1. Peridynamic modeling of fatigue in layered metal compositesFilip Jerenec, 2025, doctoral dissertation Abstract: In additive manufacturing, tooling is frequently built from laminated metal composites (LMCs) that stack dissimilar metals to shorten production times. Accurately predicting fatigue-crack growth in these unequal-strength laminates is difficult—particularly across joints where property mismatches steer crack paths. This work combines experiments and simulations to study fatigue-crack growth in an LMC formed by laser-melting AISI 316L powder onto high-strength structural steel S960. Crack growth was modeled with a linearized bond-based peridynamics (PD) formulation, with interfacial bonds tuned to the measured elastic-modulus gradient, and coupled to the Kinetic Theory of Fracture (KTF). Unlike conventional S–N–based approaches, KTF parameters were calibrated directly from measured crack-length–versus-cycles (a-N) data. Material behavior in the base metals and through the transition zone was characterized via tensile tests, hardness mapping, and nanoindentation; fatigue-crack growth data came from cyclic tests on compact-tension (CT) specimens. The model reproduced the observed differences in crack-growth rates between homogeneous and bimaterial specimens, and KTF parameter transferability across loads and geometries was confirmed for homogeneous samples. A key outcome is a practical calibration workflow for KTF within a nonlocal PD framework, enabling predictive simulation of fatigue-crack growth across multilayer metallic joints.
Keywords: Peridynamics, Fatigue Crack Growth, Kinetic Theory of Fracture, Layered Metal Composite, Additive Manufacturing
Published in DKUM: 13.01.2026; Views: 0; Downloads: 9
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2. Awareness and readiness of Industry 4.0 : the case of Turkish manufacturing industryT. Sari, H. K. Güleş, B. Yiğitol, 2020, original scientific article Abstract: The concept Industry 4.0 (I4.0) represents intelligent production processes combining cyber and physical systems through a set of technologies such as internet of things, big data and cloud computing. Transition to Industry 4.0 is expected to cause formidable structural changes, productivity increments and competitiveness in manufacturing industry in all over the world. This study aimed to investigate the general approach to the concept of Industry 4.0 and levels of adoption of the basic Industry 4.0 technologies in manufacturing firms across Turkey. For this purpose, a survey was conducted with 427 firms with various sizes (micro, small, medium and large) operating in six sub-sectors (automotive; electronic; machinery; chemical; food; and textile) of Turkish manufacturing. The paper examined nine I4.0 technologies: autonomous robots, big data applications, cloud computing, cyber security, simulation approaches, additive manufacturing, system integration, internet of things, and augmented reality. The results revealed that, there is a significant correlation between the degrees of importance and implementation of the basic Industry 4.0 technologies. Moreover, I4.0 implementation degree increases as the firm size increases. The top three industries in Turkish manufacturing that use the most basic Industry 4.0 technologies are automotive industry, electrical and electronics, and machinery, respectively. The analyses are aimed to achieve a better understanding of the concept Industry 4.0 by comparing different groups of manufacturers.
Keywords: industry 4.0, additive manufacturing, autonomous robots, cloud technologies, cyber security, internet of things, big data, augmented reality, intelligent production systems
Published in DKUM: 13.01.2026; Views: 0; Downloads: 0
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3. Predicting relative density of pure magnesium parts produced by laser powder bed fusion using XGBoostKristijan Šket, Snehashis Pal, Janez Gotlih, Mirko Ficko, Igor Drstvenšek, 2025, original scientific article Abstract: In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R2 test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage.
Keywords: additive manufacturing, machine learning, XG Boost, magnesium, relative density
Published in DKUM: 03.11.2025; Views: 0; Downloads: 6
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4. A case study on the design and implementation of a platform for hand rehabilitationTomaž Kosar, Lu Zhenli, Marjan Mernik, Marjan Horvat, Matej Črepinšek, 2021, original scientific article Abstract: Rehabilitation aids help people with temporal or permanent disabilities during the
rehabilitation process. However, these solutions are usually expensive and, consequently, inaccessible
outside of professional medical institutions. Rapid advances in software development, Internet of
Things (IoT), robotics, and additive manufacturing open up a way to affordable rehabilitation
solutions, even to the general population. Imagine a rehabilitation aid constructed from accessible
software and hardware with local production. Many obstacles exist to using such technology, starting
with the development of unified software for custom-made devices. In this paper, we address
open issues in designing rehabilitation aids by proposing an extensive rehabilitation platform. To
demonstrate our concept, we developed a unique platform, RehabHand. The main idea is to use
domain-specific language and code generation techniques to enable loosely coupled software and
hardware solutions. The main advantage of such separation is support for modular and a higher
abstraction level by enabling therapists to write rehabilitation exercises in natural, domain-specific
terminology and share them with patients. The same platform provides a hardware-independent
part that facilitates the integration of new rehabilitation devices. Experience in implementing
RehabHand with three different rehabilitation devices confirms that such rehabilitation technology
can be developed, and shows that implementing a hardware-independent rehabilitation platform
might not be as challenging as expected.
