Various Types Of Defects Research Articles

A perspective on guided electrophoretic transport of particles in liquid crystals

Nonlinear electrophoresis in complex fluids like nematic liquid crystals provides new pathways toward achieving precisely controlled motion and assembly of microscopic objects. The nematic host introduces a paradigm shift in the mechanism of electrophoretic transport by generating unbalanced electro-osmotic flows around the colloidal particle due to symmetry breaking of the medium caused by the induced topological defects. Rationally designed particles, which induce various types of defects and asymmetries, provide new opportunities in this regard. In this Perspective article, we discuss how the asymmetry in the shape and interfacial properties help in piloting the particles using an AC electric field. Finally, we propose some feasible strategies to achieve navigational control using magnetic and photo-responsive particles, guided by orthogonal electric, magnetic fields, and light, respectively.

Polycrystalline silicon photovoltaic cell defects detection based on global context information and multi-scale feature fusion in electroluminescence images

In photovoltaic (PV) cell inspection, electroluminescence (EL) imaging provides high spatial resolution for detecting various types of defects. The recent integration of EL imaging with deep learning models has enhanced the recognition of defects in PV cells. However, the high surface impurity content in polycrystalline silicon PV cells presents a challenge. Defect features can be similar to complex background textures, making highlighting features for small target defects difficult and leading to potential misclassification as background or other classes. To address these challenges, we propose a novel deep convolutional neural network (CNN) model for effectively identifying small target defects in polycrystalline PV cells. We first utilize a global context information (GCI) block to improve CNN’s modeling of global information, aiding in distinguishing PV cell defects with similar local details. Moreover, we design a channel weight feature pyramid (CWFP) that dynamically adjusts the channel weights of extracted features. We further achieve multi-scale feature fusion through the pyramid structure to enhance the resolution capability for small targets. The proposed model was evaluated on a publicly available dataset of 8 defect classes in polycrystalline PV cells, achieving an accuracy of 96.36 %. The experimental results show that the proposed model outperforms existing deep learning models in detecting small target defects with higher precision.

Establishment and evaluation of rat models of parastomal hernia.

Parastomal hernia poses a challenging problem in the field of hernia surgery. The high incidence and recurrence rates of parastomal hernia necessitate surgeons to enhance surgical techniques and repair materials. This study aimed to develop a rat model of parastomal hernia by inducing various types of defects on the abdominal wall with colostomy. This established method has potential for future studies on parastomal hernia. In this study, 32 male rats were included and randomly divided into four groups: the oblique abdominis excision (OE), oblique abdominis dissection (OD), rectus abdominis excision (RE), and rectus abdominis dissection (RD) groups. In each group, colostomy was performed and an abdominal wall defect was induced. The rats were observed for 28days following surgery. The survival rate, body weight, parastomal hernia model scores, abdominal wall adhesion and inflammation, and collagen level in the hernial sac were compared. No significant differences in survival rate and weight were observed among the four groups. The parastomal hernia model scores in the RE and RD groups were significantly higher than those in the OE and OD groups. The ratio of collagen I/III in the RE and RD groups was significantly lower than that in the OE and OD groups. Adhesion and inflammation levels were lower in the RE group than in the RD group. Based on a comprehensive comparison of the findings, RE with colostomy emerged as the optimal approach for establishing parastomal hernia models in rats.

Three-dimensional phase field modeling, orientation prediction and stress field analyses of twin-twin, twin-grain boundary reactions mediated by disclinations in hexagonal close-packed metals

Crystalline defects, such as dislocations, disclinations, twins and grain boundaries, play critical roles in determining the mechanical properties of metals and alloys. In particular, with multiple competitive deformation modes activated, the mechanical behaviors of hexagonal close-packed metals are strongly influenced by the interactions and reactions of various types of defects. Despite extensive studies on the elastic interactions of defects, a theoretical framework capturing crystallographic reactions, especially reaction products and associated local stress concentration, is still unavailable. Here we suggest a disclination-based method to quantify defect reactions. By using a combination of crystallographic calculations and phase field modeling/simulations, twin-twin and twin-grain boundary reactions in hexagonal close-packed metals have been quantitatively analyzed. It has been found that partial disclinations, accompanied with other defects (e.g., {112¯6} and {112¯2} high-index twins), can be generated by defect reactions as typical byproducts. The orientation change and stress fields caused by disclination formation have been systematically calculated, which offers a rigorous mathematical foundation to explore twin-twin, twin-grain boundary reactions. By quantitatively determining defect reactions and local stress fields, our work provides new insights into the deformation mechanism and microstructure-property relationship in metallic materials.

