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A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 8325

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Special Issue Editors

Dr. Csaba Felhő

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Guest Editor

Institute of Manufacturing Science, University of Miskolc, H-3515 Miskolc, Hungary
Interests: surface roughness investigation on machined surfaces; CAD/CAM systems; process monitoring; Industry 4.0

Dr. István Sztankovics

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Guest Editor

Institute of Manufacturing Science, University of Miskolc, H-3515 Miskolc, Hungary
Interests: manufacturing technology; process planning; assembly; cutting theory; surface roughness; constructive geometric modeling; high-feed machining

Special Issue Information

Dear Colleagues,

This Special Issue, “Emerging Trends and Technologies in Manufacturing Engineering”, aims to highlight the latest scientific and technological advancements that are shaping the future of manufacturing. We invite researchers to contribute original manuscripts that address innovations in precision machining, cutting theory, and additive manufacturing, as well as developments in nontraditional machining and material forming. Contributions focusing on sustainable manufacturing and quality control are especially encouraged, reflecting the growing importance of efficiency, environmental responsibility, and product integrity. We are particularly interested in research integrating artificial intelligence into manufacturing processes, including predictive modeling, adaptive control, and data-driven process optimization. The issue also welcomes papers on process monitoring, in-line metrology, and real-time control strategies that support Industry 4.0 objectives. By bringing together diverse topics within manufacturing engineering, this Special Issue serves as a platform for academic and industrial researchers to present breakthroughs that enhance productivity, quality, reliability, and sustainability across various manufacturing fields. We look forward to receiving high-quality submissions that push the boundaries of modern manufacturing.

Dr. Csaba Felhő
Dr. István Sztankovics
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. Eng 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 1400 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

additive manufacturing
artificial intelligence in manufacturing
cutting theory
Industry 4.0
material forming
non-traditional machining
precision machining
process monitoring
quality control
sustainable manufacturing

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Published Papers (11 papers)

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Research

21 pages, 1949 KB

Open AccessArticle

Modification of the Tribomechanical Cutting Regime in Longitudinal-Torsional Ultrasonic Milling: From Adhesion to Controlled Fragmentation

by Oussama Beldi, Tarik Zarrouk, Ahmed Abbadi, Mohammed Nouari, Wenfeng Ding, Mohammed Abbadi, Jamal-Eddine Salhi and Mohammed Barboucha

Eng 2026, 7(4), 177; https://doi.org/10.3390/eng7040177 - 13 Apr 2026

Viewed by 229

Abstract

Machining Nomex honeycomb structures presents a major challenge due to their thin-walled architecture, orthotropic behavior, and sensitivity to adhesion and delamination. This study develops a three-dimensional numerical model using Abaqus/Explicit to analyze ultrasonic vibration-assisted milling in longitudinal and longitudinal-torsional modes. The model incorporates orthotropic behavior with progressive damage based on Tsai-Wu and experimental friction calibration to accurately reproduce tribological conditions. A parametric analysis examines the effect of vibration mode, amplitude (5–25 µm), frequency (21–22.5 kHz), cutting width, and tool geometry on stresses, bond wear, and material buildup. An optimal coefficient of friction ensures excellent simulation–experiment agreement. Compared to conventional milling, the longitudinal-torsional configuration reduces cutting forces by up to 50%, while frequency optimization allows for gains of 40 to 60%. Hybrid vibration coupling establishes intermittent contact and oscillatory micro-shearing, limiting adhesion and build-up. Thus, longitudinal-torsional assistance improves tribological stability, tool life and wall integrity, offering a validated digital strategy to optimize ultrasonic milling of composite honeycomb structures. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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20 pages, 3330 KB

