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A review of the problems of preparing accurate geometric models of steel ropes based on mathematical models without significant simplifications, taking into account the intended purpose of the model, is carried out. Possible approaches to the generation of precise geometric models of steel ropes that have no fundamental limitations on their integration in computational domains and the subsequent construction of finite element models based on them are shown. A generalized parameterized geometric model of single and double twist ropes and its algorithmic implementation using the OpenCASCADE Core Technology geometric modeling kernel in the Gmsh environment (open source software) is considered. The problems of using generic tabular data from steel rope assortment standards as initial data for constructing geometric models are considered. Methods of preliminary verification of collisions of a geometric model based on the initial data of a geometric model are given. Post-verification methods based on Boolean operations over rope wire bodies are given to identify incorrect results of generating models of wire bodies with curvilinear side surfaces based on the algorithm of sequential hierarchical construction of individual wires of single strand and sequential copying of it. Various methods of the process of constructing geometric models of rope wires by extrusion are shown: through a sequence of generatrix with the formation of a body limited by curvilinear surfaces, through a sequence of generatrix with the formation of a body limited by linearly approximated surfaces, and extrusion of one generatrix along a single guideline. The computational complexity of the geometric model generation and the required volume of RAM for the two most universal methods of creating a body of wire are investigated. A method for estimating the value of the step of the arrangement of the generatrix of a single wire is shown, and the influence of its value on the computational complexity of the procedure of wire construction is investigated. Recommendations are given for choosing the value of the radial gap between the layers of wires. An algorithmic implementation of the method for searching for collisions of a geometric model of a steel rope in a non-interactive mode is shown. Approaches to the formation of procedures for processing collisions are proposed. Approaches presented in the article can be implemented in the form of software modules for execution in the Gmsh environment, as well as for another environment using the OpenCascade Core Technology geometric modeling kernel. Such modules allow automation of the construction of accurate geometric models of steel ropes in any configuration without fundamental restrictions on subsequent use, both stand-alone and in the form of objects (primitives) suitable for integration in a third-party model.

The paper presents a hybrid artificial neural network model based on an architecture that incorporates a convolutional image encoder (CNN) and an attention module (Attention-based Multiple Instance Learning, Attention MIL). This module aggregates informative features from a sequence of frames capturing the flame spraying process. Additional technological parameters—air pressure, propane pressure, and standoff distance — are integrated into the model via a tabular channel, enabling it to account for the relationship between visual data and numerical process regime characteristics. The software implementation was developed using the Streamlit platform and the PyTorch library. It features an interactive interface for model training and result visualization, analysis of attention weights across frames, and a prediction mode for output characteristics: surface roughness ($R_a$) and the mass of the deposited coating ($m$). Experimental studies were conducted on data from real-world technological processes, and a comparative analysis of the accuracy of various model configurations was performed. The results demonstrate that the hybrid neural network, which combines visual and tabular features, achieves higher prediction accuracy compared to models using only a single modality. Furthermore, when comparing different implementations of the hybrid network, it was established that using the attention mechanism to process the series of flame spray images provides a significant increase in accuracy over a simple averaging of features without attention. The application includes an attention visualization module that creates a montage of the most significant frames and displays their attention weights, allowing users to identify which frames had the greatest influence on the prediction. The model’s capability for export to the ONNX format for integration into process control systems is also demonstrated. The proposed approach showcases the effectiveness of fusing visual and tabular information for manufacturing process monitoring tasks. The model can serve as a foundation for developing a decision support system or an automated quality control system for coatings produced by flame spraying. The limitations of the implemented model and prospects for its further development are also considered.

This article is about the organization of the cluster management using puppet. It tells about: safety of usage, from the point of view of mass apply at a computing cluster wrong configuration (by reason of human factor); collaboration work and the creation of opportunities for each cluster administrator, regardless of others, writing and debugging your own scripts, before include them in the overall system of cluster managment; writing scripts, which allow to get as fully configured nodes, and updates the configuration of any system parts, without affecting the rest of the nodes components, regardless of the current state of the node of computing cluster.

The article compares different methods of the creation of the hierarchy of puppet scenarios, describes problems associated with the use of “include” for the organization hierarchy, and tells about the transition to a system of sequential call classes through shell-script.