Extrapolative prediction of the hot strength of austenitic steels with a combined constitutive and ANN model

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable.

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The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v7i1.25 ◽

2018 ◽

Vol 7 (1) ◽

pp. 72

Author(s):

Khalid AA Abakar ◽

Chongwen Yu

Keyword(s):

Data Mining ◽

Kernel Functions ◽

Support Vector ◽

Ann Model ◽

Data Mining Techniques ◽

Yarn Quality ◽

Yarn Properties ◽

Svm Model ◽

Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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A Review on Application of ANN Model for the Prediction of Fuel Properties of Biodiesel

JOURNAL OF ADVANCED RESEARCH IN MECHANICAL ENGINEERING AND TECHNOLOGY ◽

10.24321/2454.8650.201906 ◽

2019 ◽

Vol 06 (1&2) ◽

pp. 32-37

Author(s):

Nithyananda BS ◽

Keyword(s):

Fuel Properties ◽

Ann Model

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NIROSTA 4305

Alloy Digest ◽

10.31399/asm.ad.ss0854 ◽

2002 ◽

Vol 51 (5) ◽

Keyword(s):

Corrosion Resistance ◽

Physical Properties ◽

Tungsten Carbide ◽

Tensile Properties ◽

High Speed ◽

High Speed Steel ◽

Heat Treating ◽

Austenitic Steels ◽

High Sulfur Content ◽

Machined Parts

Abstract NIROSTA 4305 is an austenitic alloy with a high sulfur content. The alloy is typically used for machined parts. As with other austenitic steels, it is necessary to machine with good-quality high-speed steel or tungsten carbide tools. This datasheet provides information on composition, physical properties, elasticity, and tensile properties. It also includes information on corrosion resistance as well as forming, heat treating, machining, and joining. Filing Code: SS-854. Producer or source: ThyssenKrupp Nirosta GmbH.

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ALLEGHENY LUDLUM TYPE 205

Alloy Digest ◽

10.31399/asm.ad.ss0640 ◽

1997 ◽

Vol 46 (10) ◽

Keyword(s):

Mechanical Properties ◽

Stainless Steel ◽

Corrosion Resistance ◽

Physical Properties ◽

Tensile Properties ◽

Magnetic Permeability ◽

Cold Working ◽

High Strength ◽

Heat Treating ◽

Austenitic Steels

Abstract Allegheny Stainless Type 205 is a chromium-manganese nitrogen austenitic high strength stainless steel that maintains its low magnetic permeability even after large amounts of cold working. Annealed Type 205 has higher mechanical properties than any of the conventional austenitic steels-and for any given strength level, the ductility of Type 205 is comparable to that of Type 301. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fatigue. It also includes information on corrosion resistance as well as heat treating, machining, and joining. Filing Code: SS-640. Producer or source: Allegheny Ludlum Corporation. Originally published March 1996, revised October 1997.

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