Open AccessArticle

Multi-Objective Optimization of FDM Infill Patterns Using Design of Experiments Considering Load-Path Alignment

by Waqar Shehbaz and Qingjin Peng

Eng 2026, 7(4), 175; https://doi.org/10.3390/eng7040175 - 11 Apr 2026

Viewed by 291

Abstract

The roles of layer height, build orientation, and infill density in determining mechanical properties are well recognized in Fused Deposition Modelling (FDM). However, the combined influence of infill topology, density, and skin layer configuration on structural performance and resource efficiency has not been thoroughly investigated. This research presents a systematic multi-objective investigation of infill architectures, aiming to simultaneously maximize tensile strength and minimize printing time, material consumption, and energy usage. Six infill patterns (concentric, line, triangle, honeycomb, grid, and gyroid) were evaluated at three density levels (50%, 75%, and 90%) across multiple skin layer configurations using an L36 orthogonal experimental design. Analysis of variance (ANOVA) quantified the relative significance of process parameters on tensile performance. The results reveal that the infill topology strongly influences tensile strength, with continuous, load-aligned filament paths (concentric, linear, and gyroid) outperforming segmented lattice geometries. Notably, the concentric infill pattern achieved the highest tensile performance while simultaneously reducing printing time, material usage, and energy consumption. This performance is attributed to enhanced load transfer along continuous filament trajectories, which mitigates stress concentrations at filament junctions and interlayer interfaces. These findings provide a novel, design-oriented framework for optimizing FDM infill architectures and demonstrate that strategic topology selection can improve both mechanical efficiency and sustainability without relying solely on high-density infill. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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35 pages, 2827 KB

Open AccessArticle

A Hybrid Regression and Machine Learning-Based Multi-Output Predictive Modeling of Cutting Forces and Surface Roughness in Rotational Turning of C45 Steel

by István Sztankovics

Eng 2026, 7(4), 154; https://doi.org/10.3390/eng7040154 - 31 Mar 2026

Viewed by 298

Abstract

Rotational turning is a hybrid machining process that combines features of milling and conventional turning, resulting in altered chip formation and force generation mechanisms. Despite its technological relevance, the predictive modeling of cutting forces and surface roughness in rotational turning has received little attention. This study applies and evaluates a hybrid regression and machine learning modeling for the multi-output prediction of three cutting force components and two surface roughness parameters during rotational turning of normalized C45 steel. The input variables are tool inclination angle, depth of cut, feed, and cutting speed. Three modeling approaches are compared: stepwise polynomial regression, Gaussian Process Regression, and Random Forest regression, using repeated five-fold cross-validation with ten repetitions. The results show that Gaussian Process Regression provides the highest predictive accuracy for most outputs, particularly for axial and radial forces and roughness parameters, while stepwise regression achieves comparable performance for tangential force with greater interpretability. Random Forest regression exhibits lower accuracy under the structured experimental design. The study demonstrates that combining interpretable regression with probabilistic machine learning enables the accurate prediction of process responses in rotational turning. The proposed methodology represents a novel, statistically validated approach for multi-output modeling of this machining process and supports future applications in process optimization and adaptive manufacturing systems. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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22 pages, 1759 KB

Open AccessArticle

A Framework for Integrated Maintenance of a Multi-Robot Packaging Workcell

by Daynier Rolando Delgado Sobrino, Matej Bilačič, Radovan Holubek, Miroslav Škuba, Csaba Felhő and Tanuj Namboodri

