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Manufacturing processes in industry applications are often controlled by the evaluation of surface topography. Topography, in its overall performance, includes form, waviness, and roughness. Methods of measurement of surface roughness can be roughly divided into tactile and contactless techniques. The latter ones are much faster but sensitive to external disturbances from the environment. One type of external source error, while the measurement of surface topography occurs, is a high-frequency noise. This noise originates from the vibration of the measuring system. In this study, the methods for reducing high-frequency errors from the results of contactless roughness measurements of turned surfaces were supported by machine learning methods. This research delves into optimizing filtration methods for surface topography measurements through the application of machine learning models, focusing on enhancing the accuracy of surface roughness assessments. By examining turned surfaces under specific machining conditions and employing a variety of digital filters, the study identifies the Gaussian regression filter and spline filter as the most effective methods at a 22.5 µm cut-off. Utilizing neural networks, support vector machines, and decision trees, the research demonstrates the superior performance of SVMs, achieving remarkable accuracy and sensitivity in predicting optimal filtration methods. [ABSTRACT FROM AUTHOR]

This article provides a detailed review of the types of magnesium wheels available in the industry and the current methods of the wheels' production. The past several years have seen a significant development of magnesium-based lightweight alloys employed as a structural material for modern light vehicles. Magnesium alloys are characterized by their low density while maintaining good mechanical properties. The use of these alloys in the industry enables vehicles' weight reduction while increasing their technical parameters. The first part of the article presents the unique properties of magnesium alloys that determine the application of this material for lightweight vehicle wheels. The advantages of using magnesium wheels over aluminum wheels are also presented. Next, a classification of the types of magnesium wheels was made in regard to their construction, applications, and manufacturing methods. At present, magnesium wheels by construction can be classified according to their geometry as single parts or assembled parts. In reference to geometry, wheels can have different shapes: classic, multi-spoke, with holes, or with frames. Depending on the geometry used, magnesium wheels can have different parameters, such as their mounting hole spacing, wheel diameters, or rim width. Considering the applications in various industries, main distinctions can be made between magnesium wheels for automobiles, motorcycles, bicycles, and wheelchairs. Magnesium wheels can also be categorized in regards to the manufacturing methods: casting, machining, forging, and hybrid manufacturing. The second part of the article focuses on the analysis of magnesium alloy wheel-manufacturing technologies used in the industry and developed by research centers. This article discusses these manufacturing technologies in detail and indicates prospective directions for further development. [ABSTRACT FROM AUTHOR]

In modern times, the risk of food insecurity is a concern for policymakers at the global and national levels, as the issue of hunger and malnutrition still exists. Food security is vulnerable to any crises. The main goal of this paper is to create a neural-network-based predictive model to forecast food consumption trends in Kazakhstan, aiming to reduce the risk of food insecurity. The initial phase of this study involved identifying socioeconomic factors that significantly influence food consumption behaviors in Kazakhstan. Principal component analysis was used to identify key variables, which became the basis for modelling artificial neural networks. It was revealed that the poverty rate, GDP per capita, and food price index are pivotal determinants of food consumption in Kazakhstan. Two models were prepared: to predict food consumption on a national scale per capita per month, and to predict the percentage distribution of various food categories. The prediction of the percentage distribution of various food categories in Kazakhstan demonstrates the positive modelling quality indicators and strengthens the assumption that network modelling can be used. Predictions for total food consumption over the next three years indicate declining metrics, raising concerns about the potential food insecurity risk in Kazakhstan. [ABSTRACT FROM AUTHOR]

This study concerns research on using electrical impedance tomography (EIT) to image moisture inside the porous walls of buildings. In order to transform the electrical measurements into the values of the reconstructed 3D images, a neural network containing the LSTM layer was used. The objective of the study was to evaluate the impact of various loss functions on the efficacy of a neural network's learning process. During the training process, three distinct variations of the loss function were employed, namely mean squared error (MSE), Huber, and a hybrid of MSE + Huber, to attain the desired outcome. Given that the primary focus of the study was on the loss function, the particular neural network architecture employed was deemed non-essential. In order to minimize the influence of the neural network architecture on the outcomes of the test, a comparatively uncomplicated neural model was implemented, comprising a solitary LSTM layer and a single fully connected layer. [ABSTRACT FROM AUTHOR]

