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Emerging Trends and Technologies in Manufacturing Engineering
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Emerging Trends and Technologies in Manufacturing Engineering

A Special Issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: closed (31 August 2026) | Viewed by 20803

Editors

Dr. Csaba Felhő

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. István Sztankovics

E-Mail Website
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 Issues, Collections and Topics in MDPI journals

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

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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. 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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Related Special Issue

Published Papers (19 papers)

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18 pages, 9782 KB  
Article
Application of Statistical Quality-Analysis Tools in the 8D Methodology for Manufacturing Quality Improvement
by Arícia Motta, Beatriz Castro, Jocieli Fouchy, Lorena Viana and António Rocha
Eng 2026, 7(9), 490; https://doi.org/10.3390/eng7090490 - 21 Sep 2026
Abstract
This study investigates and resolves a pin warping defect in an aluminum component supplied to the automotive industry, applying the eight-discipline (8D) structured problem-solving methodology, supported by established statistical and quality-analysis tools within a statistical quality-control framework. A cross-functional team conducted root cause [...] Read more.
This study investigates and resolves a pin warping defect in an aluminum component supplied to the automotive industry, applying the eight-discipline (8D) structured problem-solving methodology, supported by established statistical and quality-analysis tools within a statistical quality-control framework. A cross-functional team conducted root cause analysis using a cause-and-effect diagram and Minitab statistical software, complemented by process capability studies and non-parametric hypothesis tests on pin angle measurements from two injection moulding machines. Results demonstrated that manual handling of hot sprues—arising from an automation failure and compounded by the absence of standardized work instructions and operator training—was the primary cause of excessive pin warping. Statistical analysis confirmed significant variability in the manually operated process (Mann–Whitney U-test p = 0.005; Cpk < 0), in contrast to the automated process (Cp = 2.36). Corrective actions encompassing process standardization, operator training, and re-automation were implemented and validated, substantially reducing defect occurrence and restoring the automated process operation. The P-FMEA was updated to prevent recurrence. This work demonstrates the practical value of combining structured problem-solving with statistical analysis as an applied framework for diagnosing and resolving manufacturing defects, with particular relevance to environments where automation failures introduce process variability. Full article
(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)
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20 pages, 5080 KB  
Article
Effects of Multi-Modality on the Dynamic Stability in Milling of Typical Thin-Walled Structural Components
by Xiaorong Wei, Jun Liu, Fei Li, Zhe Feng and Tengju Li
Eng 2026, 7(9), 487; https://doi.org/10.3390/eng7090487 - 20 Sep 2026
Abstract
Thin-walled structural components possess low structural stiffness and are prone to regenerative chatter in milling, while continuous material removal leads to time-varying dynamic characteristics during machining. For traditional methods concerning multi-modal stability analysis, neither the minimum envelope method nor the comprehensive modal method [...] Read more.
Thin-walled structural components possess low structural stiffness and are prone to regenerative chatter in milling, while continuous material removal leads to time-varying dynamic characteristics during machining. For traditional methods concerning multi-modal stability analysis, neither the minimum envelope method nor the comprehensive modal method is capable of accurately predicting the specific chatter mode within a given cutting region prior to machining. Against this background, this study focuses on the identification of individual modal contributions before machining, aiming to reveal the influence mechanism of different modes on the stability of typical thin-walled structures within a given cutting region. A discretization-based stability lobe prediction model is constructed, which integrates multi-node contact characteristics within the tool–workpiece interaction region and considers the time-varying evolution of dynamic parameters induced by both material removal and variable tool positions. Stability prediction is individually performed for the first three dominant modes under different machining stages and tool positions to quantitatively distinguish the independent contribution of each mode. Milling experiments on typical rectangular thin-walled components are conducted, and the multi-modal stability mechanism is comprehensively illustrated through surface roughness analysis, real-time vibration analysis, and FFT spectra of the signals analysis. The results suggest that the second-order torsional node and the third-order bending–torsion nodes can effectively suppress the corresponding chatter modes within the given cutting region. Further validation demonstrates that multimodal effects are indispensable for reliable stability prediction in thin-walled component milling. Full article
(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)
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18 pages, 5736 KB  
Article
Application-Driven Analysis of MEX Parameters for Deformation Control in Annealed HTPLA Patient-Specific Instruments
by Leonardo Frizziero, Grazia Chiara Menozzi, Andrea Montalti, Giulia Alessandri, Paola Papaleo, Giovanni Trisolino and Gino Rocca
