Development of regression models to predict and optimize the composition and the mechanical properties of aluminium bronze alloy

2021 ◽  
pp. 1-18
Author(s):  
C.C. Nwaeju ◽  
F.O. Edoziuno ◽  
A.A. Adediran ◽  
E.E. Nnuka ◽  
E.T. Akinlabi ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Regression Models ◽  
Bronze Alloy ◽  
Aluminium Bronze

scholarly journals Mechanical Properties and Microstructures of Locally Produced Aluminium-Bronze Alloy

2012 ◽  
Vol 11 (10) ◽  
pp. 1020-1026 ◽  
Author(s):  
Uyime Donatus ◽  
Joseph Ajibade Omotoyinbo ◽  
Itopa Monday Momoh
Keyword(s):  
Mechanical Properties ◽  
Bronze Alloy ◽  
Aluminium Bronze

ANACONDA ALLOY (C) 521

Alloy Digest ◽  
1979 ◽  
Vol 28 (10) ◽  
Keyword(s):  
Mechanical Properties ◽  
Corrosion Resistance ◽  
Physical Properties ◽  
Tensile Properties ◽  
Heat Treating ◽  
Heavy Duty ◽  
Bronze Alloy ◽  
Good Corrosion Resistance ◽  
Bridge Bearing

Abstract ANACONDA Alloy (C) 521 is the phosphor bronze used where the highest demand is made for resilience, strength and resistance to fatigue. It has generally higher mechanical properties than Anaconda Alloy (A) 510 which is the most widely used phosphor bronze. Alloy (C) 521 has excellent to good corrosion resistance in most environments. Typical applications include heavy-duty springs, bridge bearing plates and heavy-duty cold-headed parts. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties. It also includes information on corrosion resistance as well as forming, heat treating, machining, and joining. Filing Code: Cu-381. Producer or source: Anaconda American Brass Company.

scholarly journals Properties of nickel-based superalloys of equiaxial crystallization

2021 ◽  
pp. 19-23
Author(s):  
O. Glotka ◽  
V. Olshanetskii
Keyword(s):  
Mechanical Properties ◽  
Chemical Composition ◽  
Regression Models ◽  
Computational Prediction ◽  
Integrated Approach ◽  
Experimental Data Processing ◽  
Predictive Regression ◽  
Heat Resistant ◽  
First Time ◽  
Thermodynamic Processes

Purpose. The aim of the work is to obtain predictive regression models, with the help of which, it is possible to adequately calculate the mechanical properties of nickel-based superalloys of equiaxial crystallization, without carrying out preliminary experiments. Research methods. To find regularities and calculate  the latest CALPHAD method was chosen, and modeling of thermodynamic processes of phase crystallization was performed. Results. As a result of experimental data processing, the ratio of alloying elements Kg¢ was proposed for the first time, which can be used to assess the mechanical properties, taking into account the complex effect of the main alloy components. The regularities of the influence of the composition on the properties of heat-resistant nickel alloys of equiaxial crystallization are established. The analysis of the received dependences in comparison with practical results is carried out. The relations well correlated with heat resistance, mismatch and strength of alloys are obtained. Scientific novelty. It is shown that for multicomponent nickel systems it is possible with a high probability to predict a mismatch, which significantly affects the strength characteristics of alloys of this class. The regularities of the influence of the chemical composition on the structure and properties of alloys are established. A promising and effective direction in solving the problem of predicting the main characteristics of heat-resistant materials based on nickel is shown Practical value. On the basis of an integrated approach for multicomponent heat-resistant nickel-based alloys, new regression models have been obtained that make it possible to adequately predict the properties of the chemical composition of the alloy, which made it possible to solve the problem of computational prediction of properties from the chemical composition of the alloy. This allows not only to design new nickel-based alloys, but also to optimize the composition of existing brands.

scholarly journals Application of Machine Learning Techniques to Predict Mechanical Properties for Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress and elongation at break for polyamide 2200 (also known as PA12). EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models have prediction accuracy higher than 80% only for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about material properties, these models need to be improved in the future based on additional experimental work.

scholarly journals Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Vol 9 (6) ◽  
pp. 1060
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for the manufacturing industry due to flexibility in its design and functionality, but inconsistency in quality is one of the major limitations preventing utilizing this technology for the production of end-use parts. The prediction of mechanical properties can be one of the possible ways to improve the repeatability of results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress, and elongation at break for polyamide 2200 (also known as PA12). An EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models only have prediction accuracy higher than 80% for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about the material properties, these models need to be improved in the future based on additional experimental work.

scholarly journals Influence of Metallic Die Temperature in the Solidification of Cu-10%Al-2%Fe Alloy

2010 ◽  
Vol 55 (4) ◽  
pp. 1029-1033 ◽  
Author(s):  
I. Cenoz
Keyword(s):  
Cooling Rate ◽  
Coating Thickness ◽  
Nucleation And Growth ◽  
Massive Transformation ◽  
The Other ◽  
Bronze Alloy ◽  
Β Phase ◽  
Aluminium Bronze ◽  
Graphite Coating ◽  
Die Temperature

Influence of Metallic Die Temperature in the Solidification of Cu-10%Al-2%Fe AlloyThe phases obtained in copper aluminium bronze alloy (Cu-Al10-Fe2) cast into a permanent die were investigated. The parameters examined were the pre-heating temperatures of the die and the graphite coating thickness. The phases α and γ2were detected as well as the metastable phases β' and γ'. The intermetallics of the system Fe-Al were obtained in various stoichiometric compositions. The different cooling rates of the casting resulted in two mechanisms of transformation to α grains out of the unstable β phase, one being nucleation and growth producing needle shaped α grains, the other exhibiting a massive transformation to spherical α grains. These two mechanisms determine the changes in the size of the α grains as a result of changes in the cooling rate in its various ranges.

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