Study of the Structure and Mechanical Characteristics of Samples Obtained by Selective Laser Melting Technology From Vt6 Alloy Metal Powder

Агаповичев, А. В.; Сотов, А В; Кокарева, В. В.; Смелов, В. Г.; Kyarimov, R R

doi:10.1615/nanoscitechnolintj.v8.i4.30

Cited by 13 publications

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“…In the context of crisis management, management decisions in the field of pricing based on methods that are sensitive to changes in economic factors come to the fore in the construction industry (Geraskin & Kuznetsova, 2020;Geras'kin & Chkhartishvili, 2017;Komarevtseva, 2017;Ramzaev et al, 2016Ramzaev et al, , 2017Ramzaev et al, , 2020. Of the modern forecasting methods with maximum sensitivity, the neural network modeling method can be distinguished as the most preferable for this parameter (Borovikov, 2001).This method is often used in various fields, for example, such as predicting optimal technological parameters in injection molding, stamping tooling (Demyanenko & Popov, 2012;Grechnikov et al, 2016;Grechnikov et al, 2018) , additive technologies (Agapovichev et al, 2017;Smelov et al, 2016), in modeling the structure of a material (Grechnikov & Khaimovich, 2015). Often these calculations are performed using BIG DATA technology (Bogdanov et al, 2018).…”

Section: Research Questionsmentioning

confidence: 99%

Forecasting Of The Cost Of Building Materials In A Volatile Market

Хаймович¹

2022

Proceedings of the III International Conference on Economic and Social Trends for Sustainability of Modern Society – (ICEST-III

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The article proposes a methodology for forecasting market price indicators of one of the main structural elements of the cost of construction material resources. Predictive models are developed taking into account changes in social and economic factors based on neural network methods. The authors provide recommendations on the use of a predictive model in managing the cost of construction products in the system of construction cost engineering. Conclusions and recommendations on the use of neural network models for predicting the cost of material resources in the system of cost engineering in construction: the developed methodology allows planning prices for building materials, taking into account changes in social and economic factors in the regional building materials market; the use of neural network predictive models of the cost of building materials is effective at various stages of the investment and construction process -when planning capital investments, when forming the estimated cost, when determining the price of contracts.

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Section: Research Questionsmentioning

confidence: 99%

Forecasting Of The Cost Of Building Materials In A Volatile Market

Хаймович¹

2022

Proceedings of the III International Conference on Economic and Social Trends for Sustainability of Modern Society – (ICEST-III

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“…For each AM machines vector VAM will consist of original attributes: A -accuracy/µm, R -surface roughness/µm, S -tensile strength/MPa, C -cost of material/ Rub, T -build time/hour. Vgoal = min Tbuild [8]. The model investigate the relation between alternatives and target goal.…”

Section: Implementation Of Multi-criteria Planning Modelmentioning

confidence: 99%

Multi-criteria planning model of engines parts additive manufacturing

et al. 2018

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“…Compared with commercial pure titanium, Ti-6Al-4V alloy is more suitable for large-scale industrial applications, such as titanium hybrid bonding [21]. According to test data [22], the tensile strength of the Ti-6Al-4V alloy produced using selective laser melting (SLM) technology is slightly inferior to the tensile strength of the alloy obtained by rolling and stamping [23], which indicates the high performance properties of the material. One of the main goals of the aerospace industry is to create lightweight designs.…”

Section: Introductionmentioning

confidence: 99%

Short Fiber-Reinforced Polymer Polyamide 6 Lugs and Selective Laser-Melted Ti-6Al-4V Bushing Contact Cohesive Zone Model Mode II Parameters’ Evaluation

Sedelnikov,

Kurkin,

Smelov

et al. 2024

Computation

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This paper discusses an approach to estimating the parameters of the cohesive zone model (CZM) by mode II by extruding the bushing along the lug axis. This method of evaluation requires small samples, which is particularly relevant when investigating short fiber-reinforced polymers (SFRPs) with additively manufactured embedded elements. Adhesion is investigated on the example of 30% carbon fiber-reinforced polyamide-6 molded to Ti-6Al-4V (VT6) selective laser-melted (SLM) alloy bushing in cases of a roughness Ra = 2.66 μm (vibratory finishing), Ra = 8.79 μm (sandblasting), and Ra = 10.02 (directly from SLM). The values of the maximum equivalent tangential contact stress were in a range from 1.1 MPa to 9.5 MPa, while the critical fracture energy for tangential slip was estimated at 15 N/mm for all cases. Experimental validation of the obtained CZM mode II was carried out by evaluating the load-carrying capacity of the lugs with different bushings. In both the experiment and the calculation, greater bushing roughness provides greater lug load-bearing capacity. The ribbed bushings added significant strength in the experiments, which confirmed the importance of considering the tangential mode in the contact model. The presented models can be used for the preliminary evaluation of short fiber-reinforced polyamide-6 parts with titanium-embedded elements bearing capacity.

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Study of the Structure and Mechanical Characteristics of Samples Obtained by Selective Laser Melting Technology From Vt6 Alloy Metal Powder

Cited by 13 publications

References 0 publications

Forecasting Of The Cost Of Building Materials In A Volatile Market

Forecasting Of The Cost Of Building Materials In A Volatile Market

Multi-criteria planning model of engines parts additive manufacturing

Short Fiber-Reinforced Polymer Polyamide 6 Lugs and Selective Laser-Melted Ti-6Al-4V Bushing Contact Cohesive Zone Model Mode II Parameters’ Evaluation

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