Abdel Magid Hamouda

Artificial neural network-based kinematics Jacobian solution for serial manipulator passing through singular configurations

Singularities and uncertainties in arm configurations are the main problems in kinematics robot control resulting from applying robot model, a solution based on using Artificial Neural Network (ANN) is proposed here. The main idea of this approach is the use of an ANN to learn the robot system characteristics rather than having to specify an explicit robot system model. Despite the fact that this is very difficult in practice, training data were recorded experimentally from sensors fixed on each joint for a six Degrees of Freedom (DOF) industrial robot. The network was designed to have one hidden layer, where the input were the Cartesian positions along the X, Y and Z coordinates, the orientation according to the RPY representation and the linear velocity of the end-effector while the output were the angular position and velocities for each joint, In a free-of-obstacles workspace, off-line smooth geometric paths in the joint space of the manipulator are obtained. The resulting network was tested for a new set of data that has never been introduced to the network before these data were recorded in the singular configurations, in order to show the generality and efficiency of the proposed approach, and then testing results were verified experimentally.

Nonlocal damage modelling in clay/epoxy nanocomposites using a multiscale approach

Abstract This study proposed a concurrent multiscale method to model damage in clay/epoxy nanocomposites. The method uses a nonlocal damage formulation to regularize the damage model. The multiscale method used, is based on the Arlequin method which couples two overlapping scales using the Lagrange multipliers method. Since the method blends the energies of two scales in a so called “handshake domain”, the spurious wave reflection from the coupling region is minimum. Hence the method is appropriate for the current dynamic problem. To show the suitability and accuracy of the proposed method, a clay/epoxy nanocomposite beam under dynamic loading is simulated using two different approaches: a full fine scale model and a multiscale model were employed. Also, a comparison between the results proves that the proposed nonlocal multiscale method can accurately predict the damage phenomena inside the clay/epoxy nanocomposites with minimal computational costs. The method presented here is also applicable to a range of related physical problems.

A new adaptive learning algorithm for robot manipulator control

Abstract This paper is devoted to the development and implementation of neural network technology to solve the inverse kinematics problems for serial robot manipulators, given the desired Cartesian path of the end effector of the manipulator in a free-of-obstacles workspace. Offline smooth geometric paths in the joint space of the manipulator are obtained. The proposed technique does not require any prior knowledge of the kinematics model of the system being controlled; the main idea of this approach is the use of an artificial neural network to learn the robot system characteristics rather than having to specify an explicit robot system model. Since one of the most important problems in using artificial neural networks is the choice of the appropriate network configuration, two different configurations were compared; they were trained to learn the desired set of joint angles positions from a given set of end effector positions. The generality and efficiency of the proposed algorithm are demonstrated through simulations of a general six-degrees-of-freedom serial robot manipulator.

An adaptive learning algorithm for controlling a two-degree-of-freedom serial ball-and-socket actuator

Abstract This article presents the development and implementation of an artificial neural network (ANN) for controlling a new two-degree-of-freedom (2DOF) serial ball-and-socket actuator. The ANN is a well-known algorithm for simulating the ability of the human brain to learn and predict sets of information. In this approach, ANN will learn the control parameters to obtain the angular displacement, angular velocity, and angular acceleration of the end-effector without any prior knowledge of the actuator. The ball-and-socket actuator has been proposed as an alternative actuator to the conventional one-degree-of-freedom (1DOF) revolute actuator. The actuator was fabricated from a ball-and-socket joint powered by two electrohydraulic cylinders. Experimental control data had been collected manually and provided for ANN to learn in off-line mode. The training process was carried out to build control knowledge. Thus, the adaptive learning algorithm adopts any modification in the actuator mechanism and hydraulic power system through updating the control knowledge. The results of implementing the build control knowledge for on-line operation of the ball-and-socket actuator shows a fully compliant actuator end-effector to the desired dynamic behaviour within the workspace.

First and Last Triggering Event Approaches for Replacement With Minimal Repairs

To make effective use of a running system, this paper proposes that, when replacement times could be scheduled at a planned time T of operation, and at a number N of minimal repairs, the newly proposed approach of whichever occurs last should be employed into optimizations for policy consideration. The replacement policy with the classical approach of whichever occurs first is also used to make comparison with the approach of whichever occurs last. Further, using the overtime technique, a replacement scheduled at time T in this paper is modified to be done at the first failure after T has arrived, to avoid probable interruptions for running jobs. That is, the system is replaced at failure N before time T, or at the first failure after T has arrived, whichever occurs first, and last, respectively. The policies acting on the approaches of first and last with minimal repairs are analyzed in detail, and their expected cost rates are formulated. Optimal solutions for separate and bivariate T and N are obtained and compared analytically.