Eng 2026, 7(3), 134; https://doi.org/10.3390/eng7030134 - 14 Mar 2026

Viewed by 420

Abstract

The increasing deployment of collaborative and industrial robots in manufacturing systems places high demands on equipment reliability, availability, and maintenance efficiency. Robotic workcells, in which multiple automated subsystems operate in tightly coordinated cycles, are particularly sensitive to unplanned downtime, as failures of individual components can disrupt the entire production process. Traditional time-based preventive maintenance is often insufficient under such conditions, as it does not adequately reflect actual operating loads or component degradation. This paper proposes a structured framework for the design of an integrated maintenance concept for a multi-robot packaging workcell. The framework systematically combines component identification, criticality assessment, and the selection of appropriate maintenance strategies, including preventive, predictive, corrective, proactive, and reactive approaches. Preventive maintenance is complemented by condition-based monitoring and trend analysis of selected diagnostic parameters, enabling predictive decision-making for critical components. The proposed methodology further integrates maintenance planning and performance evaluation through a computerized maintenance management system (CMMS), supporting the coordination of maintenance activities and the assessment of key performance indicators. The novelty of the proposed framework lies primarily in the dynamic allocation of maintenance strategies based on semi-quantified component criticality and in the structured integration of predictive diagnostic information with CMMS-supported maintenance planning. Unlike traditional RCM-based or single-strategy maintenance approaches, the framework enables coordinated preventive, predictive, corrective, proactive, and reactive actions within a unified decision-making architecture, supporting proactive continuous improvement of maintenance performance through a closed-loop feedback mechanism that updates component criticality based on real-time operational data. The framework is demonstrated on a robotic workcell comprising a collaborative robot, an industrial robot, pneumatic subsystems, and a centralized control architecture. The results suggest that the integrated approach may provide a coherent basis for reducing reactive maintenance actions, improving system availability, and supporting data-driven maintenance planning. As a conceptual framework with partial (pilot) practical implementation within the context of this paper, the proposed approach establishes a foundation for future broader implementation, experimental validation and the integration of advanced diagnostic and prognostic methods, mainly in the context of multi-Robot workcell and production process maintenance. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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20 pages, 4659 KB

Open AccessArticle

Optimisation of 3D Printing Parameters to Enhance the Ultimate Tensile Strength of PA6 Polymer Products

by Jure Marijić, Mirko Karakašić, Ivan Grgić and Željko Ivandić

Eng 2026, 7(3), 127; https://doi.org/10.3390/eng7030127 - 10 Mar 2026

Viewed by 420

Abstract

Additive manufacturing (AM) technologies are a key tool in producing complex and functional polymer parts, with Fused Deposition Modelling (FDM) emerging as the most widely used technique. PA6 polyamide is gaining increasing importance due to its high strength, wear resistance and processability, making it suitable for polymer product manufacturing. However, the mechanical properties of PA6 FDM components are largely determined by process parameters, and their optimisation is necessary to achieve stable and reliable properties. In this study, the influence of nozzle temperature, infill density and infill geometry on the tensile strength of PA6 specimens was investigated. The Central Composite Design (CCD) method was used for process modelling and optimisation, along with statistical analysis and experimental validation. The individual effects of the analysed parameters were confirmed by a preliminary experiment, while a detailed analysis of their mutual relationships was enabled through the main experiment. Analysis of the results showed that increasing both temperature and infill density positively affects tensile strength, regardless of the infill structure. The accuracy and reliability of the model were confirmed by validation, with a coefficient of determination R² = 0.8958 and a high level of agreement between experimental and predicted data. By optimising the process parameters, maximum tensile stresses of 17.705 MPa were achieved with an infill density of 74.142%, a Triangle-Hexa infill pattern, and a nozzle temperature of 254.142 °C. The confirmation experiment validated the optimised parameters, and the results provide a statistically validated framework for optimising the tensile performance of PA6 components manufactured by FDM under controlled laboratory conditions. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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19 pages, 2709 KB

Open AccessArticle

Design Compensation in Pin-Hole Dimensional Changes in Annealed FDM HTPLA Cutting Guides for Orthopedic Surgery

by Leonardo Frizziero, Grazia Chiara Menozzi, Giulia Alessandri, Alessandro Depaoli, Giampiero Donnici, Paola Papaleo, Giovanni Trisolino and Gino Rocca

