Topology Research Articles

Solvation and its influence on the electronic structure and pharmacological activity of 2-fluoro-6-trifluromethyl acetophenone

The molecular framework and vibrational spectra of 2′-Fluoro-6′-Trifluoromethyl Acetophenone (MA3) in diverse solvent environments are being studied using quantum chemistry methods like DFT. The structure was successfully optimized using gas phase and solvent phases such as ethanol, diethyl sulfoxide, chloroform, and diethyl ether. Using FT-Raman and FT-IR techniques, both theoretical and experimental scenarios were examined. The IEFPCM model was used to explore the electronic properties, including HOMO-LUMO gaps, UV–Vis spectra, NBO, Mulliken analysis, and MEP, in various solvents. The TD-DFT approach and IEFPCM model were utilized to replicate the UV spectra. The Fukui function helped locate reactive sites and understand chemical interactions. Comprehensive topological analysis using Multiwfn – 3.8 suite integrated RDG, ALIE, ELF, and LOL to identify key binding sites and weak interactions. The molecular docking studies highlighted the compound’s binding affinities with target proteins, showcasing its potential pharmacological applications. This research underscores the critical role of solvation in determining the electronic and structural properties of MA3, paving the way for its application in drug development and other fields.

PPT1 Promotes Growth and Inhibits Ferroptosis of Oral Squamous Cell Carcinoma Cells.

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers with poor prognosis in the head and neck. Elucidating molecular mechanisms underlying OSCC occurrence and development is important for the therapy. Dysregulated palmitoylation-related enzymes have been reported in several cancers but OSCC. To explore the role of palmitoyl-protein thioesterase 1 (PPT1) in OSCC. Differentially expressed genes (DEGs) and related protein-protein interaction networks between normal oral epithelial and OSCC tissues were screened and constructed via different online databases. Tumor samples from 70 OSCC patients were evaluated for the relationship between PPT1 expression level and patients'clinic characteristics. The role of PPT1 in OSCC proliferation and metastasis was studied by functional experiments including MTT, colony formation, EdU incorporation and transwell assays. Lentivirus-based constructs were used to manipulate gene expression. FerroOrange probe and malondialdehyde assay were used to determine ferroptosis. Growth of OSCC cells in vivo was investigated by a xenograft mouse model. A total of 555 DEGs were obtained, and topological analysis revealed that PPT1 and GPX4 might play critical roles in OSCC. Increased PPT1 expression was found to be correlated with poor prognosis of OSCC patients. PPT1 effectively promoted the proliferation, migration and invasion while inhibited the ferroptosis of OSCC cells. PPT1 affected the expression of glutathione peroxidase 4 (GPX4). PPT1 promoted growth and inhibited ferroptosis of OSCC cells. PPT1 might be a potential target for OSCC therapy.

Leveraging Topological Data Analysis and AI for Advanced Manufacturing: Integrating Machine Learning and Automation for Predictive Maintenance and Process Optimization

Leveraging Topological Data Analysis and AI for Advanced Manufacturing: Integrating Machine Learning and Automation for Predictive Maintenance and Process Optimization

Topological coindices and QSPR analysis for some potential drugs used in lung cancer treatment via CoM and CoNM-polynomials

Topological indices are used to convert a chemical structure into a real number, usually to study the physicochemical and biological properties of molecules. The groundwork is prepared for the interpretation of the obtained data by processing with Quantitative Structure Property/Activity Relationship (QSPR/QSAR). In this study, the drugs lorlatinib, gefitinib, sotorasib, pralsetinib, crizotinib, adagrasib, alectinib, brigatinib, dacomitinib and entrectinib, which are potential to be used in the treatment of lung cancer, are discussed. Topological coindices are calculated with the help of CoM and CoNM polynomials obtained with the graph structures of these drugs. The relationship between topological coindices and physicochemical properties such as evaporation enthalpy, flash point, molar refraction, polarisation, surface tension, molar volume are investigated by QSPR analysis. At this stage, linear, logarithmic and quadratic regression methods have been used. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the used some drugs in the treatment of lung cancer. In addition, using comparative analysis, the actual values and the values calculated with the help of topological indices have been examined in terms of predictive ability. The findings of this search demonstrate topological indices’ potential as tools for cancer drug discovery and design.