Eng 2026, 7(9), 478; https://doi.org/10.3390/eng7090478 - 15 Sep 2026
Viewed by 116
Abstract
Computer-Aided Design (CAD) and Additive Manufacturing (AM), particularly Material Extrusion (MEX) with PLA, are increasingly used in medicine for cost-effective production of sterilizable Patient-Specific Instruments (PSIs). However, dimensional stability remains challenging when large geometries undergo post-processing such as annealing prior to sterilization. This [...] Read more.
Computer-Aided Design (CAD) and Additive Manufacturing (AM), particularly Material Extrusion (MEX) with PLA, are increasingly used in medicine for cost-effective production of sterilizable Patient-Specific Instruments (PSIs). However, dimensional stability remains challenging when large geometries undergo post-processing such as annealing prior to sterilization. This study investigates the influence of selected MEX parameters on deformation in a clinically relevant large-scale PSI. A replicated 24 factorial design was used to evaluate the effect of raft thickness, infill orientation, first-layer pattern, and cooling fan speed through overall 3D and cross-sectional deformation. Infill orientation affected all deformation responses, while fan speed mainly influenced overall 3D and longitudinal deformation. Raft thickness affected the cross-section parallel to the build plane. Interactions involving fan speed were also significant for the overall 3D response. A 25% fan speed, 90° infill orientation, and 6-layer raft were associated with the lowest predicted overall deformation. These findings demonstrate that dimensional stability in large MEX-printed High-Temperature Polylactic Acid (HTPLA) PSIs depends on both individual process parameters and their interactions, with local deformation behaviour differing from the overall response. The results provide a basis for controlling deformation in large PSIs and support further investigations incorporating additional process levels, different PSIs geometries, and sterilization effects. Full article
(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)
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26 pages, 9500 KB  
Article
Optimizing Lightweight Automotive Connecting Rods Through Integrated Reverse Engineering and Lattice-Based Topology Methods
by Mohamed A. Daha, Islam Shyha, Dehong Huo, Mohamed Elkholy and May M. Youssef
Eng 2026, 7(9), 443; https://doi.org/10.3390/eng7090443 - 1 Sep 2026
Viewed by 218
Abstract
This study introduces an integrated, reverse-engineering-driven framework for optimizing automotive connecting rods under real-world industrial constraints. The geometric characteristics and material composition of a legacy component were experimentally characterized using high-resolution 3D scanning (Artec Spider Studio 16) and spectroscopy. The resulting data were [...] Read more.
This study introduces an integrated, reverse-engineering-driven framework for optimizing automotive connecting rods under real-world industrial constraints. The geometric characteristics and material composition of a legacy component were experimentally characterized using high-resolution 3D scanning (Artec Spider Studio 16) and spectroscopy. The resulting data were incorporated into the optimization workflow. Computer-aided design tools, including SolidWorks 2022® Premium 2022 × 64 (Dassault Systèmes SolidWorks Corporation, Waltham, MA, USA) and Geomagic Design X 2022 (3D Systems, Rock Hill, SC, USA), captured precise geometry, while spectroscopy confirmed cast iron as the base material. An upgrade to structural steel is proposed, offering the potential for improved strength-to-weight performance. Although reverse engineering and topology optimization are well-established, previous studies have largely focused on idealized models and unconstrained domains. ANSYS Workbench 2022 R1 (ANSYS Inc., Canonsburg, PA, USA) topology optimization is constrained to preserve functional interfaces and manufacturability. Motivated by the demand for lightweight automotive solutions in the Middle East, this workflow achieves 2.63% mass reduction and an estimated 6.6% cost savings based on prior benchmarks. Incorporating the Octet lattice structure (nTopology, 2022 (nTopology Inc., New York, NY, USA)) further improves performance, yielding a 6.4% mass reduction and a 16% cost reduction. The optimized design is considered a promising candidate for future additive manufacturing and Industry 4.0 implementation. This preliminary procedure provides a transferable template for redesign, static evaluation, and experimental validation of legacy components, bridging the gap between academic research and industrial practice. Full article
(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)
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27 pages, 996 KB  
Article
Evaluation and Prediction Methods for a Steel Company Using Six Sigma Metrics, Capability Indicators, and Markov Chains
by Tomás José Fontalvo Herrera, Enrique J. Delahoz-Domínguez and Neiser Rodelo Barrios
Eng 2026, 7(8), 383; https://doi.org/10.3390/eng7080383 - 4 Aug 2026
Viewed by 445
Abstract
The operational dynamics of the steel industry constitute one of the work systems with the highest severity and accident rates. To address this, this research multidimensionally evaluates and stochastically predicts the preventive capability of the safety system in a steel plant. Using a [...] Read more.