Eng 2026, 7(2), 63; https://doi.org/10.3390/eng7020063 - 1 Feb 2026

Viewed by 429

Abstract

(1) Background: HTPLA FDM-printed cutting guides enable the low-cost, in-hospital production of patient-specific instruments. However, annealing, which is required for steam sterilization, may alter the dimensions of fit-critical fixation pin holes. (2) Methods: HTPLA cylindrical specimens (height 5 mm) were printed with fixed process parameters and vertical orientation. Inner diameter (1.6–5.0 mm) and wall thickness (2–6 mm) were varied using a two-factor Central Composite Design (n = 13). Following a two-stage annealing treatment (80 °C, 10 min; 100 °C, 50 min), post-annealing dimensions were measured and modeled using Response Surface Methodology. An illustrative verification was performed on additional specimens. (3) Results: Annealing induced a systematic decrease in inner diameter (−0.4 to −0.9 mm) and an increase in wall thickness (+0.1 to +0.4 mm). A reduced quadratic model accurately captured these trends within the investigated range, with small residuals observed during verification (≤0.1 mm). (4) Conclusions: The proposed local, geometry-driven model supports compensation in fixation pin-hole dimensions in annealed HTPLA cutting guides, improving dimensional predictability within a defined design and process window. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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18 pages, 13161 KB

Open AccessArticle

Analysis of Cutting Forces Response to Machining Parameters Under Dry and Wet Machining Conditions in X5CrNi18-10 Turning

by Csaba Felhő, Tanuj Namboodri and Daynier Rolando Delgado Sobrino

Eng 2026, 7(1), 33; https://doi.org/10.3390/eng7010033 - 8 Jan 2026

Cited by 3 | Viewed by 599

Abstract

The shift toward digital and smart manufacturing requires an accurate prediction of cutting behavior, such as cutting forces. Controlling cutting forces in machining is important for maintaining product quality, particularly in steels such as X5CrNi18-10. This steel has high toughness, which resists cutting, thereby increasing overall cutting forces. Proper selection of machining parameters and conditions can help reduce cutting forces during machining. Several studies have been dedicated to understanding the influence of cutting parameters on cutting forces. However, limited attention is given to the influence of the cutting conditions on cutting forces. The primary objective of this study is to understand the behavior of cutting forces in chromium-nickel alloy steel by varying machining parameters, specifically cutting conditions (dry and wet), using a full factorial (3¹ × 2²) design of experiments (DoE). The secondary objective is to develop a multilinear regression model to predict cutting forces. The root mean square (RMS) values of the cutting force components were calculated from the acquired data and analyzed using OriginPro 2025b. In addition, this study analyzes the effects of cutting parameters and cutting forces on root mean square (RMS) surface roughness (R_q) to understand their impact on quality using the AltiSurf 520 profilometer. The results suggest a significant effect of the selected machining parameters and conditions on cutting force reduction and on improved surface quality when cutting forces are low. This research provides a valuable insight into optimizing the machining process for hard steels. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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17 pages, 2706 KB

Open AccessArticle

Gaussian Process Modeling of EDM Performance Using a Taguchi Design

by Dragan Rodić, Milenko Sekulić, Borislav Savković, Anđelko Aleksić, Aleksandra Kosanović and Vladislav Blagojević

Eng 2026, 7(1), 14; https://doi.org/10.3390/eng7010014 - 1 Jan 2026

Viewed by 610

Abstract

Electrical discharge machining (EDM) is widely used for machining hard and difficult-to-cut materials; however, the complex and nonlinear nature of the process makes the accurate prediction of key performance indicators challenging, particularly when only limited experimental data are available. In this study, a combined Taguchi design and Gaussian process regression (GPR) modeling framework is proposed to predict the surface roughness (Ra), material removal rate (MRR), and overcut (OC) in die-sinking EDM. An L18 Taguchi orthogonal array was employed to efficiently design experiments involving discharge current, pulse duration, and electrode material. GPR models with an automatic relevance determination (ARD) radial basis function kernel were developed to capture nonlinear relationships and varying parameter relevance. Model performance was evaluated using strict leave-one-out cross-validation (LOOCV). The developed GPR models achieved low prediction errors, with RMSE (MAE) values of 0.54 µm (0.41 µm) for R_a, 1.56 mm³/min (1.21 mm³/min) for MRR, and 0.0065 mm (0.0055 mm) for OC, corresponding to approximately 9.8%, 5.4%, and 5.9% of the respective response ranges. These results confirm stable and reliable predictive accuracy within the investigated parameter domain. Based on the validated surrogate models, multi-objective optimization was performed to identify Pareto-optimal process conditions, revealing graphite electrodes as the dominant choice within the feasible operating region. The proposed approach demonstrates that accurate and robust prediction of EDM performance can be achieved even with compact experimental datasets, providing a practical tool for process analysis and optimization. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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19 pages, 1929 KB