The purpose of the article is to propose a fuzzy logic solution for decision-making based on data from CRM (Customer Relationship Management) systems to evaluate banking customer attractiveness. The article is based on theory about management IT systems, especially the CRM type. Based on the literature research, nine identified factors were proposed that can influence whether the relationship with the customer will be profitable for the bank. Factors that affect banking customer attractiveness are considered, including the share of the customer's wallet and the customer's tendency to express a positive opinion of the bank. The data allowing for the identification of these factors is collected in the bank's IT systems, among other CRMs. Based on the research, a model prepared in Simulink using a Mamdani-type Fuzzy Inference System was made. It is a decision model that provides a result in the form of a binary value of 0 or 1, where 1 means it is worth investing in a customer, while 0 means it is not. After considering the subjective opinions, competence and experience of specialists as well as confronting them with the results from the developed model, it can be confirmed that the model works as expected. [ABSTRACT FROM AUTHOR]

This paper presents the model for milling AZ91HP magnesium alloy with TiAlN coated carbide end mill. The model was developed on the basis of experimental data from the neural network training data set. The milling process was conducted at constant parameters of tool geometry, workpiece strength properties, technological machine properties, radial and axial depth of cut. The range of changeable machining parameters specified in this study included cutting speed, feed per tooth, and the output variable: the arithmetical mean roughness parameter (Ra). The process was modelled by means of MatLab software and its Neural Network Toolbox. The developed model was implemented in the algorithm designed to determine optimal milling conditions, exploring the space of acceptable parameters in search of those which would meet the specified roughness parameter at maximum efficiency.

This paper reports on the study of vibration acceleration in milling and vibration prediction by means of artificial neural networks. The milling process, carried out on AZ91D magnesium alloy with a PCD milling cutter, was monitored to observe the extent to which the change of selected technological parameters (vc, fz, ap) affects vibration acceleration ax, ay and az. The experimental data have shown a significant impact of technological parameters on maximum and RMS vibration acceleration. The simulation works employed the artificial neural networks modelled with Statistica Neural Network software. Two types of neural networks were employed: MLP (Multi-Layered Perceptron) and RBF (Radial Basis Function).

The following paper will give an account of experimental tests and simulation of the cutting force components Fx, Fy and Fz in down milling of AZ91D magnesium alloy. The milling operation employed two milling cutters with a different helix angle, λs = 20° and λs = 50°, and was conducted with changeable milling machining parameters: cutting speed, feed per tooth, axial depth of cut. The simulation part of the study was conducted in Statistica software environment with the application of Multi-Layered Perceptron neural network architecture, and using a “black box” approach, which guarantees a good fit of input and output data obtained from the experimental tests.

The aim of this study was to investigate the effect of milling and brushing cutting data settings on the surface geometry and energy parameters of two Mg alloy substrates: AZ91D and AZ31. In milling, the cutting speed and the trochoidal step were modified (vc = 400–1200 m/min and str = 5–30%) to investigate how they affect selected 2D (Rz, Rku, Rsk, RSm, Ra) and 3D (Sa, Sz, Sku, Ssk) roughness parameters. The brushing treatment was carried out at constant parameters: n = 5000 rev/min, vf = 300 mm/min, ap = 0.5 mm. The surface roughness of specimens was assessed with the Ra, Rz, and RSm parameters. The effects of the two treatments on the workpiece surface were analyzed comparatively. It was found that the roughness properties of the machined surface may be improved by the application of a carbide milling cutter and ceramic brush. The use of different machining data was also shown to impact the surface free energy and its polar component of Mg alloy specimens. Complementary to the results from the experimental part of the study, the investigated machining processes were modelled by means of statistical artificial neural networks (the radial basis function and multi-layered perceptron). The artificial neural networks (ANNs) were shown to perform well as a tool for the prediction of Mg alloy surface roughness parameters and the maximum height of the profile (Rz) after milling and brushing. [ABSTRACT FROM AUTHOR]