Search the digital library catalog

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
Full text (2,12 MB)
This document has many files! More...
This document is also a collection of 1 document!

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
Full text (1,04 MB)
This document has many files! More...
This document is also a collection of 1 document!

Abstract: In this paper, the Conditional Average Estimator artificial neural network (CAE ANN) was used to analyze the influence of chemical composition in conjunction with selected process parameters on the yield strength and elongation of an extruded 6082 aluminum alloy (AA6082) profile. Analysis focused on the optimization of mechanical properties as a function of casting temperature, casting speed, addition rate of alloy wire, ram speed, extrusion ratio, and number of extrusion strands on one side, and different contents of chemical elements, i.e., Si, Mn, Mg, and Fe, on the other side. The obtained results revealed very complex non-linear relationships between all of these parameters. Using the proposed approach, it was possible to identify the combinations of chemical composition and process parameters as well as their values for a simultaneous increase of yield strength and elongation of extruded profiles. These results are a contribution of the presented study in comparison with published research results of similar studies in this field. Application of the proposed approach, either in the research and/or in industrial aluminum production, suggests a further increase in the relevant mechanical properties.
Keywords: AA6082, hot extrusion, mechanical properties, yield strength, elongation, artificial neural networks, analysis
Published in DKUM: 12.03.2025; Views: 0; Downloads: 15
Full text (4,40 MB)
This document has many files! More...

Abstract: Artificial intelligence methods, especially artificial neural networks (ANNs), have increasingly been utilized for the mathematical description of physical phenomena in (metallic) material processing. Traditional methods often fall short in explaining the complex, real-world data observed in production. While ANN models, typically functioning as “black boxes”, improve production efficiency, a deeper understanding of the phenomena, akin to that provided by explicit mathematical formulas, could enhance this efficiency further. This article proposes a general framework that leverages ANNs (i.e., Conditional Average Estimator—CAE) to explain predicted results alongside their graphical presentation, marking a significant improvement over previous approaches and those relying on expert assessments. Unlike existing Explainable AI (XAI) methods, the proposed framework mimics the standard scientific methodology, utilizing minimal parameters for the mathematical representation of physical phenomena and their derivatives. Additionally, it analyzes the reliability and accuracy of the predictions using well-known statistical metrics, transitioning from deterministic to probabilistic descriptions for better handling of real-world phenomena. The proposed approach addresses both aleatory and epistemic uncertainties inherent in the data. The concept is demonstrated through the hot extrusion of aluminum alloy 6082, where CAE ANN models and predicts key parameters, and ChatGPT explains the results, enabling researchers and/or engineers to better understand the phenomena and outcomes obtained by ANNs.
Keywords: artificial neural networks, automatic explanation, hot extrusion, aluminum alloy, large language models, ChatGPT
Published in DKUM: 27.02.2025; Views: 0; Downloads: 8
Full text (3,18 MB)
This document has many files! More...

Abstract: This paper assesses the fault-detection capabilities of modern deep-learning models. It highlights that a naive deep-learning approach optimized for accuracy is unsuitable for learning fault-detection models from time-series data. Consequently, out-of-the-box deep-learning strategies may yield impressive accuracy results but are ill-equipped for real-world applications. The paper introduces a methodology for estimating fault-detection delays when no oracle information on fault occurrence time is available. Moreover, the paper presents a straightforward approach to implicitly achieve the objective of minimizing fault-detection delays. This approach involves using pseudo-multi-objective deep optimization with data windowing, which enables the utilization of standard deep-learning methods for fault detection and expanding their applicability. However, it does introduce an additional hyperparameter that needs careful tuning. The paper employs the Tennessee Eastman Process dataset as a case study to demonstrate its findings. The results effectively highlight the limitations of standard loss functions and emphasize the importance of incorporating fault-detection delays in evaluating and reporting performance. In our study, the pseudo-multi-objective optimization could reach a fault-detection accuracy of 95% in just a fifth of the time it takes the best naive approach to do so.
Keywords: artificial neural networks, deep learning, fault detection, accuracy, multi-objective optimization
Published in DKUM: 30.11.2023; Views: 363; Downloads: 36
Full text (478,93 KB)
This document has many files! More...