Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.1
2.6
Journals
Machines
Special Issues
Applications of AI and Digital Twinning in Electric Machines
share announcement

Applications of AI and Digital Twinning in Electric Machines

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 1336

Special Issue Editor

Dr. Farzad Ferdowsi

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA, USA
Interests: real-time analysis of power system stability and control; simulation and analysis of power electronic-based components in power systems; power converters design, integration, control, and hardware implementation; operational resilience enhancement in power and energy systems; cyber-physical security in power systems

Special Issue Information

Dear Colleagues,

The Special Issue, "Applications of AI and Digital Twinning in Electric Machines" hosted by MDPI Machines, aims to showcase cutting-edge research and innovative applications of artificial intelligence (AI) and digital twinning in the field of electric machines. Electric machines play a pivotal role in various industries, from transportation to renewable energy, making their optimization and performance enhancement crucial. AI and digital twinning have emerged as powerful tools for achieving these objectives. This Special Issue will serve as a platform for researchers, engineers, and practitioners to disseminate their insights and findings in this dynamic and rapidly evolving field.

We are seeking contributions that explore the synergistic use of AI and digital twin technology in electric machines. Topics of interest include, but are not limited to:

  • AI-driven predictive maintenance for electric machines;
  • Design optimization and performance enhancement through digital twin modeling;
  • AI-based fault detection and diagnosis in electric machines;
  • Advanced control strategies using AI for electric machines;
  • Energy efficiency improvement and resource management in electric machines through AI.

Dr. Farzad Ferdowsi
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 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. 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

  • electric machines
  • AI
  • digital twin

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 2477 KiB  
Article
ML-Enabled Piezoelectric-Driven Internal Defect Assessment in Metal Structures
by Daniel Adeleye, Mohammad Seyedi, Farzad Ferdowsi, Jonathan Raush and Ahmed Khattab
Machines 2023, 11(12), 1038; https://doi.org/10.3390/machines11121038 - 21 Nov 2023
Cited by 1 | Viewed by 1104
Abstract
With the growth of 3D printing in the production space, it is inevitable that quality assurance will be needed to keep final products within the constraints of requirements. Also, the variety of materials that can be used with 3D printing has increased over [...] Read more.
With the growth of 3D printing in the production space, it is inevitable that quality assurance will be needed to keep final products within the constraints of requirements. Also, the variety of materials that can be used with 3D printing has increased over the years. Testing also must consider the process of manufacturing. This paper focuses its efforts on the finished product and not the process of manufacturing. Ultrasonic testing is a type of nondestructive testing. The experiments performed in this study aim to explore the usefulness of ultrasonic testing in materials that are 3D printed. The two materials used in this study are steel alloy metals and aluminum blocks of the same dimensions—120 mm × 40 mm × 15 mm. These materials represent common choices in additive manufacturing processes. The chosen alloys, such as Aluminum (6063T6) and grade-304 stainless steel, possess distinct properties crucial for validating the proposed testing method. Metal 3D-printed materials play a pivotal role in diverse industries, since ensuring their structural integrity is imperative for reliability and safety. Testing is crucial to identify and mitigate defects that could compromise the functionality and longevity of the final products, especially in applications with demanding performance requirements. An ultrasonic transducer is used to scan for subsurface defects within the samples and an oscilloscope is used to analyze the signals. Furthermore, several Machine Learning (ML) techniques are used to estimate the severity of the defects. The application of Machine Learning methods in the manufacturing industry has proven advantageous in terms of detecting defects due to its practicality and wide application. Due to their distinct benefits in processing image information, convolutional neural networks (CNNs) are the preferred method when working with picture data. In order to perform binary and multi-class classification, support vector machines that employ the alternative kernel function are a viable option for processing sensor signals and picture data. The study reveals that ultrasonic tests are viable for metallic materials. The primary objective of this work is to evaluate and validate the application of ultrasonic testing for the inspection of 3D-printed steel alloy metals and aluminum blocks. The novelty lies in the integration of Machine Learning techniques to estimate defect severity, offering a comprehensive and non-invasive approach to quality assessment in 3D-printed materials. The proposed method can successfully detect the presence of internal defects in objects, as well as estimate the location and severity of the defects. Full article
(This article belongs to the Special Issue Applications of AI and Digital Twinning in Electric Machines)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop