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Coatings
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Surface Integrity and Engineering Optimization in Advanced Manufacturing and Mechanical...
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Surface Integrity and Engineering Optimization in Advanced Manufacturing and Mechanical Systems

A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Surface Characterization, Deposition and Modification".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 996

Special Issue Editors

Dr. Alina Bianca Pop

E-Mail Website
Guest Editor
Department of Engineering and Technology Management, Faculty of Engineering, Northern University Centre of Baia Mare, Technical University of Cluj-Napoca, 62A Victor Babes Street, 430083 Baia Mare, Romania
Interests: machining processes; surface integrity; manufacturing optimization; mechanical behavior of materials; quality management; quality engineering; experimental research and data processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aurel Mihail Titu

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, Faculty of Engineering, Lucian Blaga University of Sibiu, 10 Victoriei Street, 550024 Sibiu, Romania
Interests: quality management; university management; quality engineering; intellectual property protection management; knowledge management; experimental research and data processing; innovation management and technology transfer; strategic management; assisted design of processing and control devices; nonconventional technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current industrial landscape, the reliability and efficiency of mechanical components are profoundly influenced by their surface characteristics. The interplay between manufacturing processes and the resulting surface integrity remains a critical factor in determining the operational lifespan and performance of advanced engineering systems.

This Special Issue aims to gather high-quality research and review articles focused on the latest developments in surface engineering, material processing, and the optimization of manufacturing systems. We seek to explore how innovative machining techniques and specialized tooling geometries impact the surface quality of high-performance alloys.

Furthermore, we welcome studies regarding the design and optimization of industrial equipment and clamping systems, particularly those that prioritize surface protection and precision during the manufacturing cycle. The scope also extends to the mechanical behavior and dynamic analysis of complex assemblies, where surface-related degradation and wear are key factors in structural stability and safety.

Topics of interest include, but are not limited to:

  • Influence of advanced machining parameters on surface integrity and morphology;
  • Tool–workpiece interaction and its effects on surface quality;
  • Innovative manufacturing equipment and processes for surface-sensitive components;
  • Optimization of clamping and handling systems to ensure surface protection;
  • Analysis of wear, friction, and surface-related degradation in mechanical assemblies;
  • Characterization and performance evaluation of modified surfaces in industrial applications.

We look forward to receiving your contributions!

Dr. Alina Bianca Pop
Prof. Dr. Aurel Mihail Titu
Guest Editors

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

  • surface integrity
  • advanced manufacturing
  • functional coatings
  • surface wear and degradation
  • process optimization

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

Published Papers (2 papers)

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Research

15 pages, 640 KB  
Article
Training an Artificial Neural Network Based on Results of the Experiment on Machining of Aluminum Alloys 2196, 2043 and 2099 Used in the Aeronautical Industry
by Nicolae Ioan Pasca, Mihai Banica and Vasile Nasui
Coatings 2026, 16(5), 519; https://doi.org/10.3390/coatings16050519 - 26 Apr 2026
Viewed by 314
Abstract
The paper presents a study regarding the tool-life of uncoated and DLC-coated cutting inserts used for machining aluminum–lithium components used in the structure of the Airbus A350 aircraft. The experiment was conducted in an industrial environment that produced aircraft parts, using industrial equipment, [...] Read more.
The paper presents a study regarding the tool-life of uncoated and DLC-coated cutting inserts used for machining aluminum–lithium components used in the structure of the Airbus A350 aircraft. The experiment was conducted in an industrial environment that produced aircraft parts, using industrial equipment, under serial processing conditions during 5874 machining hours, resulting in 1440 samples. The experimental results were used as the input data for obtaining predictive models for the estimation of the tool-life machining supervised learning from MATLAB 2025b based on four machine-learning algorithms: trainlm and trainbr (artificial neural networks), fitrtree (decision trees), and fitrensemble (ensemble methods) respectively. The models were evaluated and compared in terms of their performance, which determined the best option. Also, a sensitive analysis of the five predictors was performed. The validation of the four learning algorithms was performed based on a separate set of experimental data, which was not used in learning. The analysis between the experimental results and those predicted by the learning models confirmed their robustness. The analysis between the experimental results and those predicted concluded the best model. Full article
(This article belongs to the Special Issue Surface Integrity and Engineering Optimization in Advanced Manufacturing and Mechanical Systems)
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23 pages, 4096 KB  
Article
Prediction of the Surface Quality Obtained by Milling Using Artificial Intelligence Methods
by Andrei Osan, Mihai Banica and Cornel Florian
Coatings 2026, 16(4), 478; https://doi.org/10.3390/coatings16040478 - 16 Apr 2026
Viewed by 413
Abstract
The paper explores the use of artificial neural networks for surface roughness parameter Ra prediction when milling the finishing of flat surfaces with toroidal milling on C45 steel. The experiments were conducted on a 5-axis CNC center, varying three main parameters: cutting speed, [...] Read more.
The paper explores the use of artificial neural networks for surface roughness parameter Ra prediction when milling the finishing of flat surfaces with toroidal milling on C45 steel. The experiments were conducted on a 5-axis CNC center, varying three main parameters: cutting speed, feed per tooth, and tool axis tilt angle. In total, 70 surfaces were processed, with multiple measurements of Ra roughness. The data were preprocessed in MATLAB (noise reduction by Z-score and augmentation to 630 values) and used to train an artificial feedforward neural network with Bayesian regularization. The resulting model showed good performance on the dataset and was experimentally validated on three new parameter combinations, processed and measured independently with a 3D scanner. The results confirm the network’s ability to estimate Ra roughness based on varying process parameters. The paper proposes the model as a useful tool for assessing surface quality in finishing milling and recommends extending the experimental base as the main direction of continuation. Full article
(This article belongs to the Special Issue Surface Integrity and Engineering Optimization in Advanced Manufacturing and Mechanical Systems)
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