Basem F. Yousef

Bio: Basem F. Yousef is an academic researcher from United Arab Emirates University. The author has contributed to research in topics: Robotic arm & Robot control. The author has an hindex of 6, co-authored 18 publications receiving 188 citations. Previous affiliations of Basem F. Yousef include University of Western Ontario.

Neural network modeling and analysis of the material removal process during laser machining

Abstract: To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.

Modeling of the mechanical behavior of polyethylene/polypropylene blends using artificial neural networks

Abstract: Polymers possess good thermal and electrical insulation properties, low density, and high resistance to chemicals, thus they have been widely used in industrial applications. Nevertheless, they are mechanically weaker and exhibit lower strength and stiffness than metals. However, their mechanical behavior can be enhanced through different techniques such as blending. Accurate estimation of the mechanical behavior is essential in structural design. Since the process of experimental measurements of a blend’s properties can be costly and time consuming, this paper explores the potential use of artificial neural networks (ANNs) in the field of polymer characterization. It addresses the use of ANNs in modeling the tensile curves and mechanical properties of two commonly utilized polymers (polyethylene PE and polypropylene PP) and their blends. Blends of different proportions have been considered. The experimentally acquired data is used to train and test the neural network’s performance. The key system inputs for the ANN modeler are blend ratio and percent strain, and the system output is the stress. The ANN-predicted outputs were compared and verified against the experimental date. The study indicates that a multilayered ANN can simulate the effect of the blending ratio on the mechanical behavior and properties to a high degree of accuracy. It also demonstrates that ANN approach is an effective tool that can be adopted to reduce cost and time of the experimental work. Moreover, the results show that ANNs demonstrate promising potential in the area of polymer characterization.

Manipulator For Surgical Tools

Abstract: Apparatus 10 includes arcuate links 12, 14 , a block-like hub 16 adapted to hold a surgical tool 50 , and a frame 26 for supporting the links 12, 14 . Each link 12, 14 includes a curved portion extending from a first extremity at an end of the link 12, 14 to an extremity at an opposite end thereof. The hub 16 is slidably mounted to the links 12, 14 . The hub 16 moves along arcuate paths defined by the links 12, 14 , for guiding manipulation of the tool 50 about a pivot point 40 . The frame 26 includes a lateral wall having a first pair of opposed guiding slots 28 and a second pair of opposed guiding slots 29 . The slots 28 slidingly receive the extremities of the link 12 and have a degree of curvature equivalent to the degree of curvature of the link 14 . The slots 29 slidingly receive the extremities of the link 14 and have a degree of curvature equivalent to the degree of curvature of the link 12.

A Macro-Robot Manipulator for Medical Applications

Abstract: An actuated robot arm is designed for use as a macro manipulator that can carry, appropriately orient, precisely position and firmly "lock" in position different types of micro robots and surgical tools necessary for applications in minimally invasive therapy. The sophisticated configuration and joint structure of the arm enable it to perform and interact efficiently with the constrained and limited workspace of surgical environments. The normally locked braking system and the simple quick release joint enhance the safety features of the robot for emergencies and power shutdown. With a simple manipulation protocol, the surgeon can use the robot without undergoing any training. Robot workspace analysis indicates that all singularities are outside the operating work envelope. A performance analysis shows that the robot operates with an average displacement accuracy of 0.58 mm and a roll, pitch and yaw angular accuracies of 0.26deg, 0.26deg and 0.38deg respectively.

Laser beam machining—A review

Abstract: Laser beam machining (LBM) is one of the most widely used thermal energy based non-contact type advance machining process which can be applied for almost whole range of materials. Laser beam is focussed for melting and vaporizing the unwanted material from the parent material. It is suitable for geometrically complex profile cutting and making miniature holes in sheetmetal. Among various type of lasers used for machining in industries, CO2 and Nd:YAG lasers are most established. In recent years, researchers have explored a number of ways to improve the LBM process performance by analysing the different factors that affect the quality characteristics. The experimental and theoretical studies show that process performance can be improved considerably by proper selection of laser parameters, material parameters and operating parameters. This paper reviews the research work carried out so far in the area of LBM of different materials and shapes. It reports about the experimental and theoretical studies of LBM to improve the process performance. Several modelling and optimization techniques for the determination of optimum laser beam cutting condition have been critically examined. The last part of this paper discusses the LBM developments and outlines the trend for future research.

Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel

Abstract: This article focuses on modeling and optimizing process parameters in pulsed laser micromachining. Use of continuous wave or pulsed lasers to perform micromachining of 3-D geometrical features on difficult-to-cut metals is a feasible option due the advantages offered such as tool-free and high precision material removal over conventional machining processes. Despite these advantages, pulsed laser micromachining is complex, highly dependent upon material absorption reflectivity, and ablation characteristics. Selection of process operational parameters is highly critical for successful laser micromachining. A set of designed experiments is carried out in a pulsed Nd:YAG laser system using AISI H13 hardened tool steel as work material. Several T-shaped deep features with straight and tapered walls have been machining as representative mold cavities on the hardened tool steel. The relation between process parameters and quality characteristics has been modeled with artificial neural networks (ANN). Predictions with ANNs have been compared with experimental work. Multiobjective particle swarm optimization (PSO) of process parameters for minimum surface roughness and minimum volume error is carried out. This result shows that proposed models and swarm optimization approach are suitable to identify optimum process settings.

A review of modeling and simulation of laser beam machining

Abstract: Laser beam machining (LBM) is a widely used thermal advance machining process capable of high accuracy machining of almost any material with complex geometries. CO 2 and Nd:YAG lasers are mostly used for industrial purposes. Drilling, cutting, grooving, turning and milling are the applications of LBM with different material removal mechanisms. Modeling and simulation of the LBM process is indispensable for optimization purposes. Modeling can be done by implementing analytical, numerical, experimental and artificial intelligence-based methods. This paper provides a review of the various methods used for modeling and simulation of the laser beam machining process as well as key researches done in this field so far.