Modeling the technology transfer process in the petroleum industry: Evidence from Libya

The purpose of this study was to propose a conceptual model for technology transfer (TT) that houses several factors. These factors are believed to influence the processes’ effectiveness and guide the TT performance. In addition, this study aimed to explore TT performance and the relationship between TT government support, infrastructure, TT environment, and TT learning capability. Oil production in Libya is dependent on foreign technology transferred into the country by foreign multinational petroleum companies. During the 1980s, the Libyan government launched a program of development known as “Libyanization” in the Libyan petroleum industry in an effort to create an absorptive capacity to acquire petroleum technology dominated by foreign companies. This study evaluates the level of technical change because of TT programs and the impact on knowledge and competitiveness performance of the Libyan petroleum industry. A questionnaire survey was administered to companies in the Libyan petroleum industry. There were 201 responses from industry professionals in the Libyan petroleum industry that were analyzed using structural equation modeling (SEM), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). In addition, the significance of direct and indirect interrelationships between model factors was determined through SEM. A path model was estimated and specified to include three process enablers, namely government support, host characteristics, and learning technology capability, and one outcome factor named TT performance. The results suggested that government support factor (government support, laws and regulations, petroleum industry strategy, international quality standards, and information technology) and technology learning capability factor (i.e., supervision, adoption, teamwork, absorption, training, technology complexity, and industry knowledge) were determined to be the key predictors of TT performance to the host petroleum industry.

Trajectory tracking for a serial robot manipulator passing through singular configurations based on the adaptive kinematics Jacobian method

Abstract This paper discusses the use of artificial neural networks (ANNs) as a method of trajectory tracking control for a robotic system. Using an ANN does not require any prior knowledge of the kinematics model of the system being controlled; the basic idea of this concept is the use of the ANN to learn the characteristics of the robot system rather than to specify an explicit robot system model. In this approach, disadvantages of some schemes such as the fuzzy learning control, for example, have been elevated. Off-line training was performed for a geometric trajectory that is free of obstacles. Studying the kinematics Jacobian of serial manipulators by using ANNs has two problems: one of these is the selection of the appropriate configuration of the network and the other is the generation of a suitable training dataset. In this approach, although this is very difficult in practice, training data were recorded experimentally from sensors fixed on each joint to overcome the effect of kinematics uncertainties present in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in the gear train. Then two network configurations were compared to find the best configuration to be used. Finally, the simulation results were verified experimentally using a general six-degree-of-freedom (DOF) serial robot manipulator.

Variable Selection-based Multivariate Cumulative Sum Control Chart.

High-dimensional applications pose a significant challenge to the capability of conventional statistical process control techniques in detecting abnormal changes in process parameters. These techniques fail to recognize out-of-control signals and locate the root causes of faults especially when small shifts occur in high-dimensional variables under the sparsity assumption of process mean changes. In this paper, we propose a variable selection-based multivariate cumulative sum (VS-MCUSUM) chart for enhancing sensitivity to out-of-control conditions in high-dimensional processes. While other existing charts with variable selection techniques tend to show weak performances in detecting small shifts in process parameters due to the misidentification of the ‘faulty’ parameters, the proposed chart performs well for small process shifts in identifying the parameters. The performance of the VS-MCUSUM chart under different combinations of design parameters is compared with the conventional MCUSUM and the VS-multivariate exponentially weighted moving average control charts. Finally, a case study is presented as a real-life example to illustrate the operational procedures of the proposed chart. Both the simulation and numerical studies show the superior performance of the proposed chart in detecting mean shift in multivariate processes. Copyright

What is Middle Maintenance Policy

The main purpose of this paper is to propose several new maintenance policies that can be performed at suitable middle times for trivariate preventive maintenance scenarios. Firstly, characteristics and limitations for the approaches of whichever occurs first and whichever occurs last are analyzed, and their respective maintenance models, which are called first maintenance and last maintenance, are given directly. Secondly, a combined approach of first and last is employed and middle maintenance is proposed. Thirdly, the aforementioned policy is modified to be performed at discrete working cycles, using the method of overtime maintenance. Finally, a new approach of whichever occurs middle is proposed for modelings. We give the analyses of how to formulate the middle maintenances and obtain their expected maintenance cost rates, using the technique of cumulative damage models. Numerical examples are given to compare the optimal policies among first, last, and middle maintenances. Finally, a potential application of middle maintenance is provided for future studies. Copyright

Numerical and experimental investigation of corrugated tubes under lateral compression

ABSTRACT As a common type of energy absorbers, thin-walled structures have been extensively used in crashworthiness application in many industries. The goal of this research is the optimisation of the crushing parameters of corrugated tubes in terms of the number and shape of corrugations. Six different configurations were experimentally investigated; one refers to the straight tube without configurations and the others to corrugated tubes with different geometrical parameters. First, lateral compression tests were carried out and the experimental results were used to validate the finite element models. Other six different configurations of corrugated tubes were analysed by means of finite element simulations. The obtained results demonstrate the advantage of the corrugated tubes with respect to the straight tube in terms of the total absorbed energy and the crushing load. Moreover, the geometrical shape of corrugation (i.e. the ratio of corrugation base to its depth) plays an important role in enhancing the efficiency of the corrugated tubes.