Neural Networks Applied in Manufacturing and Design
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 628
Special Issue Editor
Interests: electrical engineering; renewable energies; data science, and optimization applied to energy management; electrical equipment diagnosis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial neural networks have revolutionized manufacturing and design by enabling more efficient, accurate, and innovative processes. In the manufacturing industry, neural networks play a pivotal role in predictive maintenance, wherein they analyze data from machinery to anticipate malfunctions before their occurrence, thereby reducing downtime and maintenance expenses. They also enhance quality control by identifying defects in products with high precision. This ensures consistent quality and reduces waste.
In design, neural networks facilitate the creation of optimized parts of machines by analyzing vast amounts of process-related data to orient their improvement or design. They can quickly simulate and evaluate numerous design variations, leading to more efficient and effective machine development cycles. This capability is particularly important in industries like automotive and aerospace, where design precision and innovation are critical. In addition, applications are increasingly located in more demanding environments.
We must not forget that neural networks enhance energy efficiency by optimizing production processes, resulting in significant cost reductions and environmental advantages. They also improve supply chain management by predicting demand and optimizing inventory levels, improving overall operational efficiency.
This Special Issue welcomes proposals or applications related to neural networks in the context of manufacturing and design for improving productivity and quality and driving innovation and sustainability since they are indispensable tools in modern practices where industrial machines are the core part of the process.
Dr. Ignacio Martin-Diaz
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
- predictive maintenance
- quality control
- process optimization
- defect detection
- machine design
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