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Turn Milling of Inconel 718 Produced via Additive Manufacturing Using HVOF and DMLS Methods
(2025) Povolný, Michal; Straka, Michal; Gombár, Miroslav; Hnátík, Jan; Kutlwašer, Jan; Sklenička, Josef; Fulemová, Jaroslava
Additive and coating technologies, such as high-velocity oxy-fuel (HVOF) thermal spraying and direct metal laser sintering (DMLS), often require extensive post-processing to meet dimensional and surface quality requirements, which remains challenging for nickel-based superalloys such as Inconel 718. This study presents the design and topology optimisation of a cutting tool with a linear cutting edge, capable of operating in turn-milling or turning modes, offering a viable alternative to conventional grinding. A non-optimised tool served as a baseline for comparison with a topology-optimised variant improving cutting-force distribution and stiffness-to-mass ratio. Finite element analyses and experimental turn-milling trials were performed on DMLS and HVOF Inconel 718 using carbide and CBN inserts. The optimised tool achieved significantly reduced roughness values: for DMLS, Ra decreased from 0.514 ± 0.069 µm to 0.351 ± 0.047 µm, and for HVOF from 0.606 ± 0.069 µm to 0.407 ± 0.069 µm. Rz was similarly improved, decreasing from 4.234 ± 0.343 µm to 3.340 ± 0.439 µm (DMLS) and from 5.349 ± 0.552 µm to 4.521 ± 0.650 µm (HVOF). The lowest measured Ra, 0.146 ± 0.030 µm, was obtained using CBN inserts at the highest tested cutting speed. All improvements were statistically significant (p < 0.005). No measurable tool wear was observed due to the small engagement and the use of a fresh cutting edge for each pass. The resulting surface quality was comparable to grinding and clearly superior to conventional turning. These findings demonstrate that combining topology optimisation with a linear-edge tool provides a practical and efficient finishing approach for additively manufactured and thermally sprayed Inconel 718 components.
Fast Approximate Symmetry Plane Computation as a Density Peak of Candidates
(SciTePress, 2025) König, Alex; Váša, Libor
Symmetry is a common characteristic exhibited by both natural and man-made objects. This property can be used in various applications in computer vision and computer graphics. There are various types of symmetries, amongst the most prominent belong reflection symmetries and rotation symmetries. In this paper, a method focusing on the fast detection of approximate reflection symmetry of a 3D point cloud with respect to a plane is proposed. The method is based on the creation of a set of candidates that are represented as rigid transformations, and have assigned weights, reflecting the estimated quality of the candidate. The final symmetry plane corresponds to a density peak in the transformation space. The method is demonstrated to be able to find symmetry planes in various objects in 3D, with its main benefit being the speed of the computation.
A hybrid Spiking Neural Network-Transformer architecture for motor imagery and sleep apnea detection
(2025) Pham, Duc Thien; Khoshkhooy Titkanlou, Maryam; Mouček, Roman
Introduction: Motor imagery (MI) classification and sleep apnea (SA) detection are two critical tasks in brain-computer interface (BCI) and biomedical signal analysis. Traditional deep learning models have shown promise in these domains, but often struggle with temporal sparsity and energy efficiency, especially in real-time or embedded applications.Methods: In this study, we propose SpiTranNet, a novel architecture that deeply integrates Spiking Neural Networks (SNNs) with Transformers through Spiking Multi-Head Attention (SMHA), where spiking neurons replace standard activation functions within the attention mechanism. This integration enables biologically plausible temporal processing and energy-efficient computations while maintaining global contextual modeling capabilities. The model is evaluated across three physiological datasets, including one electroencephalography (EEG) dataset for MI classification and two electrocardiography (ECG) datasets for SA detection.Results: Experimental results demonstrate that the hybrid SNN-Transformer model achieves competitive accuracy compared to conventional machine learning and deep learning models.Discussion: This work highlights the potential of neuromorphic-inspired architectures for robust and efficient biomedical signal processing across diverse physiological tasks.
Quantitative Analysis of Flash-Pulse Thermographic Detection of Gunshot Residue
(Japanese Society for Non-Destructive Inspection and co-organized by Kobe University, 2025) Švantner, Michal; Moskovchenko, Alexey; Muzika, Lukáš; Skála, Jiří; Honner, Milan
This study addresses the detection of gunshot residue (GSR) around a bullet hole, which is one of the key forensic procedures for estimating the firing distance. GSR was inspected using flash-pulse thermography (FPT) with Kurtosis statistical processing. The result of such an inspection is a pattern composed of numerous small indications distributed around the hole, attributed to gunshot residue particles. The number and spatial distribution of these indications depend on the firing distance. Analyzing such results based on individual indications is impractical, as the pattern must be evaluated as a whole. Therefore, quantifying the overall result can significantly improve the analysis of the firing distance estimation. This study presents a quantification procedure based on threshold-based mass-marking of indications and evaluation of several statistical characteristics. The correlation between these characteristics and firing distance is then analyzed. A strong but distinctly nonlinear correlation was found between the firing distance and some simple quantitative characteristics, such as the total number of indications. However, the study shows that some derived characteristics, such as the contrast between marked areas and background, exhibit a near-linear correlation. These parameters are, therefore, promising for firing distance analysis based on FPT inspection of GSR on through-shot targets.
Online Simulation of a Diabetic-Patient Metabolism and a Realistic Meal Composition
(IEEE, 2025) Koutný, Tomáš; Úbl, Martin
There are in-silico models to aid both research and treatment of patients with diabetes. In general, these models consider insulin and carbohydrate content of the ingested food only. By considering complete meal composition, i.e., carbohydrates, protein, fat and fiber, we can substantially improve the metabolic simulation to obtain considerably more precise metabolic responses to meal, drugs and physical activity. To do so, we integrated the SmartCGMS framework for glucose level monitoring and control with the state-of-the-art Sirael virtual metabolic machine, which is capable of simulatingmetabolic responses to a realistic meal composition. Specifically, we adapted the capability of building SmartCGMS for low- power devices to produce a WebAssembly instead of targeting alow-power device. At diabetes.zcu.cz, there is a live demonstration of our work, which can execute online, in a web browser. It demonstrates, how a web browser brings the simulation to patients for education, to improve their outcomes by increasing their motivation via learning. In the wider context, he presented work extends research options for both biomedical engineers, physicians and health care professionals, which educate patients.