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
Smart Manufacturing and Materials: 3rd Edition
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

Smart Manufacturing and Materials: 3rd Edition

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5555

Special Issue Editor

Prof. Dr. Marek Placzek

E-Mail Website
Guest Editor
Department of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: piezoelectric materials; vibration damping; energy harvesting; sensors; actuators; smart materials and their applications; pneumatic drives and their energy efficiency; electromagnetic compatibility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of industrial transformation aligned with the Industry 4.0 concept, modern manufacturing technologies and advanced materials are becoming a growing area of interest for scientists, engineers, and businesses alike. The pursuit of increased production efficiency, product customization, and flexible assortment changes presents new challenges for manufacturing industries. However, cutting-edge technologies and materials enable the rapid development of industry and are reshaping both the nature of work and the role of humans in production processes. Efficiency, flexibility, and production speed translate directly into enhanced competitiveness and reduced production costs.

To achieve these goals, modern manufacturing technologies are applied, including additive manufacturing, systems based on virtual and augmented reality, artificial intelligence, autonomous robots, simulations, and systems integration. The use of big data operations and cloud computing is becoming essential, while the Industrial Internet of Things (IIoT) and cybersecurity are gaining increasing importance. All these aspects are integral to the concept of smart manufacturing and smart factories.

The development of smart materials—both as components of manufactured products and as elements of production systems—opens entirely new technological paths and creates unprecedented opportunities. Their applications lead to the design of innovative devices and solutions that were previously unattainable. It is therefore crucial to develop concepts and research frameworks, both in laboratory conditions and in real-world applications of systems utilizing smart materials. Due to the complexity of the phenomena involved, modeling technical systems that incorporate smart materials is a demanding task. Nevertheless, creating accurate mathematical models at the design stage is a fundamental condition for their proper functioning. Thus, developing mathematical models and algorithms for analyzing and determining the characteristics of such systems is an important area of scientific research.

For this Special Issue, I invite the submission of original research papers and review articles that present innovative solutions in the field of smart technologies and smart materials. Topics may include modeling, testing, and practical applications.
I particularly encourage submissions that report conceptual work, laboratory experiments, and studies conducted on real-world systems, as well as research addressing modern modeling and simulation methods.

If you have any questions or concerns, please contact me via email.

Prof. Dr. Marek Placzek
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 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

  • smart technology
  • smart materials
  • simulation
  • modeling
  • analysis
  • testing
  • big data
  • autonomous robots
  • system integration
  • industrial IoT
  • cybersecurity
  • cloud computing
  • additive manufacturing
  • reverse engineering
  • virtual and augmented reality
  • artificial intelligence
  • piezoelectric transducers
  • electrostatic materials
  • magnetostrictive materials
  • shape memory materials
  • variable viscosity liquids
  • thermoelectric materials
  • light-emitting materials

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.

Related Special Issue

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

30 pages, 6139 KB  
Article
The Use of Augmented Reality in Manufacturing Company’s Environment
by Monika Töröková, Darina Dupláková, Jozef Török, Maryna Yeromina, Martin Koroľ and Miroslav Jaščur
Appl. Sci. 2026, 16(4), 2009; https://doi.org/10.3390/app16042009 - 18 Feb 2026
Viewed by 652
Abstract
This study presents a structured development and implementation process executed within the KAMAX manufacturing plant, leveraging a sophisticated technical workflow that integrates 3D scanning (via iPad Pro), the FataMorgana AR ecosystem, and Microsoft HoloLens 2 hardware. The goal is to practically show the [...] Read more.
This study presents a structured development and implementation process executed within the KAMAX manufacturing plant, leveraging a sophisticated technical workflow that integrates 3D scanning (via iPad Pro), the FataMorgana AR ecosystem, and Microsoft HoloLens 2 hardware. The goal is to practically show the possibilities of using the means of augmented reality in connection with specific hardware equipment, which helps in more agile management and functioning of a modern production company. A fundamental methodological advancement of this research is the deployment of a QR-code-based spatial synchronization protocol, which guarantees high-fidelity alignment during the superimposition of digital twins onto the physical production environment. Through a pilot initiative centered on the configuration of new manufacturing cells, the research empirically validates that AR-enhanced auditing substantially mitigates spatial design discrepancies. Specifically, the system excels at detecting physical interferences undetectable in conventional 2D blueprints, thereby streamlining the consultative and decision-making processes for organizational stakeholders during layout verification. These findings offer significant empirical evidence regarding the integration and interoperability of AR devices and IoT datasets within the broader Industry 4.0 paradigm. Full article
(This article belongs to the Special Issue Smart Manufacturing and Materials: 3rd Edition)
►▼ Show Figures