Morse theoretic signal compression and reconstruction on chain complexes

At the intersection of Topological Data Analysis (TDA) and machine learning, the field of cellular signal processing has advanced rapidly in recent years. In this context, each signal on the cells of a complex is processed using the combinatorial Laplacian, and the resultant Hodge decomposition. Meanwhile, discrete Morse theory has been widely used to speed up computations by reducing the size of complexes while preserving their global topological properties. In this paper, we provide an approach to signal compression and reconstruction on chain complexes that leverages the tools of algebraic discrete Morse theory. The main goal is to reduce and reconstruct a based chain complex together with a set of signals on its cells via deformation retracts, preserving as much as possible the global topological structure of both the complex and the signals. We first prove that any deformation retract of real degree-wise finite-dimensional based chain complexes is equivalent to a Morse matching. We will then study how the signal changes under particular types of Morse matchings, showing its reconstruction error is trivial on specific components of the Hodge decomposition. Furthermore, we provide an algorithm to compute Morse matchings with minimal reconstruction error.

Research on brain functional network property analysis and recognition methods targeting brain fatigue

At present, researches on brain fatigue recognition are still in the stage of single task and simple brain region network features, while researches on high-order brain functional network features and brain region state mechanisms during fatigue in multi-task scenarios are still insufficient, making it difficult to meet the needs of fatigue recognition under complex conditions. Therefore, this study utilized functional near-infrared spectroscopy (fNIRS) technology to explore the correlation and differences in the low-order and high-order brain functional network attributes of three task induced mental fatigue, and to explore the brain regions that have a major impact on mental fatigue. Self-training algorithms were used to identify the three levels of brain fatigue. The results showed that during the fatigue development, the overall connection strength of the endothelial cell metabolic activity and neural activity frequency bands of the low-order brain functional network first decreased and then increased, while the myogenic activity and heart rate activity frequency bands showed the opposite pattern. Network topology analysis indicated that from no fatigue to mild fatigue, the clustering coefficient of endothelial cell metabolic activity and myogenic activity frequency bands significantly decreased, while the characteristic path length of myogenic activity significantly increased; when experiencing severe fatigue, the small-world attribute of the neural frequency band significantly weakened. However, each frequency band maintained its small-world attribute, reflecting the self-optimization and adaptability of the network during the fatigue process. During mild fatigue, neuronal activity bands’ node degree, cluster coefficient, and efficiency rose in high-order brain networks, while low-order networks showed no significant changes. As fatigue progressed, the myogenic activity bands of high-order network properties dominated, but neural bands gained prominence in mild fatigue, approaching the level of myogenic bands in severe fatigue, indicating that brain fatigue orchestrated a shift from myogenic to neural dominance in frequency bands. In addition, during the process of fatigue, the four network attributes of the high-order network cluster composed of low-order nodes related to the prefrontal cortex region, left anterior motor region, motor assist region, and left frontal lobe eye movement region significantly increased, indicating that these brain regions had a significant impact on brain fatigue status. The accuracy of using both high-order and low-order features to identify fatigue levels reached 88.095%, indicating that the combined network features of both high-order and low-order fNIRS signals could effectively detect multi-level mental fatigue, providing innovative ideas for fatigue warning.

