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Abstract: This paper shows the possibility of applying artificial intelligence methods in milling, as one of the most common machining operations. The main goal of the research is to obtain reliable intelligent models for selected output characteristics of the milling process, depending on the input parameters of the process: depth of cut, cutting speed and feed to the tooth. One of the problems is certainly determining the value of input parameters of the processing process depending on the objective function, i.e. the output characteristics of the milling process. The selected objective functions in this paper are the temperature in the cutting zone and arithmetic mean roughness of the machined surface. The paper examines the accuracy of three models based on artificial intelligence, obtained through artificial neural networks, fuzzy logic, and genetic algorithms. Based on the mean percentage error of deviation, conclusions were drawn as to which of the three models is most adequately applied and implemented in appropriate process systems, which are based on artificial intelligence.
Keywords: artificial intelligence, artificial neural networks (ANN), fuzzy logic, genetic algorithms (GA), face milling, modelling, surface roughness, cutting temperature
Published in DKUM: 15.01.2026; Views: 0; Downloads: 1
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Abstract: The real-time performance of target tracking, detection, and positioning behaves not well for non-Gaussian and nonlinear model with circumstance uncertainty. The weak observability of the system under large noise causes the algorithm unstable and slow to converge. A new estimation algorithm combining square-root cubature Kalman filter (SRCKF) with simultaneous localization and mapping (SLAM) is proposed. By connecting neural network weights, network input, functional types and ideal output network, the algorithm firstly update iteratively the SRCKF-SLAM state model and observation model, then conduct the cubature point estimate (weights) neural network framework. Thus, a point set better representing the target state and a more accurate state estimation are achieved, which can improve the filtering accuracy. This paper also estimates robot and characteristic states by filtering in groups. The simulation results showed that the proposed algorithm is feasible and effective. Compared with other filtering algorithms such as SRUKF and SRCDKF, it improves the estimation accuracy. Applying the new algorithm to the position filtering estimation of mobile robot can effectively reduce the positioning error, achieve high-precision tracking detection, and improve the accuracy of robot target detection.
Keywords: mobile robots, Square-root cubature Kalman filter, simultaneous localization and mapping, SLAM, sensors, artificial neural networks, Iiteration update, filter estimate
Published in DKUM: 12.01.2026; Views: 0; Downloads: 0
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Abstract: Accurate and efficient evaluation of steel properties is crucial for modern manufacturing. This study presents a novel approach that combines spark imaging and deep learning to predict carbon content in steel. By capturing and analyzing sparks generated during grinding, the method offers a fast and cost-effective alternative to conventional testing. Using convolutional neural networks (CNNs), the proposed models demonstrate high reliability and adaptability across different steel types. Among the tested architectures, MobileNet-v2 achieved the best performance, balancing accuracy and computational efficiency. The findings highlight the potential of machine vision and artificial intelligence in non-destructive steel analysis, providing rapid and precise insights for industrial applications.
Keywords: carbon content prediction, convolutional neural networks, deep learning, machine vision, spark imaging, steel analysis
Published in DKUM: 03.11.2025; Views: 0; Downloads: 7
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Abstract: Nowadays, technological progress has promoted the integration of artificial intelligence into modern human lives rapidly. On the other hand, extreme weather events in recent years have started to influence human well-being. As a result, these events have been addressed by artificial intelligence methods more and more frequently. In line with this, the paper focuses on searching for predicting the air temperature in a particular Slovenian micro-location by using a weather prediction model Maximus based on a longshort term memory neural network learned by the long-term, lower-resolution dataset CERRA. During this huge experimental study, the Maximus prediction model was tested with the ICON-D2 general-purpose weather prediction model and validated with real data from the mobile weather station positioned at a specific micro-location. The weather station employs Internet of Things sensors for measuring temperature, humidity, wind speed and direction, and rain, while it is powered by solar cells. The results of comparing the Maximus proposed prediction model for predicting the air temperature in micro-locations with the general-purpose weather prediction model ICON-D2 has encouraged the authors to continue searching for an air temperature prediction model at the micro-location in the future.
Keywords: long short-term memory neural networks, air temperature, micro-location, prediction, weather, Internet of Things
Published in DKUM: 25.09.2025; Views: 0; Downloads: 10
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Abstract: Childhood pneumonia remains a key cause of global morbidity and mortality, highlighting the need for accurate and efficient diagnostic tools. Ensemble methods have proven to be among the most successful approaches for identifying childhood pneumonia from chest X-ray images. However, deploying large, complex convolutional neural network models in resource-constrained environments presents challenges due to their high computational demands. Therefore, we propose a novel ensemble-based knowledge distillation method for identifying childhood pneumonia from X-ray images, which utilizes an ensemble of classification models to distill the knowledge to a more efficient student model. Experiments conducted on a chest X-ray dataset show that the distilled student model achieves comparable (statistically not significantly different) predictive performance to that of the Stochastic Gradient with Warm Restarts ensemble method (F1-score on average 0.95 vs. 0.96, respectively), while significantly reducing inference time and decreasing FLOPs by a factor of 6.5. Based on the obtained results, the proposed method highlights the potential of knowledge distillation to enhance the efficiency of complex methods, making them more suitable for utilization in environments with limited computational resources.
Keywords: knowledge distillation, convolutional neural networks, childhood pneumonia
Published in DKUM: 20.08.2025; Views: 0; Downloads: 7
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