Figure 1

16 pages, 252 KB  
Article
Review and Analysis of Methods for Separating Plastic Micro-Particles from Pipe Systems, Taking into Account Efficiency and Automation Potential
by Piotr Skudlik, Andrzej Wróbel and Marek Łukasz Płaczek
Appl. Sci. 2026, 16(4), 1707; https://doi.org/10.3390/app16041707 - 9 Feb 2026
Viewed by 435
Abstract
The issue of microplastics in the aquatic environment has become one of the key topics in contemporary environmental engineering, chemical engineering and materials technology. Plastic microparticles are found not only in natural waters, but also in industrial and municipal piping systems, process installations [...] Read more.
The issue of microplastics in the aquatic environment has become one of the key topics in contemporary environmental engineering, chemical engineering and materials technology. Plastic microparticles are found not only in natural waters, but also in industrial and municipal piping systems, process installations and even in drinking water, posing a growing threat to public health, ecosystem stability and the reliability of technical equipment. Due to its chemical resistance, hydrophobicity and variety of sizes and shapes, microplastics are difficult to remove using traditional separation methods, and their harmful impact is part of a broader analysis of the life cycle of plastics, from their production and use to the waste phase and their impact on the environment. In response to the scale of the phenomenon, a number of liquid–solid separation methods have been developed, including approaches based on physical, chemical and biological principles. These methods vary in their scope of application, operational requirements and the way they interact with the particles present in the flow. The scientific literature describes mechanical techniques, chemical reactions and the interaction of biological organisms in a controlled environment as the main groups of separation. Each group has specific limitations resulting from the properties of microplastics, flow conditions and medium characteristics, which means that the choice of separation technology must take into account the specific nature of the system in question. The development of advanced measurement methods, monitoring systems and control techniques enables more accurate observation and analysis of particle movement, as well as the study of the relationship between device operating parameters and the behaviour of contaminants in the flow. The increasingly widespread use of measurement data, predictive algorithms and pattern recognition techniques makes it possible to describe the phenomena accompanying microplastic separation in greater detail and to formulate new concepts for devices and flow systems based on analytical methods, computational tools and adaptive control systems is in line with current trends in process engineering and automation, as well as with the concept of Industry 4.0. Taking the above information into account, the aim of this work is to analyse selected liquid–solid separation methods in order to identify the most optimal in terms of the effectiveness of removing plastic microparticles, with the assumption of the greatest possible number of features indicating the possible future automation of a given process. Full article
(This article belongs to the Special Issue Smart Manufacturing and Materials: 3rd Edition)
24 pages, 2667 KB  
Article
An Automated ML Anomaly Detection Prototype
by Daniel Resanovic and Nicolae Balc
Appl. Sci. 2026, 16(1), 337; https://doi.org/10.3390/app16010337 - 29 Dec 2025
Viewed by 833
Abstract
Predictive maintenance (PdM) often fails to progress beyond pilot projects because machine learning-based anomaly detection requires expert knowledge, extensive tuning, and labeled fault data. This paper presents an automated prototype that builds and evaluates multiple anomaly detection models with minimal manual configuration. The [...] Read more.
Predictive maintenance (PdM) often fails to progress beyond pilot projects because machine learning-based anomaly detection requires expert knowledge, extensive tuning, and labeled fault data. This paper presents an automated prototype that builds and evaluates multiple anomaly detection models with minimal manual configuration. The prototype automates feature creation, model training, hyperparameter search, and ensemble construction, while allowing domain experts to control how anomaly alerts are triggered and how detected events are reviewed. Developed in a multi-year photovoltaic (PV) solar farm case study, it targets operational anomalies such as sudden drops, underperformance periods, and abnormal drifts, using expert validation and synthetic benchmarks to shape and evaluate anomaly categories. Experiments on the real PV data, a synthetic PV benchmark, and a machine temperature dataset from the Numenta Anomaly Benchmark show that no single model performs best across datasets. Instead, diverse base models and both rule-based and stacked ensembles enable robust configurations tailored to different balances between missed faults and false alarms. Overall, the prototype offers a practical and accessible path toward PdM adoption by lowering technical barriers and providing a flexible anomaly detection approach that can be retrained and transferred across industrial time-series datasets. Full article
(This article belongs to the Special Issue Smart Manufacturing and Materials: 3rd Edition)
►▼ Show Figures

