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Cutting Process of Advanced Materials
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Cutting Process of Advanced Materials

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 2758

Special Issue Editor

Dr. Łukasz Ślusarczyk

E-Mail Website
Guest Editor
Mechanical Faculty, Cracow University of Technology, Al. Jana Pawła II 37, 31-864 Kraków, Poland
Interests: physical aspects of the cutting process; modeling and simulation of machining processes; numerical calculation methods (FEM); construction and operation of machines; CNC machine tools; CAD/CAM systems; measurements of the geometric surface structure

Special Issue Information

Dear Colleagues,

A recent dynamic progression in many fields of technology drives the development of modern, advanced structural materials with complex mechanical, physical, and chemical properties. Such materials include titanium alloys, nickel alloys, special ceramics, materials with high hardness (up to 70 HRC), chemical and thermal resistance (resistance to wear, oxidation, corrosion), composites, plastics, etc.

Advanced structural materials play a significant role in the manufacturing technology of machine parts, particularly in sectors such as aerospace, automotive, and tooling industries, and the manufacture of casting molds. The practical use of these materials in industry is closely related to the need to develop new and improve existing manufacturing technologies.

Progress in this area is measured mainly by the increase in accuracy and quality of machined surfaces obtained by various types of machining, such as turning, milling, or grinding, and by using hybrid machining processes.

This Special Issue intends to present the latest developments in the machining of advanced structural materials, in particular the CNC machining, the use of modern cutting tools, modeling and computer simulation of machining, and analysis of physical phenomena occurring in the decohesion zone of the machined material. 

Please feel free to submit original, high-quality research, short messages, and the latest reviews to this Special Issue.

Dr. Łukasz Ślusarczyk
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly 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

  • machining
  • simulation of cutting processes
  • physical phenomena during cutting
  • optimization of manufacturing
  • material models
  • difficult-to-cut materials

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Published Papers (3 papers)