Keywords: movement observation, rehabilitation aid, assistive technology, robot-assisted rehabilitation, additive manufacturing, local production, human-computer interaction, code generation, domain-specific languages
Published in DKUM: 16.06.2025; Views: 0; Downloads: 7
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5. Predictive modelling of weld bead geometry in wire arc additive manufacturingKristijan Šket, Miran Brezočnik, Timi Karner, Rok Belšak, Mirko Ficko, Tomaž Vuherer, Janez Gotlih, 2025, original scientific article Abstract: This study investigates the predictive modelling of weld bead geometry in wire arc additive manufacturing (WAAM) through advanced machine learning methods. While WAAM is valued for its ability to produce large, complex metal parts with high deposition rates, precise control of the weld bead remains a critical challenge due to its influence on mechanical properties and dimensional accuracy. To address this problem, this study utilized machine learning approaches—Ridge regression, Lasso regression and Bayesian ridge regression, Random Forest and XGBoost—to predict the key weld bead characteristics, namely height, width and cross-sectional area. A Design of experiments (DOE) was used to systematically vary the welding current and travelling speed, with 3D weld bead geometries captured by laser scanning. Robust data pre-processing, including outlier detection and feature engineering, improved modelling accuracy. Among the models tested, XGBoost provided the highest prediction accuracy, emphasizing its potential for real-time control of WAAM processes. Overall, this study presents a comprehensive framework for predictive modelling and provides valuable insights for process optimization and the further development of intelligent manufacturing systems.
Keywords: wire arc additive manufacturing, WA AM, predictive modelling, machine learning, weld bead geometry, XGBoost
Published in DKUM: 13.03.2025; Views: 0; Downloads: 13
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6. Comparative analysis of a 3D printed polymer bonded magnet composed of a TPU-PA12 matrix and Nd-Fe-B atomised powder and melt spun flakes respectivelyGranit Hajra, Mihael Brunčko, Leo Gusel, Ivan Anžel, 2025, original scientific article Abstract: The present study reports the development of new polymer bonded magnet containing a Thermoplastic Polyurethane (TPU) – Nylon (PA12) blend as the matrix material and Nd-Fe-B magnetic particles. Two composite materials were explored: one using Nd-Fe-B atomised spherical powder (ASP) and another incorporating Nd-Fe-B melt-spun flakes (MSF). The filaments were formulated by blending TPU, PA12, and one of selected type of Nd-Fe-B particles using a mixing device. The ASP and the MSF were integrated into the matrix via a precise compounding process and 3D printing was used to produce the testing specimens. The preliminary findings indicate that both formulations exhibited promising magnetic properties while maintaining the mechanical characteristics of TPU and PA12. The atomised spherical powder formulation demonstrated worse magnetic behaviour compared to the melt-spun flake formulation. ASP particles enable better fluidity of the composite material during 3D printing. However, the close-packed arrangement of these particles is the cause of much higher porosity and consequently the poorer mechanical and magnetic properties. Optimization of the processing parameters showed significant influence on the final magnetic performance and structural integrity of the printed specimens.
Keywords: bonded magnets, Nd-Fe-B melt spun flakes, Nd-Fe-B atomised powders, material extrusion, additive manufacturing, fused specimen fabrication
Published in DKUM: 08.01.2025; Views: 0; Downloads: 23
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7. A holistic approach to cooling system selection and injection molding process optimization based on non-dominated sortingJanez Gotlih, Miran Brezočnik, Snehashis Pal, Igor Drstvenšek, Timi Karner, Tomaž Brajlih, 2022, original scientific article Abstract: This study applied a holistic approach to the problem of controlling the temperature of critical areas of tools using conformal cooling. The entire injection molding process is evaluated at the tool design stage using four criteria, one from each stage of the process cycle, to produce a tool with effective cooling that enables short cycle times and ensures good product quality. Tool manufacturing time and cost, as well as tool life, are considered in the optimization by introducing a novel tool-efficiency index. The multi-objective optimization is based on numerical simulations. The simulation results show that conformal cooling effectively cools the critical area of the tool and provides the shortest cycle times and the lowest warpage, but this comes with a trade-off in the tool-efficiency index. By using the tool-efficiency index with non-dominated sorting, the number of relevant simulation cases could be reduced to six, which greatly simplifies the decision regarding the choice of cooling system and process parameters. Based on the study, a tool with conformal cooling channels was made, and a coolant inlet temperature of 20 °C and a flow rate of 5 L/min for conformal and 7.5–9.5 L/min for conventional cooling channels were selected for production. The simulation results were validated by experimental measurements.