Defect Recognition and Classification Using Ultrasonic Scattered Signals Based on Convolutional Neural Network

Abstract Carbon Fiber Reinforced Polymer (CFRP) is widely used in the aerospace, aircraft, and railway industries because of its remarkable advantages, such as its lightweight and high strength. Detecting and identifying defects in CFRP is crucial for ensuring its secure and efficient use. Ultrasonic non-destructive testing is one of the most practical and effective methods for defect detection and identification. The ultrasonic scattered signals of defects, which contain abundant defect feature information, are advantageous for defect recognition and classification. However, the anisotropy and inhomogeneity of CFRP, which lead to a low signal-to-noise ratio and poor image quality, pose challenges in flaw recognition. To address the issue, this paper proposes using Convolutional Neural Networks (CNN) methods for defect classification, which can adequately extract information from ultrasonic scattered signals directly. The scattering signals from various types of defects in CFRP, including the different sizes of holes and folds, were selected as inputs for CNN models. Different network models combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with 1D-CNN were employed to classify and identify defect signals. To generate sufficient input sample signals, the data sets were further enlarged by incorporating noise into the original signals. The experimental results showed that using the 1D-LSTM-CNN and 1D-GRU-CNN network models can reduce computational resources while ensuring the accuracy of defect recognition.

Hyperspectral imaging system for pre- and post-harvest defect detection in paprika fruit

External defects in paprika fruit (Capsicum annuum, L. var. angulosum) directly impact their storage and marketability, potentially leading to internal decay and altering fruit appearance. However, automatically detecting various paprika defects, including cracks, scars, diseases, and bruises, poses a significant challenge due to variations in defect size and visual similarities among different types of defects. This study proposed a comprehensive, accurate, and rapid defect detection method utilizing hyperspectral imaging (HSI) alongside multivariate analysis and imaging technologies. The reflective surface of fruit poses a primary limitation to efficient defect detection using imaging technologies. Therefore, a polarized HSI visible–near-infrared (VIS-NIR, 397–1000 nm) system was developed, and over 500 paprika fruit with various types of defects were imaged using the developed system. Additionally, fruit were manually bruised in different sizes to ensure spectral data variability and measured every 12 hours for two days. Spectral data (over 4580 spectral data) from the regions of interest (ROIs) of the paprika fruit samples, including manually bruised fruit, were extracted. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was developed to distinguish between normal and defective fruit. Remarkably, the developed model achieved an accuracy of over 99 % in predicting normal and defective fruit. Moreover, the chemical images generated enhance the inspection process. The successive projection algorithm (SPA) and variable importance in projection (VIP) were further utilized for critical band selection, yielding an accuracy of 93 % and 98 % in the prediction set, respectively. Furthermore, a classification algorithm based on analysis of variance (ANOVA) was employed to determine the optimal wavelength band ratios (F-values). This approach yielded an impressive accuracy rate of over 88.8 % and further enhanced the performance of the band ratio by an improved watershed segmentation algorithm (IWSA) to reach an accuracy of 91.3 % in multi-defect detection. These findings offer a practical and efficient solution for the external quality inspection of paprika fruit, particularly in cases with naturally occurring defects. By integrating hyperspectral imaging, PLS-DA, band selection methods, ANOVA-based classification, and IWSA, this comprehensive analysis provides a reliable and practical means for identifying and categorizing defects in paprika fruit, ultimately enhancing product quality and consumer satisfaction.

A robust defect detection method with a generalization enhancer and cross-modality aggregator for cylinder bores

