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Digital Technologies Enabling Modern Industries
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Digital Technologies Enabling Modern Industries

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 15533

Special Issue Editors

Dr. Janis Arents

E-Mail Website
Guest Editor
Institute of Electronics and Computer Science, LV-1006 Riga, Latvia
Interests: robotics; mobile manipulators; grasping; AI-based systems; perception
Prof. Dr. Vytautas Bucinskas

E-Mail Website
Guest Editor
Department of Mechatronics, Robotics, and Digital Manufacturing, Vilnius Gediminas Technical University, LT-10105 Vilnius, Lithuania
Interests: biodegradable material; fexible strain sensors; robotic process automation; artificial intelligence in robotics; virtual and augmented reality in industry; smart sensors and systems
Special Issues, Collections and Topics in MDPI journals
Dr. Andrius Dzedzickis

E-Mail Website
Guest Editor
Department of Mechatronics, Robotics, and Digital Manufacturing, Vilnius Gediminas Technical University, LT-10105 Vilnius, Lithuania
Interests: robotic process automation; artificial intelligence in robotics; virtual and augmented reality in industry; digital twins of industrial systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, digital technologies are becoming the main factor fostering the development of modern industries. They affect all industry fields and aspects, from orders and resource management to final product delivery and maintenance.

In this upcoming Special Issue, titled "Digital Technologies Enabling Modern Industries", we aim to highlight the transformative power of digital technologies in reshaping modern industries. This Special Issue is dedicated to uncovering how advancements in robotics, artificial intelligence, the Internet of Things (IoT), and other digital innovations are synergizing to redefine traditional practices, enhance productivity, and facilitate sustainable growth. The focus ranges from the deployment of robotic solutions in complex environments to the seamless integration of AI for smarter decision making and operational efficiency. Robotics, central to this transformation, are evolving beyond their conventional roles, driven by breakthroughs in perception, navigation, grasping techniques, natural language processing, and many other aspects. These technologies are enabling autonomous operations in diverse landscapes and enhance synergy with human workers. Simultaneously, sensors and the IoT, technologies that stand at the forefront of the digital revolution, are another pivotal aspect within this Special Issue. These technologies are instrumental in creating interconnected ecosystems within industries, allowing for the seamless collection, transmission, and analysis of data.

Dr. Janis Arents
Prof. Dr. Vytautas Bucinskas
Dr. Andrius Dzedzickis
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. 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

  • perception
  • navigation
  • robotics
  • automation
  • robotic grasping
  • sensors
  • IoT
  • simulation
  • synthetic data
  • multiagent systems
  • smart manufacturing
  • artificial intelligence
  • virtual and augmented reality
  • digital twins

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Published Papers (6 papers)

