Machines | Aims & Scope - MDPI
About Machines Aims Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible.

Special Issues | Machines | MDPI
Machines publishes Special Issues to create collections of papers on specific topics, with the aim of building a community of authors and readers to discuss the latest research and develop new ideas and research directions.

Machines | Instructions for Authors - MDPI
Manuscripts for Machines should be submitted online at susy.mdpi.com. The submitting author, who is generally the corresponding author, is responsible for the manuscript during the submission and peer review process.

Machines | January 2025 - Browse Articles - MDPI
This study explores the optimization of the machining time in CNC milling machines by varying the machine parameters and toolpath strategies. Using the ICAM3D simulation software version 3.1.0, this approach focuses on minimizing the machining time while adhering to operational constraints.

Machines | Editorial Board - MDPI
Interests: design of high precision machines; ultraprecision machining systems; micro-cutting mechanics and physics; multiscale multiphysics modelling and analysis; smart cutting tools and smart machining; physical AI for manufacturing systems

All Sections | Machines | MDPI
Mechatronics technologies have evolved into indispensable components for the development of intelligent devices, machines, and robots that play a crucial role in supporting human life in our modern society.

Machines | Article Processing Charges - MDPI
All articles published in Machines (ISSN 2075-1702) are published in full open access. An article processing charge (APC) of CHF 2400 (Swiss francs) applies to papers accepted after peer review.

Machines | 2025 - Browse Issues - MDPI
share announcement Machines, Volume 13 (2025) Vol. 13, Iss. 1 January 2025 72 articles

Exploring Kernel Machines and Support Vector Machines: Principles ...
We first describe how to turn classical methods into kernel machines, and then give a literature review of existing kernel machines. We then introduce the SVM model, its principles, and various SVM training methods for classification, clustering, and regression.

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