High-quality cylinder bores in automobile engines enable drivers to respond quickly to emergencies. Automated detection methods are gradually being adopted across various industries. However, uncontrollable factors and improper preservation methods lead to various types of defects on cylinder bores, thereby causing existing high-performance detectors to exhibit not only undesirable generalizability for unseen defect types but also a certain degree of missed detection for defects of seen types, thereby allowing defective cylinder bores to flow into the market. To address these issues, we propose a foundation-model-based robust defect detection method with high generalizability for cylinder bores (RHG-Detector). Specifically, to address unseen defect categories, we propose a generalization enhancer comprising a box filter, a region extractor and a defect discriminator (DeDi) based on a foundation model to extend defect detection from a closed set to an open set. To reduce missed detections, we adopt a cross-modality aggregator to aggregate the detection results from different modalities. Additionally, we collected and annotated challenging defect classification and detection datasets for cylinder bores, named HIT-EngDC (Harbin Institute of Technology Engine defect classification dataset) and HIT-EngDD2 (Engine defect detection dataset-version 2), which cover nearly all types of cylinder bore defects. Extensive experiments on HIT-EngDC and HIT-EngDD2 demonstrate the state-of-the-art performance of RHG-Detector, with a classification accuracy of 92.0 and a mAP@50 (mean average precision under intersection over union = 0.5) score of 45.2, where the latter is increased by ∼6 and ∼3 compared to the corresponding FasterRCNN (faster region-based convolutional neural networks) and YOLOv7 (you only look once) scores, respectively.

A probabilistic coupled hardening model of double irradiation defects in iron

Irradiation hardening effect is of significance to the design and life extension of nuclear materials. Although various types of defects coexist under in-pile condition, most irradiation hardening models like the dispersed barrier hardening (DBH) model ignore their strength difference and treat them as a single defect type. In this work, the barrier strengths in presence of combined 〈111〉 and 〈100〉 loops in bcc iron are investigated using discrete dislocation dynamics simulations. Considering the coupling effect, the non-periodic distributed effect is preliminarily analyzed. A probabilistic model accounting for their coupled hardening effect is established based on dislocation depinning mechanism from simulation results, which can be also integrated into the modified DBH model using an effective barrier strength factor. The model presents good agreements with experimental results of iron alloy, and exhibits potential applicability in predicting the coupling effects of other defect types, including the dislocation loops and voids.

Guided Bone Regeneration for the Treatment of Peri-Implantitis: Clinical Case Report

Periodontal disease is characterized as an inflammatory process that damages the periodontal lining and/or support tissues. Peri-implantitis is among the periodontal alterations, being caused by bacterial biofilm causing loss of underlying bone insertion, leading to modifications in its architecture that form intraosseous defects of various types. For the treatment of peri-implantitis there are non-surgical, surgical, resective, regenerative, combined techniques and the decision-making will depend on the degree of the bone defect. Therefore, guided bone regeneration (GBR) is an efficient and simple technique for bone augmentation, which is widely used to restructure bone defects that occur in the alveolar ridge and in the peri-implant region. Biomaterials are alternatives to be used in guided bone regeneration due to their excellent biocompatibility, osteoinductive properties, low degradation rate and their hydrophilicity, which collaborates with the absorption of blood cells and proteins that will help in osseointegration. Female, 58-year-old normosistemic patient, attended a private dental office complaining of pain, bad smell, bleeding and bad taste in the peri-implant region referring to element 25, on its distal face to the mesial face of the element tooth 26. After imaging exams, periodontal probing and the patient's past history, extraction of element 26 was performed, decontamination protocol of the implant surface of 25 through mechanical, chemical and physical means performing antimicrobial photodynamic therapy and finally, a guided bone regeneration was performed, using the allogeneic bone grafting technique associated with a polytetrafluoroethylene membrane in its expanded form (e-PTFE) for recovery of the bone defect and alveolar preservation. Thus, guided bone regeneration has shown high success rates and greater predictability when well indicated and performed, making it important for the maintenance of periodontal and peri-implant health.

Model Analysis Of Worm Gear Pair System Using Finite Element Analysis

Spur gear, helical gear, worm gear, and bevel gear are all important components in industrial applications such as vehicles, pushes, conveyors, elevators, bowl mill, rolling mills, ribbon blender, machine tools, aeroplanes, and windmills. When various types of defects, such as wear, tooth breakage, corrosion, and scratches on bearings, appear in gearboxes, normal machine function may be abruptly terminated. As a result, output and dependability suffer. As a result, several quality tracking and evaluation approaches have been adopted by companies. Finite element analysis (FEA) is one of the approaches. This research paper presents the FEA of a ribbon blender worm gear pair by using Ansys 18.0 to identify the weak gear of the worm gear pair, natural frequency, and deformation. Proe-5 utilized for creation of three-dimensional geometry of threaded worm and toothed worm wheels, as well as other related elements such as shafts and bearings. Steel is used for the worm, shaft, and bearing, whereas bronze is used for the worm wheel. Ansys 18.0 is implemented to carry out worm gear pair model analysis. The results demonstrate that the worm wheel had the most deformation when compared to the worm, and that the natural frequency is greater than the operational frequency of the worm gear pair. The findings of the research study, worm wheel deteriorate early than worm, model analysis plays a significant role in vibration monitoring of worm gear pair, and this work is valuable for further fault analysis of ribbon blender worm gearbox utilising vibration response.