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Research

18 pages, 1797 KiB  
Article
Federated Learning Lifecycle Management for Distributed Medical Artificial Intelligence Applications: A Case Study on Post-Transcatheter Aortic Valve Replacement Complication Prediction Solution
by Min Hyuk Jung, InSeo Song and KangYoon Lee
Appl. Sci. 2025, 15(1), 378; https://doi.org/10.3390/app15010378 - 3 Jan 2025
Viewed by 750
Abstract
The evolution of artificial intelligence (AI) has unveiled considerable prospects for delivering efficacious solutions in the medical domain. Nevertheless, existing legal frameworks and concerns regarding data privacy associated with medical information impose substantial constraints on implementing AI solutions in this domain. Federated learning [...] Read more.
The evolution of artificial intelligence (AI) has unveiled considerable prospects for delivering efficacious solutions in the medical domain. Nevertheless, existing legal frameworks and concerns regarding data privacy associated with medical information impose substantial constraints on implementing AI solutions in this domain. Federated learning is a paradigm that enables the training of machine learning models in a decentralized manner without transferring data to a central repository, allowing model development while preserving data privacy across medical and other industries. This study provided a comprehensive framework for applying federated learning to AI solutions in the medical domain. It advocates a sustainable learning ecosystem by overseeing federated learning servers and clients and evaluating performance by managing the federated learning lifecycle. To enhance its practical relevance, this framework includes a detailed process for continuous lifecycle management, involving model deployment, aggregation, testing, evaluation, versioning, and real-time monitoring through the FedOps platform, supporting a sustainable solution. In this study, the feasibility of the proposed methodology was verified using a post-transcatheter aortic valve replacement (TAVR) complication–prediction framework. The performance of the solution after transitioning to a federated learning approach was compared with that of an existing centralized solution. The findings indicated no statistically significant difference in performance between the two methodologies. This implies that federated learning can augment data usability and facilitate the integration of AI technologies into the medical domain, where the preservation of data privacy is critically important. Full article
(This article belongs to the Special Issue Digital Technologies Enabling Modern Industries)
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19 pages, 8859 KiB  
Article
Nesting Process Automation in the Footwear Industry: A Hybrid Approach to Minimize Material Waste
by Eliseo Aguilar-Tortosa, Eduard-Andrei Duta-Costache, Elías Vera-Brazal, José-Luis Sánchez-Romero, José Francisco Gómez-Hernández, Antonio Jimeno-Morenilla and Antonio Maciá-Lillo
Appl. Sci. 2025, 15(1), 320; https://doi.org/10.3390/app15010320 - 31 Dec 2024
Viewed by 832
Abstract
In any industry, maximizing the use of raw materials is essential to reduce waste and costs, which also positively impacts the environment. In footwear production, components are typically derived from cutting processes, requiring optimized systems to maximize the use of different materials, minimize [...] Read more.
In any industry, maximizing the use of raw materials is essential to reduce waste and costs, which also positively impacts the environment. In footwear production, components are typically derived from cutting processes, requiring optimized systems to maximize the use of different materials, minimize waste, and accelerate production. In this context, nesting is a technique that arranges shapes within a confined space to maximize area utilization and reduce unused space. As this problem is classified as NP-Hard, only algorithmic approximations can be employed. This paper focuses on optimizing the cutting of leather parts for shoe manufacturing. Footwear parts are cut from cattle hides, which are not only irregular in shape but also vary in resistance and quality across different areas of the same piece of leather. This study proposes automated nesting methods that aim to compete with current manual approaches, which are conducted exclusively by experts with deep knowledge of the characteristics of both the pieces and the leather, making the manual process time-intensive. This research reviews current methods and introduces hybrid ones, achieving up to 38.4× acceleration and up to 10.18% increase in nested pieces over manual methods. Full article
(This article belongs to the Special Issue Digital Technologies Enabling Modern Industries)
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17 pages, 1584 KiB  
Article
Immersive Two-Channel Recordings Based on Personalized BRIRs and Their Applications in Industry
by Kaja Kosmenda and Witold Mickiewicz
Appl. Sci. 2024, 14(24), 11724; https://doi.org/10.3390/app142411724 - 16 Dec 2024
Viewed by 827
Abstract
The realm of immersive sound technologies in modern industries is evolving every day. Two-channel recordings using personalized HRIRs or BRIRs, which are tailored to the unique anatomical features of individual listeners, significantly enhance the spatial accuracy and naturalness of sound, providing a highly [...] Read more.
The realm of immersive sound technologies in modern industries is evolving every day. Two-channel recordings using personalized HRIRs or BRIRs, which are tailored to the unique anatomical features of individual listeners, significantly enhance the spatial accuracy and naturalness of sound, providing a highly immersive auditory experience. This paper discusses the importance of immersive sound and the externalization effect in recreating the acoustic environment. The paper also presents techniques for obtaining two-channel immersive renderings in a few different ways. The main focus is the integration of immersive audio in new technologies in the wide-ranging audio industry, from telecommunication, through applications for musicians, virtual reality scenarios, and hearing devices. In summary, this paper highlights the huge potential of personalized BRIRs in creating immersive two-channel recordings, offering substantial benefits across various industries by improving the realism and effectiveness of each auditory experience on its own. Full article
(This article belongs to the Special Issue Digital Technologies Enabling Modern Industries)
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20 pages, 2004 KiB  
Communication
Towards Open-Set NLP-Based Multi-Level Planning for Robotic Tasks
by Peteris Racinskis, Oskars Vismanis, Toms Eduards Zinars, Janis Arents and Modris Greitans
