Artificial Intelligence for Engineering Applications, 2nd Edition

A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 342

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Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas, Las Campanas, Queretaro 76010, Mexico
Interests: machine learning; neural networks and artificial intelligence; air pollution; particulate matter
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Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico
Interests: machine learning; deep learning; metaheuristics; signal processing; image processing; biomedical; bioengineering; automation
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Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the Special Issue focused on the advances in artificial intelligence applied to comprehensive engineering solutions. These techniques range from machine learning models that apply accurate prediction and decision-making to image processing, which improves visual analysis and pattern detection.

In an increasingly technologically advanced world, integrating artificial intelligence into engineering has offered prominent results. This has motivated efforts to create comprehensive solutions by optimizing processes and improving the design and functionality of electronics to enhance different systems. From the health applications of engineering, such as biomedical technology, to the efficiency of energy systems, AI has been fundamental in revolutionizing these areas. This is why our SI aims to compile AI advances applied to innovative, technological, and scientific solutions in the engineering field.

The main areas of engineering that our Special Issue focuses on are as follows:

  • Automation;
  • Electronics;
  • Electric power;
  • Sustainability;
  • Biomedical;
  • Mechatronic;
  • Computer systems;
  • Multidisciplinary engineering.

Prof. Dr. Juvenal Rodriguez-Resendiz
Prof. Dr. Marco Antonio Aceves-Fernandez
Dr. Akos Odry
Prof. Dr. José Manuel Álvarez-Alvarado
Guest Editors

Dr. Marcos Aviles
Guest Editor Assistant

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. Eng 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 1200 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

  • image processing
  • AI in embedded systems
  • optimization algorithms
  • autonomous robotics
  • system control
  • computational optimization
  • neural networks for engineering
  • IoT

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Published Papers (1 paper)

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Research

23 pages, 3009 KiB  
Article
Parametric Optimization of Train Brake Pad Using Reverse Engineering with Digital Photogrammetry 3D Modeling Method
by P Paryanto, Muhammad Faizin and R Rusnaldy
Eng 2025, 6(5), 96; https://doi.org/10.3390/eng6050096 - 12 May 2025
Viewed by 198
Abstract
Reverse engineering (RE) is essential in recreating 3D models of existing manufactured parts. It is widely used for repairing damaged components, improving used parts, and designing new items based on older models. One of the most common methods in RE is photogrammetry, which [...] Read more.
Reverse engineering (RE) is essential in recreating 3D models of existing manufactured parts. It is widely used for repairing damaged components, improving used parts, and designing new items based on older models. One of the most common methods in RE is photogrammetry, which enables 3D reconstruction by capturing multiple images. Therefore, this study aimed to explore the application of mobile photogrammetry to obtain a 3D model of a train brake pad. The process started with capturing images of objects in a quick and professional manner to ensure visualization of data. This was followed by processing 2D images using Agisoft Metashape 2.2.1 software and Artificial Intelligence (AI) algorithms to create a precise 3D model. Subsequently, assessment was performed using feasibility in terms of cost, time, and accuracy. The results show that mobile photogrammetry provided an accessible and cost-effective method, alongside maximum contact stress after reducing optimization by approximately 28.42%, with maximum error value measured by the virtual model with the reference value of 0.30 mm (on Metashape) and 0.46 mm (on AI). This suggested that reverse parameterization significantly accelerated computer-aided design (CAD) model reconstruction and reduced the part redesign development cycle. By using photogrammetry and parametric modeling, engineers could accurately analyze and optimize train brake pads, ensuring safety as well as sustainability in railway operations. Additionally, RE and parametric modeling could assist in creating durable, cost-effective alternatives, and predicting appropriate replacements. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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