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Metallurgical Engineering
ENSURING THE EXPERTISE TO GROW SOUTH AFRICA Discipline-specific Training Guideline for Candidate Engineers in Metallurgical Engineering R-05-MET-PE Revision No.: 2: 25 July 2019 ENGINEERING COUNCIL OF SOUTH AFRICA Tel: 011 6079500 | Fax: 011 6229295 Email: [email protected] | Website: www.ecsa.co.za ENSURING THE Document No.: Effective Date: Revision No.: 2 R-05-MET-PE 25/07/2019 Subject: Discipline-specific Training Guideline for Candidate Engineers in Metallurgical Engineering Compiler: Approving Officer: Next Review Date: Page 2 of 46 MB Mtshali EL Nxumalo 25/07/2023 TABLE OF CONTENTS DEFINITIONS ............................................................................................................................ 3 BACKGROUND ......................................................................................................................... 5 1. PURPOSE OF THIS DOCUMENT ......................................................................................... 5 2. AUDIENCE............................................................................................................................. 6 3. PERSONS NOT REGISTERED AS CANDIDATES OR NOT BEING TRAINED UNDER COMMITMENT AND UNDERTAKING (C&U) ..................................................... 7 4. ORGANISING FRAMEWORK FOR OCCUPATIONS ............................................................. 7 4.1 Extractive Metallurgical Engineering ..................................................................................... 8 4.2 Mineral Processing Engineering .......................................................................................... -
Control in Robotics
Control in Robotics Mark W. Spong and Masayuki Fujita Introduction The interplay between robotics and control theory has a rich history extending back over half a century. We begin this section of the report by briefly reviewing the history of this interplay, focusing on fundamentals—how control theory has enabled solutions to fundamental problems in robotics and how problems in robotics have motivated the development of new control theory. We focus primarily on the early years, as the importance of new results often takes considerable time to be fully appreciated and to have an impact on practical applications. Progress in robotics has been especially rapid in the last decade or two, and the future continues to look bright. Robotics was dominated early on by the machine tool industry. As such, the early philosophy in the design of robots was to design mechanisms to be as stiff as possible with each axis (joint) controlled independently as a single-input/single-output (SISO) linear system. Point-to-point control enabled simple tasks such as materials transfer and spot welding. Continuous-path tracking enabled more complex tasks such as arc welding and spray painting. Sensing of the external environment was limited or nonexistent. Consideration of more advanced tasks such as assembly required regulation of contact forces and moments. Higher speed operation and higher payload-to-weight ratios required an increased understanding of the complex, interconnected nonlinear dynamics of robots. This requirement motivated the development of new theoretical results in nonlinear, robust, and adaptive control, which in turn enabled more sophisticated applications. Today, robot control systems are highly advanced with integrated force and vision systems. -
Technical Report 05-73 the Methodology Center, Pennsylvania State University
Engineering Control Approaches for the Design and Analysis of Adaptive, Time-Varying Interventions Daniel E. Rivera and Michael D. Pew Control Systems Engineering Laboratory Department of Chemical and Materials Engineering Arizona State University Tempe, Arizona 85287-6006 Linda M. Collins The Methodology Center and Department of Human Development and Family Studies Penn State University State College, PA Susan A. Murphy Institute for Social Research and Department of Statistics University of Michigan Ann Arbor, MI May 30, 2005 Technical Report 05-73 The Methodology Center, Pennsylvania State University 1 Abstract Control engineering is the field that examines how to transform dynamical system behavior from undesirable conditions to desirable ones. Cruise control in automobiles, the home thermostat, and the insulin pump are all examples of control engineering at work. In the last few decades, signifi- cant improvements in computing and information technology, increasing access to information, and novel methods for sensing and actuation have enabled the extensive application of control engi- neering concepts to physical systems. While engineering control principles are meaningful as well to problems in the behavioral sciences, the application of this