Cladding-free Fermi arc surface states and topological directional couplers in ideal photonic Weyl metamaterials

Photons can freely propagate in a vacuum, making it not a simple insulator but rather a conductor for photons. Consequently, in topological photonics, domain wall structures with opposing effective mass terms are used as cladding to confine electromagnetic waves. This approach is necessary to demonstrate topological edge/surface waves and Fermi arc surface states (FASS). Here, we show that the cladding-free FASS with high field localization at the boundary can be achieved using ideal Weyl gyromagnetic metamaterials (GMs). In these GMs, the ideal Weyl semimetal phase exists due to the dispersionless longitudinal modes. At the boundary of the GMs-vacuum system, the cladding-free FASS connects the projections of Weyl nodes with opposite chirality, thanks to the bulk-boundary correspondence principle. We further confirm that chiral boundary modes can propagate without experiencing scattering or backward reflection, i.e., they can advance seamlessly approximately various types of defects. Remarkably, various types of topological directional couplers are achieved by utilizing cladding-free FASS in an ideal gyromagnetic medium. Our theoretical analysis reveals that the underlying operational principle for accomplishing these nonreflecting directional couplers is due to the single coupling channel between the cladding-free FASS and the multi-type scatterers of the continuous media. Furthermore, the controllable propagation and topological directional coupling of cladding-free FASS can be further explored by adjusting the ideal gyromagnetic medium and boundary configurations of the continuous media system. This research offers increased flexibility for the development of cladding-free and directionally coupled topological devices.

Novel Reference Method for the Characterization of PD Measuring Systems Using HFCT Sensors.

During their lifespan, high-voltage (HV) electrical systems are subjected to operating conditions in which electrical, mechanical, thermal and environmental-related stresses occur. These conditions over time lead to unforeseen failures caused by various types of defects. For this reason, there are several technologies for measuring and monitoring the electrical systems, with the aim of minimizing the number of faults. The early detection of defects, preferably in their incipient state, will enable the necessary corrective actions to be taken in order to avoid unforeseen failures. These failures generally lead to human risks and material damage, lack of power supply and significant economic losses. An efficient maintenance technique for the early detection of defects consists of the supervision of the dielectrics status in the installations by means of on-line partial discharge (PD) measurement. Nowadays, there are numerous systems in the market for the measurement of PD in HV installations. The most efficient with a reasonable cost will be those that offer greater security guarantees and the best positioned in the market. Currently, technology developers and users of PD measuring systems face difficulties related to the lack of reference procedures for their complete characterization and to the technical and economic drawback of performing the characterization tests on site or in laboratory installations. To deal with the previous difficulties, in this paper a novel method for the complete and standardized characterization of PD measuring systems is presented. The applicability of this method is mainly adapted for the characterization of systems operating in on-line applications using high-frequency current transformer (HFCT) sensors. For the appropriate application of the method, an associated and necessary scale modular test platform is used. In the test platform, the real on-site measuring conditions of an HV insulated distribution line are simulated in a controlled way. Practical characterizations, showing the convenience and advantages of applying the method using the modular test platform, are also presented.

Parameter Estimation of Birnbaum-Saunders Distribution under Competing Risks Using the Quantile Variant of the Expectation-Maximization Algorithm

Competing risks models, also known as weakest-link models, are utilized to analyze diverse strength distributions exhibiting multi-modality, often attributed to various types of defects within the material. The weakest-link theory posits that a material’s fracture is dictated by its most severe defect. However, multimodal problems can become intricate due to potential censoring, a common constraint stemming from time and cost limitations during experiments. Additionally, determining the mode of failure can be challenging due to factors like the absence of suitable diagnostic tools, costly autopsy procedures, and other obstacles, collectively referred to as the masking problem. In this paper, we investigate the distribution of strength for multimodal failures with censored data. We consider both full and partial maskings and present an EM-type parameter estimate for the Birnbaum-Saunders distribution under competing risks. We compare the results with those obtained from other distributions, such as lognormal, Weibull, and Wald (inverse-Gaussian) distributions. The effectiveness of the proposed method is demonstrated through two illustrative examples, as well as an analysis of the sensitivity of parameter estimates to variations in starting values.

