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Machines
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Integration of Industrial Machines into Smart Manufacturing, Digital Twin Technology...
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Integration of Industrial Machines into Smart Manufacturing, Digital Twin Technology for Industry 4.0 Machinery

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

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 4946

Special Issue Editor

Dr. Chakib Alaoui

E-Mail Website
Guest Editor
Department of Electrical Engineering, Euromed Polytechnique School, Euromed University of Fez, Fez, Morocco
Interests: embedded systems; renewable energy technologies; electric mobility; smart grid; applied artificial intelligence

Special Issue Information

Dear Colleagues,

The advent of smart manufacturing and Industry 4.0 has paved the way for a new era, where modern technologies, such as Artificial Intelligence (AI), cloud computing, Virtual Reality (VR), Digital Twin (DT), etc., play crucial roles in driving this industrial revolution. Various methods have been developed and implemented for both in-service industrial machines and design processes. Industry 4.0 methodologies are applied to new machines and processes at the design stage, as well as to existing machines and processes to adapt them to Industry 4.0 standards. This transition marks the shift from traditional industry practices to the advanced frameworks of Industry 4.0.

This Special Issue aims to synthesize research and recent advancements in methods and technologies developed for smart manufacturing and Industry 4.0. It focuses on the improvement of machine design and integrating existing industrial machines into the Industry 4.0 ecosystem. The methods covered include Industrial Internet of Things (IIoT), Digital Twin, energy efficiency, AI-based and hybrid algorithms, Virtual Reality, and advanced Maintenance, all aimed at enhancing performance and adapting machines to Industry 4.0 standards.

Dr. Chakib Alaoui
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. 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
  • Industry 4.0 methodologies
  • Industrial Internet of Things (IIoT)
  • Artificial Intelligence (AI)
  • cloud computing
  • Virtual Reality (VR)
  • Digital Twin (DT)

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Published Papers (3 papers)