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Research

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22 pages, 6811 KB  
Article
Sound-Based Tool Wear Classification in Turning of AISI 316L Using Multidomain Acoustic Features and SHAP-Enhanced Gradient Boosting Models
by Savaş Koç, Mehmet Şükrü Adin, Ramazan İlenç, Mateusz Bronis and Serdar Ekinci
Materials 2026, 19(5), 861; https://doi.org/10.3390/ma19050861 - 25 Feb 2026
Cited by 1 | Viewed by 595
Abstract
Reliable tool-wear monitoring is essential for maintaining machining quality and preventing unscheduled downtime in manufacturing. This investigation presents a sound-based classification framework for identifying wear states in the turning of AISI 316L stainless steel using advanced gradient-boosting models. Acoustic signals were recorded under [...] Read more.
Reliable tool-wear monitoring is essential for maintaining machining quality and preventing unscheduled downtime in manufacturing. This investigation presents a sound-based classification framework for identifying wear states in the turning of AISI 316L stainless steel using advanced gradient-boosting models. Acoustic signals were recorded under constant cutting parameters to eliminate process-induced variability, and each recording was divided into standardized 2 s segments. A total of 540 multidomain features—including RMS, ZCR, spectral descriptors, Mel-spectrogram statistics, MFCCs and their derivatives, and discrete wavelet energies—were extracted to capture both stationary and transient characteristics of tool–workpiece interactions. Feature selection was performed using a three-stage pipeline comprising Boruta, LASSO, and SHAP analysis, resulting in a compact subset of highly informative descriptors. LightGBM, XGBoost, and CatBoost classifiers were trained using stratified 10-fold cross-validation across three wear states: Unworn, Slight wear, and Severe wear. LightGBM and XGBoost achieved the best performance, with mean accuracies above 0.96 and strong PRC–AUC and ROC–AUC values (0.98–1.00). Although Slight wear remained the most difficult class due to its transitional acoustic characteristics, all models showed clear separability for Unworn and Severe wear conditions. The results confirm that boosted decision-tree methods combined with SHAP-enhanced feature selection provide an effective, low-cost, and non-contact solution for tool-wear classification in 316L turning. Full article
(This article belongs to the Special Issue Cutting Process of Advanced Materials)
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33 pages, 8608 KB  
Article
Multi-Response Optimization of Drilling Parameters in Direct Hot-Pressed Al/B4C/SiC Hybrid Composites Using Taguchi-Based Entropy–CoCoSo Method
by Gokhan Basar, Funda Kahraman and Oguzhan Der
Materials 2025, 18(18), 4319; https://doi.org/10.3390/ma18184319 - 15 Sep 2025
Cited by 8 | Viewed by 1084
Abstract
In this study, aluminium matrix hybrid composites reinforced with boron carbide (B4C) and silicon carbide (SiC) were fabricated using the direct hot-pressing technique under 35 MPa pressure at 600 °C for 5 min. Particle size distribution and scanning electron microscope analysis [...] Read more.
In this study, aluminium matrix hybrid composites reinforced with boron carbide (B4C) and silicon carbide (SiC) were fabricated using the direct hot-pressing technique under 35 MPa pressure at 600 °C for 5 min. Particle size distribution and scanning electron microscope analysis were conducted for the input powders. The microstructure, mechanical properties, and drillability of the fabricated composites were examined. As the SiC content increased, the density decreased, hardness improved, and transverse rupture strength declined. Drilling experiments were performed based on the Taguchi L18 orthogonal array. The control factors included cutting speed (25 and 50 m/min), feed rate (0.08, 0.16, and 0.24 mm/rev), point angle (100°, 118°, and 136°), and SiC content (0%, 5%, and 10%). Quality characteristics such as thrust force, torque, surface quality indicators, diameter deviation, and circularity deviation were evaluated. The Taguchi method was applied for single-response optimization, while the Entropy-weighted, Taguchi-based CoCoSo method was used for multi-response optimization. Analysis of Variance was conducted to assess factor significance, and regression analysis was used to model relationships between inputs and responses, yielding high R2 values. The optimal drilling performance was achieved at 50 m/min, 0.08 mm/rev, 136°, and 10% SiC, and the confirmation tests verified these results within the 95% confidence interval. Full article
(This article belongs to the Special Issue Cutting Process of Advanced Materials)
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Review

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27 pages, 2825 KB  
Review
Research Progress in Multidimensional Prediction of Machining-Induced Surface Residual Stress
by Zichuan Zou, Xinxin Zhang and Wei Gong
Materials 2026, 19(3), 510; https://doi.org/10.3390/ma19030510 - 27 Jan 2026
Viewed by 553
Abstract
Intense thermo-mechanical coupling effects during cutting generate residual stress within the surface layer of a workpiece. This residual stress is a critical factor influencing the fatigue life, corrosion resistance, and dimensional stability of mechanical components, making its accurate prediction and control essential for [...] Read more.
Intense thermo-mechanical coupling effects during cutting generate residual stress within the surface layer of a workpiece. This residual stress is a critical factor influencing the fatigue life, corrosion resistance, and dimensional stability of mechanical components, making its accurate prediction and control essential for improving product performance. To address the often generalized treatment of residual stress prediction modeling in existing literature, this paper presents a systematic review of recent advances in surface residual stress prediction for cutting operations. It details the formation mechanisms and significance of residual stress, focusing on four primary modeling approaches: empirical models based on experimental data, analytical models founded on metal cutting and elastoplastic theory, finite element models that simulate actual machining conditions, and hybrid models. A comprehensive analysis and comparison of these four model types is provided, summarizing their respective advantages and limitations. Furthermore, this paper identifies potential future research directions and development trends in residual stress prediction modeling, serving as a valuable reference for work in this field. Full article
(This article belongs to the Special Issue Cutting Process of Advanced Materials)
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