Research on Fabric Defect Detection Algorithm Based on Improved YOLOv8n Algorithm

In the process of fabric production, various types of defects affect the quality of a fabric. However, due to the wide variety of fabric defects, the complexity of fabric textures, and the concealment of small target defects, current fabric defect detection algorithms suffer from issues such as having a slow detection speed, low detection accuracy, and a low recognition rate of small target defects. Therefore, developing an efficient and accurate fabric defect detection system has become an urgent problem that needs to be addressed in the textile industry. Addressing the aforementioned issues, this paper proposes an improved YOLOv8n-LAW algorithm based on the YOLOv8n algorithm. First, LSKNet attention mechanisms are added to both ends of the C2f module in the backbone network to provide a broader context area, enhancing the algorithm’s feature extraction capability. Next, the PAN-FPN structure of the backbone network is replaced by the AFPN structure, so that the different levels of features of the defects are closer to the semantic information in the progressive fusion. Finally, the CIoU loss is replaced with the WIoU v3 loss, allowing the model to dynamically adjust gradient gains based on the features of fabric defects, effectively focusing on distinguishing between defective and non-defective regions. The experimental results show that the improved YOLOv8n-LAW algorithm achieved an accuracy of 97.4% and a detection speed of 46 frames per second, while effectively increasing the recognition rate of small target defects.

Defect Engineering in Transition Metal Dichalcogenide-Based Gas Sensors

Since the discovery of innovative two-dimensional (2D) materials, significant efforts have been dedicated to exploring their intriguing properties and emerging applications. Among all candidates, transition metal dichalcogenides (TMDs) have proven to be exceptional for gas sensing, while defects engineering has been introduced to modify the pristine TMDs for better gas sensing performances. In this review, we systematically summarize types of defects, advanced characterization techniques, and state-of-the-art controllable synthetic methods. Various types of defects in TMDs can induce diverse changes in chemical and electron structures, which are closely correlated with gas sensing ability. Therefore, connections between defects and gas sensing mechanisms and performances have been addressed based on both defect categories and electron affinity of gases. This review will be a guide for researchers in defective materials and open up the field of precisely synthesis chemistry and deepen the understanding of the underlying effects of defects in other 2D materials.

Non-invasive inspection for a hand-bound book of the 19th century: Numerical simulations and experimental analysis of infrared, terahertz, and ultrasonic methods

Due to fungal growth and mishandling in the book, there are various types of defects as they age such as foxing, tears, and creases. It is important to develop novel non-invasive inspection techniques and defect recognition algorithms. In this work, three non-invasive inspection techniques, including infrared thermography (IRT), terahertz time-domain spectroscopy (THz-TDS), and air-coupled ultrasound (ACU), were employed for the detection of defects in an ancient book cover. To improve the image quality and defect contrast, principal component analysis, fast Fourier transform, and partial least squares regression algorithms are used as the post-processing methods. Furthermore, the YOLOv7 network is deployed for defect automatic detection. Finite element analysis and finite-difference time-domain methods were employed for generating training dataset of YOLOv7 network. Experimental results demonstrate that IRT and THz-TDS has excellent detection capability for surface and subsurface defects, respectively. By employing YOLOv7 network with simulation datasets, defects can be effectively identified.

Determination of the internal structural heterogeneity of natural diamond: Methodological aspects of using confocal Raman spectroscopy with polarization analysis