topic to this field remains largely unexplored. This technical report examines how engineering control principles can play a role in the be- havioral sciences through the analysis and design of adaptive, time-varying interventions in the prevention field. The basic conceptual framework for this work draws from the paper by Collins et al. (2004). In the initial portion of the report, a general overview of control engineering principles is presented and illustrated with a simple physical example. From this description, a qualitative description of adaptive time-varying interventions as a form of classical feedback control is devel- oped, which is depicted in the form of a block diagram (i.e., a control-oriented signal and systems representation). -
Industrial and Systems Engineering (ISE)
Lehigh University 2021-22 1 Industrial and Systems Engineering (ISE) Courses ISE 224 Information Systems Analysis and Design 3 Credits ISE 100 Industrial Employment 0 Credits An introduction to the technological as well as methodological Usually following the junior year, students in the industrial engineering aspects of computer information systems. Content of the course curriculum are required to do a minimum of eight weeks of practical stresses basic knowledge in database systems. Database design and work, preferably in the field they plan to follow after graduation. A evaluation, query languages and software implementation. Students report is required. Must have sophomore standing. that take CSE 241 cannot receive credit for this course. ISE 111 Engineering Probability 3 Credits ISE 226 Engineering Economy and Decision Analysis 3 Credits Random variables, probability models and distributions. Poisson Economic analysis of engineering projects; interest rate factors, processes. Expected values and variance. Joint distributions, methods of evaluation, depreciation, replacement, breakeven covariance and correlation. analysis, aftertax analysis. decision-making under certainty and risk. Prerequisites: MATH 022 or MATH 096 or MATH 032 or MATH 052 Prerequisites: ISE 111 or MATH 231 or IE 111 Can be taken Concurrently: ISE 111, MATH 231, IE 111 ISE 112 Computer Graphics 1 Credit Introduction to interactive graphics and construction of multiview ISE 230 Introduction to Stochastic Models in Operations representations in two and three dimensional space. Applications in Research 3 Credits industrial engineering. Must have sophomore standing in industrial Formulating, analyzing, and solving mathematical models of real- engineering. world problems in systems exhibiting stochastic (random) behavior. Discrete and continuous Markov chains, queueing theory, inventory ISE 121 Applied Engineering Statistics 3 Credits control, Markov decision process. -
Control Theory
Control theory S. Simrock DESY, Hamburg, Germany Abstract In engineering and mathematics, control theory deals with the behaviour of dynamical systems. The desired output of a system is called the reference. When one or more output variables of a system need to follow a certain ref- erence over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system. Rapid advances in digital system technology have radically altered the control design options. It has become routinely practicable to design very complicated digital controllers and to carry out the extensive calculations required for their design. These advances in im- plementation and design capability can be obtained at low cost because of the widespread availability of inexpensive and powerful digital processing plat- forms and high-speed analog IO devices. 1 Introduction The emphasis of this tutorial on control theory is on the design of digital controls to achieve good dy- namic response and small errors while using signals that are sampled in time and quantized in amplitude. Both transform (classical control) and state-space (modern control) methods are described and applied to illustrative examples. The transform methods emphasized are the root-locus method of Evans and fre- quency response. The state-space methods developed are the technique of pole assignment augmented by an estimator (observer) and optimal quadratic-loss control. The optimal control problems use the steady-state constant gain solution. Other topics covered are system identification and non-linear control. System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data. -
Major Factors That Influence the Employment Decisions of Generation X Consulting Engineers
