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Applied Sciences
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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 October 2024 | Viewed by 1138

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: robotic process automation; artificial intelligence in robotics; virtual and augmented reality in industry; smart sensors and systems; flexible sensors
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 (1 paper)

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Research

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
Viewed by 818
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

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.

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