Aim. To describe a technique for studying the internal structural heterogeneity of natural diamond crystals, based on confocal Raman spectroscopy with polarization analysis, including angular resolution, at high spectral (0.5–0.6 cm–1) and spatial (1 μm) resolution. Results. The parameters of the F2g vibrational mode in diamond (position, width, intensity, shape, including the Gaussian and Lorentzian contributions to the broadening) are determined by the superposition influence of a number of factors, including the type and content of structural stresses, deformations, various types of defects, as well as orientation of crystallographic axes of the crystal relative to the directions of incident and scattered rays and the directions of their electric polarization vectors. The proposed analytical technique includes: (1) analysis of the crystallographic orientation of the sample in the spectrometer coordinate system and possible misorientations of its fragments with an error of ≈8–15°; (2) visualization of the distribution of structural stresses, deformations, twins, impurity defects and their associates based on sample surface mapping by spectral parameters of the F2g vibration mode; (3) obtaining statistical characteristics of the internal structural heterogeneity of the samples based on diagrams of spectral parameter frequency with a statistically significant number (≈103): unimodality (uni-, bimodal distributions) and distribution dispersion (from ≈0.1 to ≈0.6 cm–1 for width and from ≈0.04 to ≈0.6 cm–1 for line position). The procedure was tested using two synthetic CVD diamond single crystals doped with nitrogen and boron. The possibility of typification of natural samples by statistical characteristics of internal heterogeneity is considered using the example of samples from kimberlite pipes of Yakutia and placers of the Western Cis-Urals. Conclusions. A method for determining the internal structural heterogeneity of natural diamond crystals based on confocal Raman spectroscopy with polarization analysis is proposed. The possibility of using statistical characteristics of heterogeneity as a typomorphic feature of the original diamond source is demonstrated. The proposed diagrams are promising for sample comparison and typification.

Defect Detection in Civil Structure Using Deep Learning Method

With the usage time, undesirable defects like crack and spalling appeared in various types of civil structures including beam; wall and column. These defects have a great influence on the usage life of the structures and hence need attention and maintenance. Normally, defect detection in civil structures is the human visual inspection. The manual approach is time-consuming and has a low accuracy. In this paper, we use deep learning method to predict multiple defects in various types of civil structures. Crack and spalling are recognized using the convolutional neural network model in a pixel level. To build the inspection model, the U-net Plus Plus architecture is used for segmentation. In the dataset, 1300 images are taken for training and 600 images for validation. The obtained computer vison model is tested on a dataset of about 491 images. The recall for crack and spalling reach 0.89 and 0.85 respectively. This research work develops an automated approach for defects detection in civil structures. The results can provide helpful guidance for structure health monitoring and maintenance in civil engineering like buildings and bridges.

Large-area inspection of defects in metal plates using multi-mode guided acoustic waves and sparse sensor networks

Various types of defects can be induced during the manufacturing or operation of engineering structures. For effective detection and characterization of the defects in large engineering structures, this paper proposes a large-area inspection technique that combines multi-mode guided acoustic waves with sparse sensor networks. The basic sparse sensor network employed in this study is composed of one transmitter and three receivers, distributed in a square lattice on the test plates. Multi-mode guided waves were excited and acquired by means of commercial single-element sensors of the network. To experimentally demonstrate the proposed technique, four different types of defects were simulated in aluminum test plates, including aluminum tape-based material addition, drilled material loss, indented deformation, and thermal embrittlement. For the evaluation of defects, acoustic response of each defect was analyzed based on the combination of linear vs. nonlinear acoustic characteristics, dependence on the type of the guided acoustic mode, and the directionality of the acoustic response on the network. Results indicate that each of the four representative defects can be uniquely identified (classified) and quantified using the proposed technique.

ОСОБЛИВОСТІ НЕОДНОРІДНОСТІ ПЛАСТИЧНОЇ ДЕФОРМАЦІЇ ГЕТЕРОФАЗНИХ НЕМЕТАЛЕВИХ ВКЛЮЧЕНЬ В СТАЛЯХ

The nature and level of plasticity of microcomposite heterophase inclusions in steels under pressure treatment condition was investigated. Plastic phases in heterophase inclusions of various types under the conditions of hot and cold deformation of steels were investigated. It is shown that each type of inclusions, which are microcomposite formations in steels, is characterized by its own laws of the development of deformation processes, which are determined by their chemical and phase composition, structure, deformation capacity of inclusion phases. The generalized of the plastic behavior of heterophase inclusions of various types with a complex structure have been established and inhibitory effect of non-deformable inclusion phases on the behavior of plastic phases in a wide range of deformation temperatures of steels has been established. The peculiarities of the nature of plasticity of heterophase inclusions with different compositions and structures are discussed. The use of the obtained results will make it possible to develop technologies for obtaining steels with a regulated content and types of heterophase non-metallic inclusions, which will significantly increase their technological plasticity, as well as prevent the formation of various types of defects during pressure treatment of steels.