Andrii Malyniak

This research establishes the first comprehensive project management framework for 3DCP through quantitative standards for design preparation, technological setup, and quality management. Based on systematic analysis of construction projects completed during 2023-2025 by "Geopolimer" LTD (Kharkiv, Ukraine), we developed mathematical models for time estimation incorporating perimeter length, geometric complexity, and feature count parameters. Regression analysis of project data enabled formulation of predictive equations: design preparation time accounts for base setup, perimeter-dependent modeling, and complexity coefficients (complex curved surfaces with architectural details). Technological preparation time integrates G-code generation, trajectory verification per meter, and build step validation. A systematic complexity classification system evaluates four geometric factors: curved surfaces percentage, architectural detail count, protruding elements, and internal cavity complexity, enabling quantitative risk assessment and resource allocation decisions. The framework incorporates a three-level quality management system with standardized defect classification (aesthetic, attention-required, critical) defining acceptance criteria for crack dimensions, surface porosity, and structural integrity. Trajectory verification methodology enables proactive defect identification, detecting 85-90% of potential issues before production begins. Economic analysis demonstrates 8-12% rework cost avoidance, 15‒20% preparation time savings, and 5‒10% schedule compression, with return on investment achieved within 0.5‒1.5 months. Case study validation on a 174 m² residential structure demonstrates framework effectiveness: calculated preparation time of 45.1 hours versus actual 47.2 hours. The framework facilitates 3DCP transition from experimental technology to predictable industrial process, enabling evidence-based project planning, systematic risk management, and competitive market positioning. Future research directions include expansion to additional printer types, integration with Building Information Modeling workflows, real-time computer vision quality monitoring, and long-term performance tracking for continuous standard refinement.

This study addresses the critical challenge of predicting residual deformations in industrial products manufactured using selective laser sintering (SLS) technology. Residual deformations represent one of the primary factors leading to geometric inaccuracies in SLS-produced parts, directly affecting their functional performance and dimensional precision. The research proposes and validates a novel hypothesis that existing prediction models developed for plastic injection molding can be effectively adapted for SLS applications through appropriate conversion factors. Given the absence of specialized tools for SLS deformation prediction in the current market, this approach leverages the mature capabilities of the SOLIDWORKS Plastics software as an alternative solution. The methodology involves creating finite element models of test components, specifying material properties similar to SLS powders, and simulating thermal conditions that mimic the SLS process. Through a comparative analysis of twelve distinct geometries, a significant correlation between predicted deformations and actual measured deformations was established. This coefficient enables reliable translation between simulation results and actual SLS outcomes. The findings demonstrate that technological compensating deformations can be effectively calculated and applied to original triangulation models, substantially reducing geometric deviations in final products. The research bridges the gap between established injection molding simulation techniques and the rapidly evolving field of additive manufacturing, providing a practical approach to enhance dimensional accuracy without requiring specialized SLS deformation prediction software. This research was developed at the Department of "Integrated Technologies of Mechanical Engineering" named after M. Semko of NTU "KhPI".

The main aspects of color visualization of triangulated models of industrial products are presented. The implementation of visualization capabilities is based on RGB and HSV color models. The structure and key features of the software implementation of color visualization and the export of the displayed image in PLY, and AMF formats are discussed. Methods for transformations between RGB and HSV color models are described, as well as an algorithm for coloring the triangular faces of the model based on specified color ranges. The developed algorithms allow for a sufficiently informative representation of the desired areas of the product's surfaces by significantly altering one color component while minimally changing the other two. This is achieved by directing the assignment of functional dependencies and value ranges for each component of the color model. Examples of various methods for color shading of vertices and/or edges and/or faces of the model are provided. The visualization subsystem enables the analysis of the geometric characteristics of the polygonal model during the preparation phase of additive manufacturing processes. Significant advantages of these approaches to color visualization are evident when adapting the product design to technological requirements (design preparation) and when solving optimization tasks in technological preparation. The developed software is integrated into the technological preparation system for manufacturing enterprises in the machine engineering sector. This research was developed at the Department of "Integrated Technologies of Mechanical Engineering" named after M. Semko of NTU "KhPI".