M. Rakhshkhorshid

University of Birjand

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S.H. Hashemi

University of Birjand

H. Rastegari

University of Birjand

Afshin Emamikhah

Shahid Rajaee Teacher Training University

Afshin Kazerooni

Shahid Rajaee Teacher Training University

Mohammad Lakhi

Deakin University

Alireza Maldar

Shanghai Jiao Tong University

Mostafa Nakhaei

Auburn University

Moein Gholami

University of Birjand

Sayyed-Amin Teimouri Sendesi

Islamic Azad University, Tehran

Shahrokh Hosseini-Hashemi

Iran University of Science and Technology

Cooperative Institutions

University of Birjand

Islamic Azad University of Najafabad

Amirkabir University of Technology

Iran University of Science and Technology

Islamic Azad University, Tehran

Shahid Rajaee Teacher Training University

Islamic Azad University of Birjand

University of Oulu

Sharif University of Technology

University of Sheffield

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Experimental–Numerical Study of the Effect of Per Pass Applied Strain on the Electrical and Mechanical Properties of Copper Samples in Twist Extrusion Process

Metals and Materials International (2023)

Hamid Bohluli Mohammad Lakhi M. Rakhshkhorshid

Strain (injury)

Universal testing machine

10.1007/s12540-022-01368-5

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Citations (1)

Neural Network Prediction of Warm Deformation Flow Curves in Ferrite+ Cementite Region

international journal of iron and steel society of iran (2016)

M. Rakhshkhorshid H. Rastegari

Many efforts have been made to model the the hot deformation (dynamic recrystallization) flow curves of different materials. Phenomenological constitutive models, physical-based constitutive models and artificial neural network (ANN) models are the main methods used for this purpose. However, there is no report on the modeling of warm deformation (dynamic spheroidization) flow curves of any kind of steels. In this work, a neural network with feed forward topology and Bayesian regularization training algorithm was used to predict the warm deformation flow curves of a eutectoid steel. The experimental data was provided by sampling the dynamic spheroidization flow curves of the tested steel obtained from warm compression tests conducted over a temperature range of 620-770 °C with different strain rates in the range of 0.01-10 s-1. To develop the neural network model, the overal data was divided into three categries of training, validation and testing. The scatter diagrams together with the root mean square error (RMSE) criterion were used to evaluate the prediction performance of the developed model. The low calculated RMSE value of 4.15 MPa for the overall data showed the robustness of the developed ANN model in predicting the warm deformation flow curves of the tested steel. The results can be further used in the mathematical simulation of warm metal forming processes.

Dynamic Recrystallization

Flow stress

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Citations (2)

A Mechanical and Microstructural Study on Friction Stir Welding of 5083 Aluminum Alloy Based on Optimal Values of the Response Surface Method

Journal of Materials Engineering and Performance (2022)

Afshin Emamikhah Afshin Kazerooni M. Rakhshkhorshid

Friction Stir Welding

Friction Stir Processing

Process variable

Design of experiments

10.1007/s11665-022-06901-6

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Citations (8)

A Robust RBF-ANN Model to Predict the Hot Deformation Flow Curves of API X65 Pipeline Steel

Iranian Journal of Materials Forming (2017)

M. Rakhshkhorshid

Abstract In this research, a radial basis function artificial neural network (RBF-ANN) model was developed to predict the hot deformation flow curves of API X65 pipeline steel. The results of the developed model was compared with the results of a new phenomenological model that has recently been developed based on a power function of Zener-Hollomon parameter and a third order polynomial function of strain power m (m is a constant). Root mean square error (RMSE) criterion was used assess the prediction performance of the investigated models. According to the results obtained, it was shown that the RBF-ANN model has a better performance than that of the investigated phenomenological model. Very low RMSE value of 0.41 MPa was obtained for RBF-ANN model that shows the robustness of it to predict the hot deformation flow curves of tested steel. The results can be further used in mathematical simulation of hot metal forming processes.

Flow stress

Robustness

10.22099/ijmf.2017.3941

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Citations (6)

CAD Model Generation Using 3D Scanning

Advanced materials research (2007)

Khalil Khalili Seyed Yousef Ahmadi‐Brooghani M. Rakhshkhorshid

3D Scanners are used in industrial applications such as reverse engineering and inspection. Customization of existing CAD systems is one of rapid ways to supplying a 3D Scanning software. In this paper, using AutoLisp and Visual Basic programming languages, AutoCAD has been customized. Also facilities of automatic scanning of physical parts, in the domain of free form surfaces, have been provided. Furthermore, possibilities such as, control of scanner automotive system, representation of registered point clouds, generation of polygon and /or NURBS model from primary or modified point clouds, have been prepared. Triangulation and image processing techniques along with a new fuzzy logic algorithm have been used to extract the depth information more accurate. These, accompanying with AutoCAD capabilities have provided acceptable facilities for 3D scanning.

AutoLISP

Polygon (computer graphics)

3D Scanning

Laser Scanning

Reverse engineering

Structured Light

10.4028/www.scientific.net/amr.23.169

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Citations (0)

Experimental study of hot deformation behavior in API X65 steel

Materials Science and Engineering A (2013)

M. Rakhshkhorshid Shahrokh Hosseini-Hashemi

10.1016/j.msea.2013.02.045

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Citations (42)

An Analytical Model for Chemical Etching in One Dimensional Space

Advanced materials research (2012)

Hossein Amirabadi M. Rakhshkhorshid

In this paper an analytical model for chemical etching in one dimensional space has been presented. Regarding to the special specifications of Ferric chloride, etching of an Aluminum work piece exposed to Ferric chloride etchant has been modeled. The proposed model shows that, in the condition of constant reaction parameters, etching rate is a linear function of time. Excellent agreement between the proposed model and the experimental results, published by Çakır (2008), validates the model. By generalization the proposed model, etching rate, or in the other word depth of etch in a specified time, for different materials with different etchants can be predicted.

Isotropic etching

Constant (computer programming)

10.4028/www.scientific.net/amr.445.167

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Citations (0)

MODELING THE HOT DEFORMATION FLOW CURVES OF API X65 PIPELINE STEEL USING THE POWER LAW EQUATION

DOAJ (DOAJ: Directory of Open Access Journals) (2016)

M. Rakhshkhorshid

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INVESTIGATION OF CHEMICAL COMPOSITION INFLUENCE ON MECHANICAL PROPERTIES OF API X65 MICROALLOYED STEEL