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Metals
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Smart Sensing and Artificial Intelligence in Metal Processing and Machining
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Smart Sensing and Artificial Intelligence in Metal Processing and Machining

A Special Issue of Metals (ISSN 2075-4701).

Deadline for manuscript submissions: 20 November 2026 | Viewed by 1451

Editor

Prof. Dr. Simon Klančnik

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
Interests: digital manufacturing; intelligent manufacturing systems; machining; metal cutting and cutting tools; artificial intelligence; machine vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the integration of smart sensing, artificial intelligence (AI), and advanced monitoring techniques in metal processing and machining. It aims to explore how modern digital technologies—such as machine learning, machine vision, data-driven modeling, and sensor fusion—can enhance the understanding and control of metal-related processes and properties.

The Special Issue welcomes contributions that investigate the relationships among processing parameters, metal structures, and properties, especially when these aspects are monitored or optimized using intelligent systems. Papers may address topics including, but not limited to, the following:

  • intelligent machining and forming of metals;
  • AI-based modeling of metal microstructure evolution;
  • machine vision for quality inspection of metallic surfaces;
  • digital twins for metal processing systems;
  • real-time monitoring of wear, tool condition, and surface quality;
  • multi-modal sensor fusion in metal manufacturing;
  • data-driven control strategies in metal forming, casting, or additive manufacturing;
  • predictive maintenance and diagnostics in metalworking machinery;
  • Case studies from industry involving AI in ferrous and non-ferrous metal processing.

The scope of this issue encompasses both theoretical and experimental work, as well as industrial case studies that demonstrate the benefits and challenges of AI integration in metal production environments.

Prof. Dr. Simon Klančnik
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals 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 2600 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

  • artificial intelligence in metal processing
  • smart sensing and monitoring
  • machine vision and quality inspection
  • data-driven modeling and control
  • intelligent machining and forming

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

17 pages, 3938 KB  
Article
Integrated Modeling and Multi-Criteria Analysis of the Turning Process of 42CrMo4 Steel Using RSM, SVR with OFAT, and MCDM Techniques
by Dejan Marinkovic, Kenan Muhamedagic, Simon Klančnik, Aleksandar Zivkovic, Derzija Begic-Hajdarevic and Mirza Pasic
Metals 2026, 16(2), 131; https://doi.org/10.3390/met16020131 - 23 Jan 2026
Cited by 1 | Viewed by 793
Abstract
This paper analyzes different approaches for the mathematical modeling and optimization of process parameters in the hard turning process of 42CrMo4 steel using a hybrid approach combining response surface methodology (RSM), multi-criteria decision making (MCDM), and machine learning through, support vector regression (SVR) [...] Read more.
This paper analyzes different approaches for the mathematical modeling and optimization of process parameters in the hard turning process of 42CrMo4 steel using a hybrid approach combining response surface methodology (RSM), multi-criteria decision making (MCDM), and machine learning through, support vector regression (SVR) with one-factor-at-a-time (OFAT) sensitivity analysis. Controlled process parameters such as cutting speed, depth of cut, feed, and insert radius are applied to conduct the experiments based on a full factorial experimental design. RSM was used to develop models that describe the effect of controlled parameters on surface roughness and cutting forces. Special emphasis was placed on the analysis of standardized residuals to evaluate the predictive capabilities of the RSM-developed model on an unseen data set. For all four outputs considered, analysis of the standardized residuals shows that over 97% of the points lie within ±3 standard deviations. A multi-criteria optimization technique was applied to establish an optimal combination of input parameters. The SVR model had high performance for all outputs, with coefficient of determination values between 89.91% and 99.39%, except for surface roughness on the test set, with a value of 9.92%. While the SVR model achieved high predictive accuracy for cutting forces, its limited generalization capability for surface roughness highlights the higher complexity and stochastic nature of surface formation mechanisms in the turning process. OFAT analysis showed that feed rate and depth of cut have been shown to be the most important input variables for all analyzed outputs. Full article
(This article belongs to the Special Issue Smart Sensing and Artificial Intelligence in Metal Processing and Machining)
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