Federated Incremental Learning algorithm based on Topological Data Analysis

Federated learning is a distributed learning approach aimed at preserving user’s data privacy, while incremental learning is an adaptive machine learning method that enables continuous learning of new data. The combination of these two approaches into Federated Incremental Learning (FIL) algorithms brings together their respective advantages. However, the existing federated incremental learning still faces two main challenges: (1) catastrophic forgetting of previous knowledge by local models when adapting to incremental tasks, and (2) limited capability of the server to capture critical features during federated aggregation. To address these challenges, this paper proposes a federated incremental learning algorithm based on Topological Data Analysis (TDA). Firstly, the algorithm extracts topological features from the input information and designs a Topological Stability Loss (TSL) to mitigate catastrophic forgetting of previous knowledge by local models. Secondly, a feature attention mechanism is utilized to select appropriate attention weights for each local model, enhancing the recognition performance of the global model. The experimental results on publicly available datasets, namely CIFAR10, CIFAR100, and ImageNet, demonstrate that the proposed approach in this paper achieves global accuracies of 67.23%, 65.75%, and 62.41% respectively for the federated incremental learning model. These accuracies surpass the existing Icarl incremental algorithm by improvements of 3.21%, 2.87%, and 1.34% respectively. Therefore, the algorithm presented in this paper achieves better performance, indicating its superiority over existing methods.

Topological Data Analysis for Particulate Gels.

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. This work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

From Metals to Semiconductors: Advancing MXY (M = Ti, Sn, Ir, X = Se, Te, Y = Se, Te) Compounds with Strain Engineering—A Computational Perspective

In this computational study, density functional theory (DFT) is employed to analyze the structural, electronic, elastic, and topological properties of ternary compounds MXY (M = Ti, Sn, Ir, X = Se, Te, Y = Se, Te). The effects of spin–orbit interaction and pressure‐induced strain are investigated to understand their influence on the stability, mechanical properties, and electronic behavior, paving the way for potential technological applications. The findings confirm that these compounds are inherently stable in nonmagnetic phases, with spin–orbit interaction critically influencing their energy–volume landscapes. The calculated lattice parameters, ratios of lattice constants, and bulk moduli closely align with existing data, confirming the reliability of our approach. Mechanical assessments reveal distinct behaviors: IrSe2 exhibits the highest stiffness due to pronounced covalent bonding, contrasting with SnTe2's elastic anisotropy and SnSeTe's nearly isotropic properties. Electronically, most compounds show metallic characteristics, except SnSe2, which behaves as a semiconductor with an indirect, pressure‐sensitive energy bandgap. Topological analysis under varying hydrostatic pressures indicates band inversions in TiSe2, IrSe2, and SnSeTe, suggesting topological phase transitions absent in other compounds. This study enriches our understanding of these materials and refines the application of DFT in material design.

The chronic heart failure evolutions: Different fates and routes.

Individual prognostic assessment and disease evolution pathways are undefined in chronic heart failure (HF). The application of unsupervised learning methodologies could help to identify patient phenotypes and the progression in each phenotype as well as to assess adverse event risk. From a bulk of 7948 HF patients included in the MECKI registry, we selected patients with a minimum 2-year follow-up. We implemented a topological data analysis (TDA), based on 43 variables derived from clinical, biochemical, cardiac ultrasound, and exercise evaluations, to identify several patients' clusters. Thereafter, we used the trajectory analysis to describe the evolution of HF states, which is able to identify bifurcation points, characterized by different follow-up paths, as well as specific end-stages conditions of the disease. Finally, we conducted a 5-year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant). Findings were validated on internal (n=527) and external (n=777) populations. We analyzed 4876 patients (age=63 [53-71], male gender n=3973 (81.5%), NYHA class I-II n=3576 (73.3%), III-IV n=1300 (26.7%), LVEF=33 [25.5-39.9], atrial fibrillation n=791 (16.2%), peak VO2% pred=54.8 [43.8-67.2]), with a minimum 2-year follow-up. Nineteen patient clusters were identified by TDA. Trajectory analysis revealed a path characterized by 3 bifurcation and 4 end-stage points. Clusters survival rate varied from 44% to 100% at 2years and from 20% to 100% at 5years, respectively. The event frequency at 5-year follow-up for each study cohort cluster was successfully compared with those in the validation cohorts (R=0.94 and R=0.84, P<0.001, for internal and external cohort, respectively). Finally, we conducted a 5-year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant observed in 22% of cases). Each HF phenotype has a specific disease progression and prognosis. These findings allow to individualize HF patient evolutions and to tailor assessment.

