducted on cutters with varying microstructure parameters on front and back cutting faces to determine reasonable parameter ranges. Parameter combinations are subsequently used to manufacture front/back microstructured cutters, which undergo FEA validation. Finally, milling experiments are designed with milling forces, tool wear, and workpiece surface roughness as evaluation metrics. The results demonstrate that front/back microstructured cutters reduce milling forces by 19.4%, cutting temperatures by 19%, and workpiece surface roughness (Sa) by 43% compared to non-microstructured cutters, while significantly mitigating tool wear. Full article
(This article belongs to the Special Issue Advanced and Smart Manufacturing Processes and Machine Tool Technologies)
►▼ Show Figures

Figure 1

20 pages, 18827 KB  
Article
Modeling and Measurement of Tool Wear During Angular Positioning of a Round Cutting Insert of a Toroidal Milling Tool for Multi-Axis Milling
by Michał Gdula, Lucia Knapčíková, Jozef Husár and Radoslav Vandžura
Appl. Sci. 2024, 14(22), 10405; https://doi.org/10.3390/app142210405 - 12 Nov 2024
Cited by 4 | Viewed by 1928
Abstract
The aim of this study was to develop a concept for an angular positioning method for a round cutting insert in a torus cutter body dedicated to the multi-axis milling process under high-speed machining cutting conditions. The method concept is based on a [...] Read more.
The aim of this study was to develop a concept for an angular positioning method for a round cutting insert in a torus cutter body dedicated to the multi-axis milling process under high-speed machining cutting conditions. The method concept is based on a developed wear model using a non-linear estimation method adopting a quasi-linear function. In addition, a tool life model was developed, taking into account the cutting blade work angle parameter, the laser marking method for the round cutting insert, and a wear measurement methodology. The developed tool wear model provides an accuracy of 90% in predicting the flank wear of the cutting blade. The developed procedure for angular positioning of the round cutting insert enables the entire cutting edge to be fully utilized, extending the total tool life. In addition, the measured largest defect values between the worn cutting edge and the nominal outline of the round cutting insert indicate the location of notching-type wear. Full article
(This article belongs to the Special Issue Advanced and Smart Manufacturing Processes and Machine Tool Technologies)
►▼ Show Figures

Figure 1

Other

Jump to: Research

19 pages, 629 KB  
Perspective
Quality in the Era of Industry 4.0—Quality Management Principles in the Context of the Fourth Industrial Revolution
by Adam Hamrol and Marta Grabowska
Appl. Sci. 2026, 16(4), 1919; https://doi.org/10.3390/app16041919 - 14 Feb 2026
Viewed by 928
Abstract
The dynamic development of Industry 4.0 technologies, referred to as smart manufacturing technologies (SMTs), is significantly changing both production systems and quality management practices. The aim of this article is to analyse the impact of smart manufacturing technologies on the seven principles of [...] Read more.
The dynamic development of Industry 4.0 technologies, referred to as smart manufacturing technologies (SMTs), is significantly changing both production systems and quality management practices. The aim of this article is to analyse the impact of smart manufacturing technologies on the seven principles of quality management (QMP). The research is based on a narrative, semi-systematic review of the literature from the Web of Science and Scopus databases from the last seven years, using thematic analysis. Traditional interpretations of QMP principles were compared with new conditions resulting from the implementation of technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, vision systems, virtual and augmented reality, and additive manufacturing. The results indicate that SMTs do not eliminate quality management principles, but significantly change the way they are implemented. There is a shift towards product personalisation, shorter product life cycles, decentralised decision-making, flexible and autonomous processes, digital surveillance, and intensive use of real-time data. The article argues that SMT and QMP are complementary approaches—technologies increase the effectiveness and efficiency of quality management, but do not replace it. The considerations presented here are a starting point for further empirical research on the new ‘Quality 4.0’ model in the intelligent manufacturing environment. Full article
(This article belongs to the Special Issue Advanced and Smart Manufacturing Processes and Machine Tool Technologies)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop