Effects of Pouring Temperature on Microstructure and Mechanical Properties of the A356 Aluminum Alloy Diecastings

Li, Ming; Li, Yuandong; Zhou, Hongwei

doi:10.1590/1980-5373-mr-2019-0676

Cited by 13 publications

(8 citation statements)

References 20 publications

(21 reference statements)

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“…Although Cr favors the formation of primary slurry particles and pro-eutectic α-Al x (Fe,Mn,Cr) y Si z intermetallic compounds, any increase in the amount of Fe-rich particles is coupled with an increase in the size of Al x (Fe,Mn,Cr) y Si z intermetallic compounds. As the Cr level increases, the modification of the sludge morphology from a more or less well-formed hexagon tends to change to a more branched or star shape [54]. In both cases and depending on the alloy composition, the movement of the remaining liquid metal within the interdendritic space may be restricted, reducing the movement of the solid particles towards each other.…”

Section: Discussionmentioning

confidence: 99%

“…intermetallic compounds. As the Cr level increases, the modification of the sludge morphology from a more or less well-formed hexagon tends to change to a more branched or star shape [54]. In both cases and depending on the alloy composition, the movement of the remaining liquid metal within the interdendritic space may be restricted, reducing the movement of the solid particles towards each other.…”

Section: Discussionmentioning

confidence: 99%

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Solid Fraction Determination at the Rigidity Point by Advanced Thermal Analysis

et al. 2021

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The aim of this work is to determine the Solid Fraction (SF) at the rigidity point (FRP) by applying advanced thermal analysis techniques. The variation of the FRP value is important to explain the solidification behavior and the presence or absence of defects in aluminum alloys. As the final alloy composition plays a key role on obtained properties, the influence of major and minor alloying elements on FRP has been studied. A Taguchi design of experiments and a previously developed calculating method, based on the application of high rank derivatives has been employed to determinate first the rigidity point temperature (RPT) and after the corresponding FRP for AlSi10Mg alloys. A correlation factor of r2 of 0.81 was obtained for FRP calculation formula in function of the alloy composition.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Solid Fraction Determination at the Rigidity Point by Advanced Thermal Analysis

et al. 2021

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“…Moisture content, sand grain fineness number, green compression strength, permeability, mold hardness, pouring time, pouring temperature and mold pressure significantly affect the casting defects (Upadhye, 2012). It was also statistically established that pouring temperature significantly affects mechanical properties of casting (Ding et al, 2014;Jahangiri et al, 2017;Klemens et al, 2019;Li et al, 2020). Weight of CO2, mold hardness, sand particle size, content (Noorul Haq et al, 2009).…”

Section: Literature Review Methodsmentioning

confidence: 98%

Analytical modeling of quality parameters in casting process – learning-based approach

Suthar

Persis

Gupta

2022

IJQRM

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PurposeFoundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.Design/methodology/approachThe literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.FindingsThe authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.Originality/valueThis study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

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“…Porosity is a constant issue in the foundry industry [ 1 , 2 ]. Although some progress has been made in estimating the durability of porous structures [ 3 , 4 , 5 ], new approaches are still being sought to improve fatigue life prediction further.…”

Section: Introductionmentioning

confidence: 99%

Estimate of Coffin–Manson Curve Shift for the Porous Alloy AlSi9Cu3 Based on Numerical Simulations of a Porous Material Carried Out by Using the Taguchi Array

Tomažinčič

Klemenc

2022

Materials

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In real engineering applications, machine parts are rarely completely homogeneous; in most cases, there are at least some minor notch effects or even more extensive inhomogeneities, which cause critical local stress concentrations from which fatigue fractures develop. In the present research, a shift of the Coffin–Manson εa–N material curve in a structure with random porosity subjected to dynamic LCF loads was studied. This allows the rest of the fatigue life prediction process to remain the same as if it were a homogeneous material. Apart from the cyclic σ–ε curve, which is relatively easy to obtain experimentally, the εa–N curve is the second most important curve to describe the correlation between the fatigue life N and the strain level εa. Therefore, the correct shift of the εa–N curve of the homogeneous material to a position corresponding to the porous state of the material is crucial. We have found that the curve shift can be efficiently performed on the basis of numerical simulations of a combination of five porosity-specific geometric influences and the associated regression analysis. To model the modified synthetic εa–N curve, five geometric influences of porosity by X-ray or μ-CT analysis are quantified, and then the porosity-adjusted coefficients of the Coffin–Manson equation are calculated. The proposed approach has been successfully applied to standard specimens with different porosity topography.

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Effects of Pouring Temperature on Microstructure and Mechanical Properties of the A356 Aluminum Alloy Diecastings

Cited by 13 publications

References 20 publications

Solid Fraction Determination at the Rigidity Point by Advanced Thermal Analysis

Solid Fraction Determination at the Rigidity Point by Advanced Thermal Analysis

Analytical modeling of quality parameters in casting process – learning-based approach

Estimate of Coffin–Manson Curve Shift for the Porous Alloy AlSi9Cu3 Based on Numerical Simulations of a Porous Material Carried Out by Using the Taguchi Array

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