Navigating Memorability Landscapes: Hyperbolic Geometry Reveals Hierarchical Structures in Object Concept Memory.

Why are some object concepts (e.g., birds, cars, vegetables, etc.) more memorable than others? Prior studies have suggested that features (e.g., color, animacy, etc.) and typicality (e.g., robin vs. penguin) of object images influences the likelihood of being remembered. However, a complete understanding of object memorability remains elusive. In this study, we examine whether the geometric relationship between object concepts explains differences in their memorability. Specifically, we hypothesize that image concepts will be geometrically arranged in hierarchical structures and that memorability will be explained by a concept's depth in these hierarchical trees. To test this hypothesis, we construct a Hyperbolic representation space of object concepts (N=1,854) from the THINGS database (Hebart et al., 2019), which consists of naturalistic images of concrete objects, and a space of 49 feature dimensions derived from data-driven models. Using ALBATROSS (Stier, A. J., Giusti, C., & Berman, M. G., In prep), a stochastic topological data analysis technique that detects underlying structures of data, we demonstrate that Hyperbolic geometry efficiently captures the hierarchical organization of object concepts above and beyond a traditional Euclidean geometry and that hierarchical organization is related to memorability. We find that concepts closer to the center of the representational space are more prototypical and also more memorable. Importantly, Hyperbolic distances are more predictive of memorability and prototypicality than Euclidean distances, suggesting that concept memorability and typicality are organized hierarchically. Taken together, our work presents a novel hierarchical representational structure of object concepts that explains memorability and typicality.

Molecular mechanism of Dang-Shen-Yu-Xing decoction against Mycoplasma bovis pneumonia based on network pharmacology, molecular docking, molecular dynamics simulations and experimental verification.

Mycoplasma bovis pneumonia is a highly contagious respiratory infection caused by Mycoplasma bovis. It is particularly prevalent in calves, posing a significant threat to animal health and leading to substantial economic losses. Dang-Shen-Yu-Xing decoction is often used to treat this condition in veterinary clinics. It exhibits robust anti-inflammatory effects and can alleviate pulmonary fibrosis. However, its mechanism of action remains unclear. Therefore, this study aimed to preliminarily explore the molecular mechanism of Dang-Shen-Yu-Xing decoction for treating mycoplasma pneumonia in calves through a combination of network pharmacology, molecular docking, molecular dynamics simulation methods, and experimental validation. The active components and related targets of Dang-Shen-Yu-Xing decoction were extracted from several public databases. Additionally, complex interactions between drugs and targets were explored through network topology, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Subsequently, the binding affinity of drug to disease-related targets was verified through molecular docking and molecular dynamics simulation. Finally, the pharmacodynamics were verified via animal experiments. The primary network topology analysis revealed two core targets and 10 key active components of Dang-Shen-Yu-Xing decoction against Mycoplasma bovis pneumonia. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the mechanism of Dang-Shen-Yu-Xing decoction for treating mycoplasma bovis pneumonia involved multiple signaling pathways, with the main pathways including PI3K-Akt and IL17 signaling pathways. Moreover, molecular docking predicted the binding affinity and conformation of the core targets of Dang-Shen-Yu-Xing decoction, IL6, and IL10, with the associated main active ingredients. The results showed a strong binding of the active ingredients to the hub target. Further, molecular docking dynamics simulation revealed three key active components of IL10 induced by Dang-Shen-Yu-Xing decoction against Mycoplasma bovis pneumonia. Finally, animal experiments confirmed Dang-Shen-Yu-Xing decoction pharmacodynamics, suggesting that it holds potential as an alternative therapy for treating mycoplasma bovis pneumonia.

