Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network
نویسندگان
چکیده
An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the properties of maraging steels and composition, processing and working conditions. The input parameters of the model consist of alloy composition, processing parameters (including cold deformation degree, ageing temperature, and ageing time), and working temperature. The outputs of the ANN model include property parameters namely: ultimate tensile strength, yield strength, elongation, reduction in area, hardness, notched tensile strength, Charpy impact energy, fracture toughness, and martensitic transformation start temperature. Good performance of the ANN model is achieved. The model can be used to calculate properties of maraging steels as functions of alloy composition, processing parameters, and working condition. The combined influence of Co and Mo on the properties of maraging steels is simulated using the model. The results are in agreement with experimental data. Explanation of the calculated results from the metallurgical point of view is attempted. The model can be used as a guide for further alloy development. 2003 Elsevier B.V. All rights reserved.
منابع مشابه
Prediction of Mechanical Properties of TWIP Steels using Artificial Neural Network Modeling
In recent years, great attention has been paid to the development of high manganese austenitic TWIP steels exhibiting high tensile strength and exceptional total elongation. Due to low stacking fault energy (SFE), cross slip becomes more difficult in these steels and mechanical twinning is then the favored deformation mode besides dislocation gliding. Chemical composition along with processing ...
متن کاملPredicting Force in Single Point Incremental Forming by Using Artificial Neural Network
In this study, an artificial neural network was used to predict the minimum force required to single point incremental forming (SPIF) of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, the feed rate of the tool, spindle speed, wall angle, thickness of metal sheets and type of material were selected as input and t...
متن کاملPrediction of Engineered Cementitious Composite Material Properties Using Artificial Neural Network
Cement-based composite materials like Engineered Cementitious Composites (ECCs) are applicable in the strengthening of structures because of the high tensile strength and strain. Proper mix proportion, which has the best mechanical properties, is so essential in ECC design material to use in structural components. In this paper, after finding the best mix proportion based on uniaxial tensile st...
متن کاملCurl Size and Pelt Color Determination of Zandi Lambs Using Image Processing and Artificial Neural Network
In this study, a method based on using image processing and artificial neural network is introduced to determine pelt color and curl size of newborn lambs in Zandi sheep. The data was collected from 300 newborn lambs reared in the Zandi sheep breeding centre of Khojir, Tehran. Primarily, curl size and pelt color of new born lambs was recorded by experienced appraisers, and at the same time, sev...
متن کاملThe Prediction of the Tensile Strength of Sandstones from their petrographical properties using regression analysis and artificial neural network
This study investigates the correlations among the tensile strength, mineral composition, and textural features of twenty-ninesandstones from Kouzestan province. The regression analyses as well as artificial neural network (ANN) are also applied to evaluatethe correlations. The results of simple regression analyses show no correlation between mineralogical features and tensile strength.However,...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2002