The operational dynamics of the steel industry constitute one of the work systems with the highest severity and accident rates. To address this, this research multidimensionally evaluates and stochastically predicts the preventive capability of the safety system in a steel plant. Using a quantitative, evaluative, and longitudinal three-phase design, the retrospective evaluation of nine preventive variables was employed using Six Sigma metrics (DPMO, Z, Y), along with the evaluation of overall performance through the Geometric Capability Indicator (GCI) and the Arithmetic Capability Indicator (ACI), and the stochastic modeling of the process using Markov chains. It was demonstrated that evaluating processes in isolation hides structural inefficiencies, as four variables showed an Excellent individual performance (Z≈6.0), but the comprehensive multivariate evaluation revealed a Deficient systemic state (GCI of 0.471 and ACI of 0.493). Furthermore, Markov modeling on the compliance of the process management index predicted a 100% probability of long-term stagnation in a deficient absorbing state (x1=1). It is concluded that the proposed method functions as a rational anticipation system that provides potential managerial benefits by offering early warning indicators of operational degradation, supporting corrective decision-making on unstable preventive indicators. Full article
(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)
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27 pages, 782 KB  
Article
Enhancing Process Safety and Manufacturing Performance Through 5M-Based Causal Analysis and Source–Detection Matrices
by Mirel Glevitzky, Paul Mucea-Ștef, Ioana Glevitzky, Doriana Maria Popa and Maria Popa
Eng 2026, 7(6), 258; https://doi.org/10.3390/eng7060258 - 27 May 2026
Viewed by 902
Abstract
Occupational safety is increasingly addressed through engineering-based, proactive risk management approaches that emphasize early identification and mitigation of hazards within industrial systems. In manufacturing environments, the analysis of minor process deviations, near misses, and low-impact operational failures provides critical insights for improving system [...] Read more.
Occupational safety is increasingly addressed through engineering-based, proactive risk management approaches that emphasize early identification and mitigation of hazards within industrial systems. In manufacturing environments, the analysis of minor process deviations, near misses, and low-impact operational failures provides critical insights for improving system reliability and safety performance. This paper proposes an integrated engineering framework that combines the 5M model (Environment, Man, Method, Material, Machine) with Source–Detection Matrix analysis to support structured identification, classification, and control of safety-related process deviations. The approach enables systematic root-cause analysis by categorizing contributing factors according to the 5M model, followed by mapping each deviation based on its origin and point of detection within production processes. The methodology was validated through case studies conducted in cosmetics and perfumery manufacturing, involving process-related hazards such as electrical failures and exposure to volatile substances. Validation was also supported by operational data collected over two 6-month periods before (n1) and after implementation (n2), based on incident reports, near-miss records, nonconformity reports, and internal audit data (n1 = 128; n2 = 95). Quantitative results show improved safety performance, including an increase in Detection at Source Rate from 42% to 74% and a reduction in Minor Incident Frequency from 11 to 5 cases/month. The results demonstrate that integrating causal analysis with detection mapping enhances early-stage identification of process deviations, effectively limiting failure propagation across operational stages and improving overall process safety performance. The proposed framework provides a practical and data-driven tool for improving process reliability, operational safety, and continuous improvement in complex manufacturing environments. Full article
(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)
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21 pages, 1949 KB  
Article
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
Cited by 1 | Viewed by 656
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 [...] Read more.
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  
Article
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
Cited by 1 | Viewed by 1327
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 [...] Read more.
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  
Article
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 1126
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 [...] Read more.
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  
Article
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 1308
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 [...] Read more.
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  
Article
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
Cited by 1 | Viewed by 1251
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 [...] Read more.
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  
Article
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
Cited by 1 | Viewed by 1145
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 [...] Read more.
(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  
Article
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 5 | Viewed by 1425
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, [...] Read more.
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 (Rq) 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  
Article
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
Cited by 2 | Viewed by 1317
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 [...] Read more.
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 Ra, 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  
Article
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
Cited by 1 | Viewed by 3069
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 [...] Read more.
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  
Article
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 3 | Viewed by 1704
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 [...] Read more.
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  
Article
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 1493
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 [...] Read more.
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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Review