A DNA barcode reference library for the Tipulidae (Insecta, Diptera) of Germany.

Tipulidae, commonly known as true crane flies, represent one of the most species-rich dipteran families, boasting approximately 4,500 known species globally. Their larvae serve as vital decomposers across diverse ecosystems, prompting their frequent and close observation in biomonitoring programs. However, traditional morphological identification methods are laborious and time-consuming, underscoring the need for a comprehensive DNA barcode reference library to speed up species determination. In this study, we present the outcomes of the German Barcode of Life initiative focused on Tipulidae. Our DNA barcode library comprises 824 high-quality cytochrome c oxidase I (COI) barcodes encompassing 76 crane fly species, counting for ca. 54% of the German tipulid fauna. Our results significantly increased the number of European tipulid species available in the Barcode of Life Data System (BOLD) by 14%. Additionally, the number of barcodes from European tipulid specimens more than doubled, with an increase of 118%, bolstering the DNA resource for future identification inquiries. Employing diverse species delimitation algorithms - including the multi-rate Poisson tree processes model (mPTP), Barcode Index Number assignments (BIN), Assemble Species by Automatic Partitioning (ASAP), and the TaxCI R-script - we successfully match 76-86% of the morphologically identified species. Further validation through neighbor-joining tree topology analysis and comparison with 712 additional European tipulid barcodes yield a remarkable 89% success rate for the species identification of German tipulids based on COI barcodes. This comprehensive DNA barcode dataset not only enhances species identification accuracy but also serves as a pivotal resource for ecological and biomonitoring studies, fostering a deeper understanding of crane fly diversity and distribution across terrestrial landscapes.

Baicalin Ameliorates Depression-like Behaviors via Inhibiting Neuroinflammation and Apoptosis in Mice

Depression is a common neuropsychiatric disease which brings an increasing burden to all countries globally. Baicalin, a flavonoid extracted from the dried roots of Scutellaria, has been reported to exert anti-inflammatory, antioxidant, and neuroprotective effects in the treatment of depression. However, the potential biological mechanisms underlying its antidepressant effect are still unclear. In the present study, we conducted extensive research on the potential mechanisms of baicalin’s antidepressant effect using the methods of network pharmacology, including overlapped terms-based analysis, protein–protein interaction (PPI) network topology analysis, and enrichment analysis. Moreover, these results were further verified through molecular docking, weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and subsequent animal experiments. We identified forty-one genes as the targets of baicalin in the treatment of depression, among which AKT1, IL6, TP53, IL1B, and CASP3 have higher centrality in the more core position. Meanwhile, the roles of peripheral genes derived from direct potential targets were also observed. Our study suggested that biological processes, such as inflammatory reaction, apoptosis, and oxidative stress, may be involved in the therapeutic process of baicalin on depression. These mechanisms were validated at the level of structure, gene, protein, and signaling pathway in the present study. Taken together, these findings propose a new perspective on the potential mechanisms underlying baicalin’s antidepressant effect, and also provide a new basis and clarified perspective for its clinical application.

Geometry of Textual Data Augmentation: Insights from Large Language Models

Data augmentation is crucial for enhancing the performance of text classification models when labelled training data are scarce. For natural language processing (NLP) tasks, large language models (LLMs) are able to generate high-quality augmented data. But a fundamental understanding of the reasons for their effectiveness remains limited. This paper presents a geometric and topological perspective on textual data augmentation using LLMs. We compare the augmentation data generated by GPT-J with those generated through cosine similarity from Word2Vec and GloVe embeddings. Topological data analysis reveals that GPT-J generated data maintains label coherence. Convex hull analysis of such data represented by their two principal components shows that they lie within the spatial boundaries of the original training data. Delaunay triangulation reveals that increasing the number of augmented data points that are connected within these boundaries correlates with improved classification accuracy. These findings provide insights into the superior performance of LLMs in data augmentation. A framework for predicting the usefulness of augmentation data based on geometric properties could be formed based on these techniques.

