Impact of Dynamic Non-Equilibrium Processes on Fracture Mechanisms of High-Strength Titanium Alloy VT23

Maruschak, Pavlo; Konovalenko, І. V.; Чаусов, Н. Г.; Pylypenko, Andrii; Panin, S. V.; Власов, И. В.; Prentkovskis, Olegas

doi:10.3390/met8120983

Cited by 11 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the practical value is the calculation of the dimple parameters in the image: their distinct and total area, equivalent diameter, angle of inclination, etc. A comparison of the results obtained on the basis of the proposed neural network with the results obtained by the initial algorithm [22,27,28] shows their high similarity (Table 2).…”

Section: Resultsmentioning

confidence: 86%

Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks

et al. 2018

Self Cite

View full text Add to dashboard Cite

The research of fractographic images of metals is an important method that allows obtaining valuable information about the physical and mechanical properties of a metallic specimen, determining the causes of its fracture, and developing models for optimizing its properties. One of the main lines of research in this case is studying the characteristics of the dimples of viscous detachment, which are formed on the metal surface in the process of its fracture. This paper proposes a method for detecting dimples of viscous detachment on a fractographic image, which is based on using a convolutional neural network. Compared to classical image processing algorithms, the use of the neural network significantly reduces the number of parameters to be adjusted manually. In addition, when being trained, the neural network can reveal a lot more characteristic features that affect the quality of recognition in a positive way. This makes the method more versatile and accurate. We investigated 17 models of convolutional neural networks with different structures and selected the optimal variant in terms of accuracy and speed. The proposed neural network classifies image pixels into two categories: “dimple” and “edge”. A transition from a probabilistic result at the output of the neural network to an unambiguously clear classification is proposed. The results obtained using the neural network were compared to the results obtained using a previously developed algorithm based on a set of filters. It has been found that the results are very similar (more than 90% similarity), but the neural network reveals the necessary features more accurately than the previous method.

show abstract

Section: Resultsmentioning

confidence: 86%

Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…These features in DED lead to highly dynamic phenomena including dynamic melt pool, particle vaporization, rapid solidification and phase transition. In traditional manufacturing processes, highly dynamic phenomena was found to result in large scattering in mechanical properties [102], and when it comes to DED, the scattering can be more serious. When the feedstock materials become elemental powder mixture, the process will subject to more complex changes and may cause more discrepancies that have not been understood very well.…”

Section: Discussionmentioning

confidence: 99%

A Review on Metallic Alloys Fabrication Using Elemental Powder Blends by Laser Powder Directed Energy Deposition Process

et al. 2020

View full text Add to dashboard Cite

The laser powder directed energy deposition process is a metal additive manufacturing technique, which can fabricate metal parts with high geometric and material flexibility. The unique feature of in-situ powder feeding makes it possible to customize the elemental composition using elemental powder mixture during the fabrication process. Thus, it can be potentially applied to synthesize industrial alloys with low cost, modify alloys with different powder mixtures, and design novel alloys with location-dependent properties using elemental powder blends as feedstocks. This paper provides an overview of using a laser powder directed energy deposition method to fabricate various types of alloys by feeding elemental powder blends. At first, the advantage of laser powder directed energy deposition in manufacturing metal alloys is described in detail. Then, the state-of-the-art research and development in alloys fabricated by laser powder directed energy deposition through a mix of elemental powders in multiple categories is reviewed. Finally, critical technical challenges, mainly in composition control are discussed for future development.

show abstract

“…They were the first to develop a new criterion of crack resistance Kλ based on the analysis of The partitions between these sites with dimples of different shapes are formed by a typical ductile mechanism, with the formation of breakouts and deformed sections of the fracture surface. After the realization of DNP, the number of microdefects increases in the alloy, which contributes to the formation of a greater number of micropores, however, their sizes are smaller than those formed under static deformation [35][36][37][38]. Significant density of pores contributes to an increase in the energy expenditures for fracture.…”

Section: Methods Of Complete Diagramsmentioning

confidence: 99%

Modification of Mechanical Properties of High-Strength Titanium Alloys VT23 and VT23M Due to Impact-Oscillatory Loading

Чаусов

Brezinová

Pylypenko

et al. 2019

Metals

Self Cite

View full text Add to dashboard Cite

A simple technological method is proposed and tested experimentally, which allows for the improvement of mechanical properties in sheet two-phase high-strength titanium alloys VT23 and VT23M on the finished product (rolled metal), due to impact-oscillatory loading. Under impact-oscillatory loading and dynamic non-equilibrium processes (DNP) are realized in titanium alloys, leading to the self-organization of the structure. As a result, the mechanical properties of titanium alloys vary significantly with subsequent loading after the realization of DNP. In this study, the test modes are found, which can be used in the production conditions.

show abstract

Impact of Dynamic Non-Equilibrium Processes on Fracture Mechanisms of High-Strength Titanium Alloy VT23

Cited by 11 publications

References 32 publications

Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks

Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks

A Review on Metallic Alloys Fabrication Using Elemental Powder Blends by Laser Powder Directed Energy Deposition Process

Modification of Mechanical Properties of High-Strength Titanium Alloys VT23 and VT23M Due to Impact-Oscillatory Loading

Contact Info

Product

Resources

About