Keywords: conformal cooling, injection molding, tooling, additive manufacturing, numerical simulation, non-dominated sorting
Published in DKUM: 05.12.2024; Views: 0; Downloads: 10
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8. A study on the compressive behavior of additively manufactured AlSi10Mg lattice structuresDavid Liović, Sanjin Krščanski, Marina Franulović, Dražan Kozak, Goran Turkalj, Emanuele Vaglio, Marco Sortino, Giovanni Totis, Federico Scalzo, Nenad Gubeljak, 2024, original scientific article Abstract: The mechanical behavior of the metallic components fabricated by additive manufacturing (AM) technologies can be influenced by adjustments in their microstructure or by using specially engineered geometries. Manipulating the topological features of the component, such as incorporating unit cells, enables the production of lighter metamaterials, such as lattice structures. This study investigates the mechanical behavior of lattice structures created from AlSi10Mg, which were produced using the laser beam powder bed fusion (LB-PBF) process. Specifically, their behavior under pure compressive loading has been numerically and experimentally investigated using ten different configurations. Experimental methods and finite element analysis (FEA) were used to investigate the behavior of body-centered cubic (BCC) lattice structures, specifically examining the effects of tapering the struts by varying their diameters at the endpoints (dend) and midpoints (dmid), as well as altering the height of the joint nodes (h). The unit cells were designed with varying parameters in such a way that dend is changed at three levels, while dmid and h are changed at two levels. Significant differences in Young’s modulus, yield strength, and ultimate compressive strength between the various specimen configurations were observed both experimentally and numerically. The FEA underestimated the Young’s modulus corresponding to the configurations with thinner struts in comparison to the higher values found experimentally. Conversely, the FEA overestimated the Young’s modulus of those configurations with larger strut diameters with respect to the experimentally determined values. Additionally, the proposed FE method consistently underestimated the yield strength relative to the experimental values, with notable discrepancies in specific configurations.
Keywords: lattice structure, BCC, compressive behavior, additive manufacturing, AlSi10Mg
Published in DKUM: 25.11.2024; Views: 0; Downloads: 26
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9. The influence of the ratio of circumference to cross-sectional area of tensile bars on the fatigue life of additive manufactured AISI 316L steelLuka Ferlič, Filip Jerenec, Mario Šercer, Igor Drstvenšek, Nenad Gubeljak, 2024, original scientific article Abstract: The static and dynamic loading capacities of components depend on the stress level to which the material is exposed. The fatigue behavior of materials manufactured using additive technology is accompanied by a pronounced scatter between the number of cycles at the same stress level, which is significantly greater than the scatter from a material with the same chemical composition, e.g., AISI 316L, but produced by rolling or forging. An important reason lies in the fact that fatigue cracks are initiated almost always below the material surface of the loaded specimen. Thus, in the article, assuming that a crack will always initiate below the surface, we analyzed the fatigue behavior of specimens with the same bearing cross section but with a different number of bearing rods. With a larger number of rods, the circumference around the supporting part of the rods was 1.73 times larger. Thus, experimental fatigue of specimens with different sizes showed that the dynamic loading capacity of components with a smaller number of bars is significantly greater and can be monitored by individual stress levels. Although there are no significant differences in loading capacity under static and low-cycle loading of materials manufactured with additive technologies, in high-cycle fatigue it has been shown that the ratio between the circumference and the loading cross section of tensile-loaded rods plays an important role in the lifetime. This finding is important for setting a strategy for manufacturing components with additive technologies. It shows that a better dynamic loading capacity can be obtained with a larger loading cross section.
Keywords: AISI 316L stainless steel, additive manufacturing, FEM, high-cycle fatigue, fractography analysis
Published in DKUM: 25.11.2024; Views: 0; Downloads: 26
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10. Metallurgical and geometric properties controlling of additively manufactured products using artificial intelligenceSnehashis Pal, Igor Drstvenšek, 2021, original scientific article Abstract: This article has presented a technical concept for producing precisely desired Additive
Manufactured (AM) metallic products using Artificial Intelligence (AI). Due to the stochastic
nature of the metallic AM process, which causes a greater variance in product properties
compared to traditional manufacturing processes, significant inaccuracies in metallurgical
properties, as well as geometry, occur. The physics behind these phenomena are related to
the melting process, bonding, cooling rate, shrinkage, support condition, part orientation.
However, by controlling these phenomena, a wide range of product features can be achieved
using the fabricating parameters. A variety of fabricating parameters are involved in the
metal AM process, but an appropriate combination of these parameters for a given material
is required to obtain an accurate and desired product. Zero defect product can be achieved
by controlling these parameters by implementing Knowledge-Based System (KBS). A suitable
combination of manufacturing parameters can be determined using mathematical tools with
AI, considering the manufacturing time and cost. The knowledge required to integrate AM
manufacturing characteristics and constraints into the design and fabricating process is beyond
the capabilities of any single engineer. Concurrent Engineering enables the integration of design
and manufacturing to enable trades based not only on product performance, but also on other
criteria that are not easily evaluated, such as production capability and support. A decision
support system or KBS that can guide manufacturing issues during the preliminary design
process would be an invaluable tool for system designers. The main objective of this paper is to
clearly describe the metal AM manufacturing process problem and show how to develop a KBS
for manufacturing process determination.
Keywords: metallurgical properties, geometry, additive manufacturing, artificial intelligence, knowledge-based system
Published in DKUM: 25.09.2024; Views: 0; Downloads: 9
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