Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304

Desu, Raghuram Karthik; Guntuku, Sharath Chandra; Aditya, B.; Gupta, Amit Kumar

doi:10.1016/j.mspro.2014.07.047

Cited by 24 publications

(8 citation statements)

References 14 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this experiment, we have used 304‐grade S.S. as a substrate. The main constituents of S.S. are Cr and Fe ,. Therefore, the other prominent peaks correspond to Cr and Fe.…”

Section: Resultsmentioning

confidence: 99%

Influence of Cu on the Performance of Tuberose Architecture of Strontium Hydroxide Thin Film as a Supercapacitor Electrode

et al. 2018

View full text Add to dashboard Cite

A simple, economical successive ionic layer adsorption and reaction (SILAR) method has been used for synthesizing 1, 2 and 3 at.% copper‐doped strontium hydroxide over a stainless‐steel (SS) substrate. X‐ray diffraction reveals the orthorhombic crystal structure of polycrystalline Cu‐doped Sr(OH)2. FTIR and Raman analyses explore the chemical bond analysis of the active material. The morphology of the Cu‐doped Sr(OH)2 was analyzed by using SEM. This shows novel hierarchical‐branched tuberose with flakes. The energy‐dispersive X‐ray spectroscopy (EDAX) also confirms the doping of Cu into Sr(OH)2. The electrochemical properties have been investigated by using cyclic voltammetry (CV) and galvanostatic charge−discharge measurements. It has been perceived that doping clearly affects the supercapacitive behavior. The specific capacity of the 3 at.% Cu‐doped Sr(OH)2 film electrode exhibits a maximum specific capacity of 817 C g−1 at 0.4 mA cm−1 in 1 M Na2SO4 electrolyte, but has a low cycle stability. The 2 at.% Cu‐doped sample shows high cycle stability by retrieving 71 % of its capacity after 5000 cycles. The relationship between the doping concentration and supercapacitive behavior has also been discussed. The good capacitive behavior and cycle stability of the Cu‐doped Sr(OH)2 gives the new perspective for the flexible electrode material, which can be used in the near future as an energy storage material.

show abstract

“…In this experiment, we have used 304‐grade S.S. as a substrate. The main constituents of S.S. are Cr and Fe ,. Therefore, the other prominent peaks correspond to Cr and Fe.…”

Section: Resultsmentioning

confidence: 99%

Influence of Cu on the Performance of Tuberose Architecture of Strontium Hydroxide Thin Film as a Supercapacitor Electrode

et al. 2018

View full text Add to dashboard Cite

show abstract

“…As mentioned, we employ data from an unknown-form CP model (Eq. (26) and Eq. (27)) and known-form VP model (Eq.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Models of Plastic Flow Based on Representation Theory

Jones

Templeton

Sanders

et al. 2018

CMES

View full text Add to dashboard Cite

We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen input-output map. Hence, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. With these developments, we enable rapid model building in real-time with experiments, and guide data collection and feature discovery. * corresponding author: rjones@sandia.gov arXiv:1809.00267v1 [physics.comp-ph] 1 Sep 2018 1 Frame indifference is a special case of the more general principle of covariance with changes of the metric tensor [53, Sec.3.3].2 In some sense, a complete set of invariants are coordinates on the manifold induced by symmetry constraints and hence are clearly not unique in their ability to coordinatize the manifold.

show abstract

“…The implemented ANN produced more accurate results than conventional mathematical models such as Johnson Cook, modified Zerilli‐Armstrong, and modified Arrhenius. support vector regression based flow stress prediction for austenitic stainless steel 304 with strain, strain rate and, temperature as inputs and the flow stress as output was proposed by Desu et al [30] and provides more accurate results than the conventional mathematical models.…”

Section: State Of the Artmentioning

confidence: 99%

Machine learning for material characterization with an application for predicting mechanical properties

Stoll

Benner

2021

GAMM-Mitteilungen

View full text Add to dashboard Cite

Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data‐driven methods for the discovery of patterns among multiple lengthscales and time‐scales and structure‐property relationships essential. These data‐driven approaches show enormous promise within materials science. The following review covers machine learning (ML) applications for metallic material characterization. Many parameters associated with the processing and the structure of materials affect the properties and the performance of manufactured components. Thus, this study is an attempt to investigate the usefulness of ML methods for material property prediction. Material characteristics such as strength, toughness, hardness, brittleness, or ductility are relevant to categorize a material or component according to their quality. In industry, material tests like tensile tests, compression tests, or creep tests are often time consuming and expensive to perform. Therefore, the application of ML approaches is considered helpful for an easier generation of material property information. This study also gives an application of ML methods on small punch test (SPT) data for the determination of the property ultimate tensile strength for various materials. A strong correlation between SPT data and tensile test data was found which ultimately allows to replace more costly tests by simple and fast tests in combination with ML.

show abstract

Support Vector Regression based Flow Stress Prediction in Austenitic Stainless Steel 304

Cited by 24 publications

References 14 publications

Influence of Cu on the Performance of Tuberose Architecture of Strontium Hydroxide Thin Film as a Supercapacitor Electrode

Influence of Cu on the Performance of Tuberose Architecture of Strontium Hydroxide Thin Film as a Supercapacitor Electrode

Machine Learning Models of Plastic Flow Based on Representation Theory

Machine learning for material characterization with an application for predicting mechanical properties

Contact Info

Product

Resources

About