Researcher Image
احمد رحمان جاسم عبد - Ahmed Rahman jasim almusawi
PhD - lecturer
Al-Khwarizmi College of Engineering , Department of Mechatronics Engineering
[email protected]
Summary

Ahmed R. J. received the Ph.D. in Robotics and Haptic Technology from the University of Gaziantep, Turkey, He received the B.S. and M.S. degrees in Mechatronics engineering from the University of Baghdad, Iraq, in 2003 and 2009. He is a lecturer at the Mechatronics Engineering Department. University of Baghdad, Iraq, from 2009 . His research activities are focused on Mechatronics systems, and artificial intelligence algorithms

Publication Date
Mon Oct 17 2016
Journal Name
Proceedings Of The Institution Of Mechanical Engineers, Part B: Journal Of Engineering Manufacture
Development and control of shaped metal deposition process using tungsten inert gas arc heat source in additive layered manufacturing

Tungsten inert gas arc welding–based shaped metal deposition is a novel additive manufacturing technology which can be used for fabricating solid dense parts by melting a cold wire on a substrate in a layer-by-layer manner via continuous DC arc heat. The shaped metal deposition method would be an alternative way to traditional manufacturing methods, especially for complex featured and large-scale solid parts manufacturing, and it is particularly used for aerospace structural components, manufacturing, and repairing of die/molds and middle-sized dense parts. This article presents the designing, constructing, and controlling of an additive manufacturing system using tungsten inert gas plus wire–based shaped metal deposition metho

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Publication Date
Fri Jan 01 2016
Journal Name
Computational Intelligence And Neuroscience
A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl

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Publication Date
Mon Jun 01 2015
Journal Name
Conference: 8th International Conference And Exhibition On Design And Production Of Machines And Dies/molds