Open AccessArticle

Detection and Classification of Defects on Metal Surfaces Based on a Lightweight YOLOX-Tiny COCO Network

by João Duarte, Manuel Fernandes Claro, Pedro M. A. Vitoriano, Tito G. Amaral and Vitor Fernão Pires

Eng 2025, 6(11), 302; https://doi.org/10.3390/eng6110302 - 1 Nov 2025

Viewed by 2042

Abstract

The detection of metallic surface defects is an essential task to control the quality of industrial products. During the production of metal materials, several defect types may appear on the surface, accompanied by a large amount of background texture information, leading to false or missing detections during small-defect detection. Computer vision is a crucial method for the automatic detection of defects. Yet, this remains a challenging problem, requiring the continuous development of new approaches and algorithms. Furthermore, many industries require fast and real-time detection. In this paper, a lightweight deep learning model is presented for implementation on embedded devices to perform in real time. The YOLOX-Tiny model is used for detecting and classifying metallic surface defect types. The YOLOX-Tiny has 5.06M parameters and only 6.45 GFLOPs, yet performs well, even with a smaller model size than its counterparts. Extensive experiments on the dataset demonstrate that the proposed model is robust and can meet the accuracy requirements for metallic defect detection. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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16 pages, 6452 KB

Open AccessArticle

Investigation of Wear Behavior for Innovative Cutting Tool in Machining AISI 304 Stainless Steel

by Jinxing Wu, Wenhao Hu, Yi Zhang, Yanying Wu, Changcheng Wu and Zuode Yang

Eng 2025, 6(9), 248; https://doi.org/10.3390/eng6090248 - 22 Sep 2025

Cited by 2 | Viewed by 1169

Abstract

AISI 304 stainless steel is widely used in the equipment manufacturing industry due to its excellent corrosion resistance. However, its high toughness and plasticity lead to severe tool wear during machining, significantly shortening the tool’s life. To mitigate tool wear, this study designed and fabricated a novel micro-groove structure on the tool’s rake face, aiming to reduce friction and thermal stress. The performance of the micro-groove tool was evaluated through cutting simulations and durability tests. Results demonstrate that this micro-groove structure effectively reduces cutting forces, suppresses tool wear, and improves chip control and heat dissipation. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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13 pages, 4980 KB

Open AccessArticle

Characterization of Transparent Surfaces Through Double Fringe Projection, Implementing a Frequency Filtering Technique and Spatial Phase Demodulation

by Ubaldo Uribe-López, David Asael Gutiérrez-Hernández, Víctor Zamudio-Rodríguez, Josué del Valle-Hernández, Daniel Olivares-Vera, Raúl Santiago-Montero, Miguel Gómez-Díaz and Dulce Aurora Velázquez-Vázquez

Eng 2025, 6(9), 244; https://doi.org/10.3390/eng6090244 - 15 Sep 2025

Viewed by 807

Abstract

This study introduces a novel, low-cost, and non-invasive method for characterizing the surface profile of transparent objects using double digital fringe projection (DDFP). By projecting dual sinusoidal patterns that generate a Moiré effect and applying a frequency-domain Gaussian filter, the system isolates relevant data for accurate phase recovery through the isotropic quadrature transform (IQT). Experimental validation with plastic and acrylic samples confirms the method’s high spatial resolution and robustness against ambient noise. Unlike traditional systems, this technique avoids coherent light sources and complex hardware, improving its accessibility for academic and industrial use in transparent surface metrology. Full article

(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)

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