Jump to: Research

48 pages, 10002 KB  
Review
A Unified DMAIC-Based Conceptual Framework for Lean Six Sigma and Industry 4.0 Integration in Electronics Manufacturing Services
by Yasser Ibrahim, Mohamed Thariq Hameed Sultan, Jan Lean Tai and Navaneetha Krishna Chandran
Eng 2026, 7(8), 364; https://doi.org/10.3390/eng7080364 - 24 Jul 2026
Viewed by 660
Abstract
The convergence of Lean Six Sigma and Industry 4.0 has emerged as a critical pathway toward intelligent manufacturing transformation. However, existing integration studies remain fragmented across technologies, DMAIC phases, and industrial contexts, with limited deployment architectures tailored to Electronics Manufacturing Services environments. This [...] Read more.
The convergence of Lean Six Sigma and Industry 4.0 has emerged as a critical pathway toward intelligent manufacturing transformation. However, existing integration studies remain fragmented across technologies, DMAIC phases, and industrial contexts, with limited deployment architectures tailored to Electronics Manufacturing Services environments. This study addresses this gap by conducting a systematic literature review of Lean Six Sigma–Industry 4.0 integration research and developing a unified DMAIC-based conceptual framework specifically designed for EMS. Using a PRISMA-guided systematic review methodology, the selected studies were evaluated through quality appraisal, multidimensional analytical coding, quantitative diagnostic analysis, and cross-dimensional synthesis to move beyond descriptive literature mapping toward mechanism-based interpretation. The findings reveal that Industry 4.0 technologies are heavily concentrated in the Measure and Analyze phases, whereas Define and Control remain underdeveloped, resulting in structurally imbalanced maturity. To address these limitations, this study proposes an EMS-oriented conceptual framework that adapts established DMAIC practices by integrating Industry 4.0 technologies, deployment readiness criteria, digital traceability, and data-driven decision support into unified conceptual architecture. Rather than introducing a new DMAIC methodology, the framework contextualizes existing Lean Six Sigma principles for Electronics Manufacturing Services (EMS), providing a structured approach for intelligent continuous improvement in defect-sensitive manufacturing environments. Unlike previous frameworks that primarily associate Industry 4.0 technologies with individual DMAIC phases, the proposed framework introduces deployment readiness assessment, adaptive decision-gate mechanisms, enterprise-specific conceptual guidance, and EMS-oriented operational integration within a unified conceptual architecture. Full article
(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)
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23 pages, 1697 KB  
Review
Modeling Options in Injection Molding Simulation
by Kaiyu Cai and Jose Castro
Eng 2026, 7(7), 348; https://doi.org/10.3390/eng7070348 - 16 Jul 2026
Viewed by 727
Abstract
Injection molding is one of the most widely adopted manufacturing methods in the plastics industry, owing to its high efficiency, design flexibility, and mass production capabilities. Throughout the development and application of Injection molding technology, trade-offs are pervasive, arising from competing requirements such [...] Read more.
Injection molding is one of the most widely adopted manufacturing methods in the plastics industry, owing to its high efficiency, design flexibility, and mass production capabilities. Throughout the development and application of Injection molding technology, trade-offs are pervasive, arising from competing requirements such as processability versus material performance, productivity versus quality, and simplicity versus functionality. Injection molding simulation itself embodies such trade-offs, as it is used to design increasingly complex processes and mold systems to achieve improved material properties and part performance, while inevitably balancing physical accuracy against computational efficiency and modeling cost. This review examines typical modeling options in injection-molding simulation from an accuracy–complexity trade-off perspective. The modeling strategies adopted in the primary stages of the molding cycle—namely, the injection, packing, and cooling phases—are systematically reviewed, with emphasis on how simplifying assumptions are introduced to manage numerical complexity. By organizing existing research through the lens of qualitative trade-offs, this review aims to support a more structured understanding of model selection in injection molding simulation for both academic studies and industrial applications. Full article
(This article belongs to the Special Issue Emerging Trends and Technologies in Manufacturing Engineering)
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