Friction Surfaced Alloy 718 Deposits: Effect of Process Parameters on Coating Performance

2022 ◽  
Author(s):  
S. Cyril Joseph Daniel ◽  
R. Damodaram ◽  
G. M. Karthik ◽  
B. Lakshmana Rao
Keyword(s):  
Process Parameters ◽  
Alloy 718 ◽  
Coating Performance

scholarly journals Effect of Direct Energy Deposition Process Parameters on Single-Track Deposits of Alloy 718

Metals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Suhas Sreekanth ◽  
Ehsan Ghassemali ◽  
Kjell Hurtig ◽  
Shrikant Joshi ◽  
Joel Andersson
Keyword(s):  
Process Parameters ◽  
Laser Power ◽  
Scanning Speed ◽  
Deposition Process ◽  
Dendritic Morphology ◽  
Area Fraction ◽  
Grain Structure ◽  
Energy Conditions ◽  
Alloy 718 ◽  
Direct Energy

The effect of three important process parameters, namely laser power, scanning speed and laser stand-off distance on the deposit geometry, microstructure and segregation characteristics in direct energy deposited alloy 718 specimens has been studied. Laser power and laser stand-off distance were found to notably affect the width and depth of the deposit, while the scanning speed influenced the deposit height. An increase in specific energy conditions (between 0.5 J/mm2 and 1.0 J/mm2) increased the total area of deposit yielding varied grain morphologies and precipitation behaviors which were comprehensively analyzed. A deposit comprising three distinct zones, namely the top, middle and bottom regions, categorized based on the distinct microstructural features formed on account of variation in local solidification conditions. Nb-rich eutectics preferentially segregated in the top region of the deposit (5.4–9.6% area fraction, Af) which predominantly consisted of an equiaxed grain structure, as compared to the middle (1.5–5.7% Af) and the bottom regions (2.6–4.5% Af), where columnar dendritic morphology was observed. High scan speed was more effective in reducing the area fraction of Nb-rich phases in the top and middle regions of the deposit. The <100> crystallographic direction was observed to be the preferred growth direction of columnar grains while equiaxed grains had a random orientation.

scholarly journals Effect of Process Parameters on the Crack Formation in Laser Metal Powder Deposition of Alloy 718

2018 ◽  
Vol 49 (10) ◽  
pp. 5042-5050 ◽  
Author(s):  
Andreas Segerstark ◽  
Joel Andersson ◽  
Lars-Erik Svensson ◽  
Olanrewaju Ojo
Keyword(s):  
Metal Powder ◽  
Process Parameters ◽  
Crack Formation ◽  
Alloy 718 ◽  
Powder Deposition

Effect of process parameters on grain structure formation during VAR of INCONEL alloy 718

2004 ◽  
Vol 39 (24) ◽  
pp. 7175-7182 ◽  
Author(s):  
A. Kermanpur ◽  
D. G. Evans ◽  
R. J. Siddall ◽  
P. D. Lee ◽  
M. McLean
Keyword(s):  
Structure Formation ◽  
Process Parameters ◽  
Grain Structure ◽  
Alloy 718 ◽  
Inconel Alloy

Performance Forecast of Solar Spectrum Selective Absorbing Coating Based on RBF Neural Network

2013 ◽  
Vol 791-793 ◽  
pp. 577-580
Author(s):  
Han Wu Liu ◽  
Shao Bo Ping ◽  
Feng Zhang ◽  
Xin Long Zhang
Keyword(s):  
Neural Network ◽  
Magnetron Sputtering ◽  
Process Parameters ◽  
Rbf Neural Network ◽  
Solar Spectrum ◽  
Oxygen Flow ◽  
Coating Performance ◽  
Vacuum Degree ◽  
Pressure Oxygen ◽  
Neural Network Technology

The paper researches on the coating performance prepared by sputtering stainless steel particles to Cu substrate, analyzes the effect of the six magnetron sputtering process parameters on absorption rate, such as Ar pressure, oxygen flow, sputtering vacuum degree, sputtering time, target voltage and target current with the artificial neural network technology, and forecasts the performance of the coating prepared by specific process parameters. The results show that during the process of magnetron sputtering when Ar pressure is within the range of 0.2 ~ 0.5 Pa, the oxygen flow is within the range of 0 ~ 20 sccm, and the target current is within the range of 380~ 430 A, the higher the sputtering vacuum degree and the target voltage, the superior the performance of the coating can be obtained. It is also found that the sputtering time has little effect on the coating performance, in the actual preparation process, on the condition that the performance can be guaranteed, the sputtering time should be as short as possible.

Manufacturing and Analysis of Mechanical Properties of Superalloy 718 in Hydraulic Forging

2007 ◽  
Vol 539-543 ◽  
pp. 3124-3129 ◽  
Author(s):  
Young Seok Song ◽  
M.R. Lee ◽  
Jeong Tae Kim
Keyword(s):  
Mechanical Properties ◽  
Process Parameters ◽  
Flow Behavior ◽  
Melting Process ◽  
Tensile Tests ◽  
Hydraulic Press ◽  
Optimum Process ◽  
Alloy 718 ◽  
Compression Tests ◽  
Evolution Of Microstructure

Alloy 718 ingot with a diameter of 400mm was made by the vacuum melting process ; VIM followed by VAR. Compression tests were conducted on samples collected from columnar zone of the VIM/VAR-processed Alloy 718 ingot in wide temperature and strain rate ranges, i.e. 750~1,200OC and 10~0.001s-1 in order to understand the deformation behavior and evolution of microstructure. Tensile tests at high temperature were also conducted on samples in temperature ranges, i.e. 750~1,100OC. Effects of process parameters on the flow behavior as well as on the microstructure evolution during compression tests at high temperatures are considered. As a result of the deformation simulation, The VIM/VAR ingot was heat-treated for homogenization, and casting structure of the ingot was broken down for uniform microstructures and mechanical properties by controlled cogging process using a hydraulic press. The observation of the microstructure and grain size distribution was carried out to evaluate the effects of optimum process parameters during cogging and mechanical property tests were performed in this study.

scholarly journals Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

2021 ◽  
Author(s):  
Salomé Sanchez ◽  
Divish Rengasamy ◽  
Christopher J. Hyde ◽  
Grazziela P. Figueredo ◽  
Benjamin Rothwell
Keyword(s):  
Creep Rate ◽  
Process Parameters ◽  
Mechanical Performance ◽  
Manufacturing Processes ◽  
Alloy 718 ◽  
Structure Property ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Structure Property Relationships ◽  
Process Structure

AbstractThere is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process–structure–property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used—build orientation, scan strategy and number of lasers—and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at $$650\,^\circ $$ 650 ∘ C and 600 MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of $$1.40\%$$ 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process–structure–property relationships in AM. This increases the readiness of AM for use in critical applications.

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