Appl. Sci. 2024, 14(22), 10717; https://doi.org/10.3390/app142210717 - 19 Nov 2024
Cited by 1 | Viewed by 1134 | Correction
Abstract
This paper outlines a conceptual design for a multi-level natural language-based planning system and describes a demonstrator. The main goal of the demonstrator is to serve as a proof-of-concept by accomplishing end-to-end execution in a real-world environment, and showing a novel way of [...] Read more.
This paper outlines a conceptual design for a multi-level natural language-based planning system and describes a demonstrator. The main goal of the demonstrator is to serve as a proof-of-concept by accomplishing end-to-end execution in a real-world environment, and showing a novel way of interfacing an LLM-based planner with open-set semantic maps. The target use-case is executing sequences of tabletop pick-and-place operations using an industrial robot arm and RGB-D camera. The demonstrator processes unstructured user prompts, produces high-level action plans, queries a map for object positions and grasp poses using open-set semantics, then uses the resulting outputs to parametrize and execute a sequence of action primitives. In this paper, the overall system structure, high-level planning using language models, low-level planning through action and motion primitives, as well as the implementation of two different environment modeling schemes—2.5 or fully 3-dimensional—are described in detail. The impacts of quantizing image embeddings on object recall are assessed and high-level planner performance is evaluated using a small reference scene data set. We observe that, for the simple constrained test command data set, the high-level planner is able to achieve a total success rate of 96.40%, while the semantic maps exhibit maximum recall rates of 94.69% and 92.29% for the 2.5d and 3d versions, respectively. Full article
(This article belongs to the Special Issue Digital Technologies Enabling Modern Industries)
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20 pages, 724 KiB  
Article
Integration of Artificial Intelligence and Robotic Process Automation: Literature Review and Proposal for a Sustainable Model
by Leonel Patrício, Leonilde Varela and Zilda Silveira
Appl. Sci. 2024, 14(21), 9648; https://doi.org/10.3390/app14219648 - 22 Oct 2024
Cited by 8 | Viewed by 9132
Abstract
This article investigates the growing integration between Artificial Intelligence (AI) and Robotic Process Automation (RPA), proposing an innovative model aimed at optimizing the operational efficiency of organizations balancing the social and environmental impacts arising from the use of these technologies. The research identifies [...] Read more.
This article investigates the growing integration between Artificial Intelligence (AI) and Robotic Process Automation (RPA), proposing an innovative model aimed at optimizing the operational efficiency of organizations balancing the social and environmental impacts arising from the use of these technologies. The research identifies a significant gap in the literature through a systematic review, revealing the need for greater attention to the social and environmental impacts of the implementation of AI and RPA. Employing an approach based on the PICO methodology (Population, Intervention, Comparison, Outcome), this study justifies the formulation of hypotheses and the choice of methodology, ensuring scientific rigor. The proposed model considers ethical issues such as privacy and cybersecurity and explores the challenges associated with the adoption of these innovations. The discussion includes the readiness of organizations to integrate these technologies, highlighting technical and cultural limitations that may influence the model’s effectiveness. The theoretical results suggest that careful implementation can optimize resource utilization, promoting a balance between operational efficiency and social and environmental responsibility. Furthermore, the article presents an analysis of the positive impacts, such as improved efficiency, and negative impacts, such as the fear of job displacement associated with the integration of AI and RPA, reinforcing the need for responsible adoption that fosters social and environmental sustainability in the digital age. Full article
(This article belongs to the Special Issue Digital Technologies Enabling Modern Industries)
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18 pages, 6262 KiB  
Article
A Defect Detection Method Based on YOLOv7 for Automated Remanufacturing
by Guru Ratan Satsangee, Hamdan Al-Musaibeli and Rafiq Ahmad
Appl. Sci. 2024, 14(13), 5503; https://doi.org/10.3390/app14135503 - 25 Jun 2024
Cited by 1 | Viewed by 2056
Abstract
Remanufacturing of mechanical parts has recently gained much attention due to the rapid development of green technologies and sustainability. Recent efforts to automate the inspection step in the remanufacturing process using artificial intelligence are noticeable. In this step, a visual inspection of the [...] Read more.
Remanufacturing of mechanical parts has recently gained much attention due to the rapid development of green technologies and sustainability. Recent efforts to automate the inspection step in the remanufacturing process using artificial intelligence are noticeable. In this step, a visual inspection of the end-of-life (EOL) parts is carried out to detect defective regions for restoration. This operation relates to the object detection process, a typical computer vision task. Many researchers have adopted well-known deep-learning models for the detection of damage. A common technique in the object detection field is transfer learning, where general object detectors are adopted for specific tasks such as metal surface defect detection. One open-sourced model, YOLOv7, is known for real-time object detection, high accuracy, and optimal scaling. In this work, an investigation into the YOLOv7 behavior on various public metal surface defect datasets, including NEU-DET, NRSD, and KolektorSDD2, is conducted. A case study validation is also included to demonstrate the model’s application in an industrial setting. The tiny variant of the YOLOv7 model showed the best performance on the NEU-DET dataset with a 73.9% mAP (mean average precision) and 103 FPS (frames per second) in inference. For the NRSD dataset, the model’s base variant resulted in 88.5% for object detection and semantic segmentation inferences. In addition, the model achieved 65% accuracy when testing on the KolektorSDD2 dataset. Further, the results are studied and compared with some of the existing defect detection models. Moreover, the segmentation performance of the model was also reported. Full article
(This article belongs to the Special Issue Digital Technologies Enabling Modern Industries)
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