INAP 2.0: Harnessing metabolic complementarity in microbial network analysis.

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Development of a new rhodamine 6G based probe and its application as an optical sensor of Cu2+ and Fe3+ ions: A comprehensive experimental and Theoretical studies

A new rhodamine appended probe 3-allyl salicylaldehyde rhodamine hydrazone (RGAL) has been synthesized and thoroughly characterized using various spectroscopic techniques, as well as single crystal XRD. The optical properties of RGAL were investigated in 10 mM HEPES buffer in H2O:CH3CN (2:8, v/v, pH=7.2) in the presence of various cations. RGAL showed selectivity and sensitivity towards Cu2+ during absorption process and “turn on” behavior towards Fe3+ during emission study owing to the opening of a spirolactum ring. The detection limits for Cu2+ and Fe3+ ions using RGAL were determined to be 6.15 ppm and 4.75 ppm, respectively. The binding constant of RGAL with Cu2+ and Fe3+ ions was found to be 1.20 × 104 M−1 and 1.71 × 104 M−1, respectively. Hirshfeld surface and fingerprint analysis of RGAL provides the in-depth analysis of pairwise interaction between two atoms. Furthermore, the topological analysis of RGAL is performed using NCI, AIM, ELF and LOL analysis. The analysis provides information about O78-H79…N71 and C40-H41…O77 hydrogen bonding interactions in themonomer of RGAL whereas various inter- and intra- molecular interactions give strength to the dimer pattern of RGAL.

Topological analysis of entropy measure using regression model for silicon carbide network

Topological analysis of entropy measure using regression model for silicon carbide network

Quantitative Characterization of Fluorine-Centered Noncovalent Interactions in Crystalline Benzanilides.

Six isomeric molecules, featuring a minimum of three fluorine atoms on either the benzoyl or aniline side, have been synthesized, crystallized and characterized through single crystal X-ray diffraction (SCXRD). In addition, two other compounds, containing six fluorine atoms, three on each of the benzoyl and aniline side of the benzanilide scaffold have also been characterized through SCXRD. This current study aims to augment the capacity for hydrogen bond formation, specifically involving organic fluorine, by elevating the acidity of the involved hydrogens through the incorporation of highly electronegative fluorine atoms, in the presence of strong N-H⋅⋅⋅O=C H-bonds. Lattice energy calculations and assessment of intermolecular interaction energies elucidate the contributions of electrostatics and dispersion forces in crystal packing. The topological analysis of the electron density is characterized by the presence of bond critical points (BCPs) involving C-H⋅⋅⋅F and F⋅⋅⋅F contacts, thus establishing the bonding nature of these interactions which play a crucial role in the crystal packing in addition to the presence of traditional N-H⋅⋅⋅O=C H-bonds.

Methods for optimizing additive technologies using the example of loaded plastic products

The article describes the development of methods for optimizing additive technologies (FDM printing), taking into account the loads acting on the body in question, in order to save materials and reduce production time, within the framework of robotics, shipbuilding and the development of water, land and air drones. An analysis of existing modification methods to strengthen models was carried out. Various methods of modifying 3D models for printing have been tested. Based on the tests, a universal method for strengthening parts for three-dimensional printing was proposed and developed. The method is based on the principle of creating spherical cavities inside the part in place of the most loaded areas of the part, which then, when creating G-code programs for the printer, will be surrounded by outer plastic walls, creating a seal inside the part. Inventor and Ansys stress analysis and topology analysis are used to identify areas requiring optimization, based on the results of which 3D models of the most loaded areas, or models of areas requiring reinforcement, were created. A software and hardware complex has been created for modifying three-dimensional models for 3D printing and a software has been developed to automate the main parts of the processing technique for finished parts - creating cavities based on a three-dimensional model of loaded zones.