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Research

28 pages, 6191 KB  
Article
Prediction of Groove Depth in Femtosecond Laser Ablation via Attention Mechanism and Monotonic Constraint
by Guangxian Li, Luyang Ding, Meng Liu, Hui Xie and Songlin Ding
Machines 2026, 14(5), 509; https://doi.org/10.3390/machines14050509 - 3 May 2026
Viewed by 211
Abstract
Femtosecond laser ablation (FLA) is efficient for the machining of micro-groove arrays on the surface of ultrahard cutting tools. The depth of the groove determines the precision and efficiency of ablation. In this study, an “Attention-based Monotonic Physics-Guided Neural Network” (AM-PGNN) algorithm is [...] Read more.
Femtosecond laser ablation (FLA) is efficient for the machining of micro-groove arrays on the surface of ultrahard cutting tools. The depth of the groove determines the precision and efficiency of ablation. In this study, an “Attention-based Monotonic Physics-Guided Neural Network” (AM-PGNN) algorithm is proposed to accurately predict groove depth in the FLA of tungsten carbide (WC). The new algorithm incorporates machining parameters directly governing the energy deposition and thermal accumulation, thereby determining the prediction of the micro-groove depth generation. By embedding the physics-guided monotonic relationships of parameter depth into the learning process, a dedicated physical loss coupled with an attention mechanism to enable adaptive feature weighting is constructed, which strengthens the representation of causal dependencies. Experimental data for training and testing are obtained from the FLA of WC with different machining parameters. Comparison between AM-PGNN and typical algorithms, including a Support Vector Machine (SVM), Deep Neural Network (DNN), Convolutional Neural Network (CNN), Gradient Boosting Decision Tree (GBDT), and a conventional PGNN, demonstrates that the proposed AM-PGNN achieves superior prediction accuracy. Moreover, AM-PGNN attains a physical consistency degree (PCD) of 100%, indicating strict adherence to monotonicity consistent with the actual situation. AM-PGNN also exhibits enhanced robustness to input perturbations, as reflected by reduced standard deviation (Std) and normalized absolute deviation (NAD). Finally, AM-PGNN is shown to be applicable in the FLA of different materials through additional experiments on Cu and SiC, achieving R2 values above 0.93 while maintaining a PCD of 100%. Full article
(This article belongs to the Special Issue Integration of Industrial Machines into Smart Manufacturing, Digital Twin Technology for Industry 4.0 Machinery)
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32 pages, 63092 KB  
Article
A Digital Twin-Enabled Framework for Agrivoltaic System Design, Simulation, Monitoring and Control
by Eshan Edirisinghe, George Wu, Divye Maggo, Chi-Tsun Cheng, Toh Yen Pang, Azizur Rahman, Angela L. Avery, Kieran R. Murphy and Carlos A. Lora
Machines 2026, 14(3), 254; https://doi.org/10.3390/machines14030254 - 24 Feb 2026
Viewed by 1645
Abstract
Agrivoltaics offer a sustainable solution to the growing competition between food and energy production. However, their adoption is often constrained by the design and operation challenges associated with optimising the complex trade-off between crop yield and photovoltaic (PV) output. Digital twins can mitigate [...] Read more.
Agrivoltaics offer a sustainable solution to the growing competition between food and energy production. However, their adoption is often constrained by the design and operation challenges associated with optimising the complex trade-off between crop yield and photovoltaic (PV) output. Digital twins can mitigate these risks, yet most agricultural digital twins operate as fragmented digital shadows, lacking high-fidelity modelling, advanced simulation, and bidirectional control capabilities. This study presents a comprehensive, end-to-end digital twin framework to address these limitations. The framework integrates a high-resolution 3D orchard model, reconstructed via UAV photogrammetry, with a CesiumJS-based web interface linked to a modular IoT architecture built on Node-RED, Message Queuing Telemetry Transport (MQTT) protocol and InfluxDB for real-time monitoring and control. A PV simulation engine supports the design, simulation and optimisation of agrivoltaic systems. Bidirectional communication was validated through remote actuation of a physical solar tracker, demonstrating integration among the 3D environment, sensor data and control systems to achieve a closed-loop digital twin. Simulation analyses suggested that panel orientation and row spacing exert a dominant influence on crop-level light distribution. Simulation results demonstrated that a 90° azimuth configuration achieved the highest daily energy yield of 53.97 kWh but reduced peak crop-level irradiance to 205 W/m2. In contrast, the baseline 0° configuration offered a balanced output of 40.86 kWh with a peak light availability of 338 W/m2. The validated, interoperable digital twin architecture provides a reference model for the design, simulation, monitoring and control of an agrivoltaic system, reducing investment uncertainty and supporting sustainable food–energy co-production. Full article
(This article belongs to the Special Issue Integration of Industrial Machines into Smart Manufacturing, Digital Twin Technology for Industry 4.0 Machinery)
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25 pages, 2592 KB  
Article
Exploratory Integration of a Digital Twin with a Data Space: Case Study with the Asset Administration Shell
by Francisco Zenza, Luís P. Ferreira, Carlos Gonçalves, Ricardo Ribeiro and Ana L. Ramos
Machines 2025, 13(9), 751; https://doi.org/10.3390/machines13090751 - 22 Aug 2025
Viewed by 2467
Abstract
In the context of Industry 4.0, technologies such as Digital Twin (DT) and Data Space (DS) have emerged as a revolution in the way physical assets are represented in simulation models and how their information and data are represented in cloud repositories. The [...] Read more.
In the context of Industry 4.0, technologies such as Digital Twin (DT) and Data Space (DS) have emerged as a revolution in the way physical assets are represented in simulation models and how their information and data are represented in cloud repositories. The aim of this work was to investigate the technologies of DTs and DSs, with a focus on their application in an industrial context, delving into the approaches and difficulties of the integration of both technologies, so that it can be explored and answered the respective challenges. To this end, literature reviews on these topics were explored by reading various sources, as well as analyzing different methodologies for implementing and integrating the two technologies. The result was a description of the main methodologies for integrating DTs with DSs, with the addition of a practical application using AASX Package Explorer, this being a platform enabling the virtual representations of industrial equipment in the molds of DT technologies, containing the association with server tools from other developers and specifications. Full article
(This article belongs to the Special Issue Integration of Industrial Machines into Smart Manufacturing, Digital Twin Technology for Industry 4.0 Machinery)
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