Figure 1

24 pages, 3105 KB  
Article
Thermal Modeling and Investigation of Interlayer Dwell Time in Wire-Laser Directed Energy Deposition
by Panagis Foteinopoulos, Marios Moutsos and Panagiotis Stavropoulos
Appl. Sci. 2026, 16(1), 122; https://doi.org/10.3390/app16010122 - 22 Dec 2025
Cited by 2 | Viewed by 929
Abstract
This study investigates the effect of Interlayer Dwell Time (IDT) on the thermal behavior of the Wire-Laser Directed Energy Deposition (WLDED) process. A two-dimensional transient thermal model was developed in MATLAB, incorporating temperature-dependent material properties, a moving Gaussian heat source, and melting–solidification phase [...] Read more.
This study investigates the effect of Interlayer Dwell Time (IDT) on the thermal behavior of the Wire-Laser Directed Energy Deposition (WLDED) process. A two-dimensional transient thermal model was developed in MATLAB, incorporating temperature-dependent material properties, a moving Gaussian heat source, and melting–solidification phase change to simulate sequential layer deposition. The model was calibrated for thin-walled geometries, numerically validated using ANSYS, and experimentally validated with literature data. Using the validated model, twenty-seven cases were simulated to examine the combined influence of IDT, part length, and layer thickness on melt-pool dimensions and layer-wise temperature distribution. The results show that increasing IDT reduces melt-pool depth and length by limiting heat accumulation, with the magnitude of this effect depending strongly on part length and layer thickness. Shorter parts and thicker layers exhibit the highest sensitivity to IDT variations. Additionally, the Thermal Stability Factor (TSF) is introduced, a dimensionless index that effectively identifies heat-accumulation phenomena and indicates thermal instabilities. Overall, the findings enhance the understanding of the impact of IDT in the thermal profile of WLDED and demonstrate that optimized IDT selection can stabilize melt-pool geometry and reduce thermal buildup, supporting future adaptive IDT strategies in wire-based metal additive manufacturing. Full article
(This article belongs to the Special Issue Smart Manufacturing and Materials: 3rd Edition)
►▼ Show Figures

Figure 1

18 pages, 3611 KB  
Article
Optimization of the Structural Design of a Vertical Lathe Table in the Context of Minimizing Thermal Deformations
by Janusz Śliwka, Krzysztof Lis and Mateusz Wąsik
Appl. Sci. 2025, 15(21), 11439; https://doi.org/10.3390/app152111439 - 26 Oct 2025
Viewed by 1030
Abstract
Modern machining industries require high precision and efficiency in machine tools, where thermal deformations significantly impact accuracy. This study focuses on optimizing the structural parameters of a vertical turning center to minimize thermal displacements affecting machining precision. The optimization process is divided into [...] Read more.
Modern machining industries require high precision and efficiency in machine tools, where thermal deformations significantly impact accuracy. This study focuses on optimizing the structural parameters of a vertical turning center to minimize thermal displacements affecting machining precision. The optimization process is divided into parametric and topological methodologies. The parametric approach targets three primary objectives: minimizing mass (q1), maximizing static stiffness (q2), and reducing thermal displacement (q3). Multi-criteria optimization techniques, including Pareto-based and scalarization methods, are applied to balance these conflicting factors. Finite Element Analysis (FEA) models assist in evaluating machine stiffness and displacement, with constraints imposed on structural mass and stiffness to maintain performance. Parametric optimization, using iterative computational algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), refines rib and wall thicknesses of the lathe table to achieve displacement reductions. The optimization process successfully lowers displacement at critical measurement points while maintaining structural integrity. Hybrid PSO (hPSO) outperforms other algorithms in achieving optimal parameter sets with minimal computational effort. Topological optimization, based on the Solid Isotropic Microstructure with Penalization (SIMP) method, further enhances structural efficiency by refining material distribution. The iterative process identifies optimal energy flow paths while ensuring compliance with mechanical constraints. A hybrid approach integrating parametric adjustments with topological refinement leads to superior performance, achieving a 43% reduction in displacement at key measurement points compared to the initial design. The final optimized design reduces mass by 1 ton compared to the original model and 2.5 tons compared to the best rib–wall optimization results. The study’s findings establish a foundation for implementing active deformation compensation systems in machine tools, enhancing machining precision. The integration of parametric and topological optimization presents a robust framework for designing machine tool structures with improved thermal stability and structural efficiency. Full article
(This article belongs to the Special Issue Smart Manufacturing and Materials: 3rd Edition)
►▼ 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