Old Dominion University ODU Digital Commons Engineering Management & Systems Engineering Management & Systems Engineering Theses & Dissertations Engineering Spring 2002 Major Factors That Influence the Employment Decisions of Generation X Consulting Engineers Robert William Mayfield Old Dominion University Follow this and additional works at: https://digitalcommons.odu.edu/emse_etds Part of the Engineering Commons, Organizational Behavior and Theory Commons, and the Work, Economy and Organizations Commons Recommended Citation Mayfield, Robert W.. "Major Factors That Influence the Employment Decisions of Generation X Consulting Engineers" (2002). Master of Science (MS), Thesis, Engineering Management & Systems Engineering, Old Dominion University, DOI: 10.25777/t41p-rd52 https://digitalcommons.odu.edu/emse_etds/102 This Thesis is brought to you for free and open access by the Engineering Management & Systems Engineering at ODU Digital Commons. It has been accepted for inclusion in Engineering Management & Systems Engineering Theses & Dissertations by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. MAJOR FACTORS THAT INFLUENCE THE EMPLOYMENT DECISIONS OF GENERATION X CONSULTING ENGINEERS bv Robert William Mayfield B.S. March 1994, The Ohio State University A Thesis Submitted to the Faculty o f Old Dominion University in Partial Fulfillment o f the Requirement for the Degree of MASTER OF SCIENCE ENGINEERING MANAGEMENT OLD DOMINION UNIVERSITY May 2002 Approved by: Charles Keating (Direct Paul Kauffmai ember) Andres Sousa-Poza (Member) Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ABSTRACT MAJOR FACTORS THAT INFLUENCE THE EMPLOYMENT DECISIONS OF GENERATION X CONSULTING ENGINEERS Robert William Mayfield Old Dominion University. 2002 Director: Dr. Charles Keating The purpose of this research was to study Generation X consulting engineers (those bom between the years 1964 and 1980) in Lynchburg. -
Novel Substructural Health Monitoring Method Without Interface Measurements Using State Space Analysis and Extended Kalman Filtering
Novel Substructural Health Monitoring Method without Interface Measurements using State Space Analysis and Extended Kalman Filtering by Arianne Layard Muelhausen Under the Supervision of Prof. Brock Hedegaard A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE (Civil and Environmental Engineering) at the University of Wisconsin-Madison 2018 Contents 1 Introduction 2 2 Literature Review 4 3 Methodology 8 3.1 State Space Analysis . .8 3.2 Kalman Filter . .9 3.3 Extended Kalman Filter . 11 3.4 Substructural Identification using Extended Kalman Filtering . 12 3.4.1 Global Equation of Motion . 13 3.4.2 Substructural Equation of Motion . 14 3.4.3 State Space Formulation: State Transition Equation . 17 3.4.4 State Space Formulation: Measurement Equation . 18 3.4.5 Implementation of Extended Kalman Filter . 19 3.5 Methodology Step-by-Step Summary . 24 4 Results 25 4.1 Model Description . 25 4.2 Case One: All DOFs Measured . 26 4.3 Case Two: All Interface and Some Interior DOFs Measured . 29 4.4 Case Three: Some Interface and Some Interior DOFs Measured . 31 4.5 Case Four: No Interface and Some Interior DOFs Measured . 38 4.6 Discussion on Limitations of Method . 40 5 Conclusion 41 6 Work Cited 42 1 1 Introduction Structural Health Monitoring (SHM) at its core is a process to identify damage, defined as either material or geometric change, of a system that negatively affects the systems performance[7]. SHM seeks to address four questions: (Level 1) Detection: Is damage present? (Level 2) Localization: What is the probable location of damage? (Level 3) Assessment: What is the severity of the damage? (Level 4) Prognosis: What is the remaining service life of the damaged system? SHM can be used to monitor structures affected by external stimuli, long-term movement, ma- terial degradation, or demolition. -
Multivariate Statistical Process Monitoring Using Classical
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Newcastle University eTheses Multivariate Statistical Process Monitoring Using Classical Multidimensional Scaling Prepared by: Mohd Yusri Mohd Yunus A Thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy School of Chemical Engineering and Advanced Materials Newcastle University UK May 2012 ABSTRACT A new Multivariate Statistical Process Monitoring (MSPM) system, which comprises of three main frameworks, is proposed where the system utilizes Classical Multidimensional Scaling (CMDS) as the main multivariate data compression technique instead of using the linear- based Principal Component Analysis (PCA). The conventional method which usually applies variance-covariance or correlation measure in developing the multivariate scores is found to be inappropriately used especially in modelling nonlinear processes, where a high number of principal components will be typically required. Alternatively, the proposed method utilizes the inter-dissimilarity scales in describing the relationships among the monitored variables instead of variance-covariance measure for the multivariate scores development. However, the scores are plotted in terms of variable structure, thus providing different formulation of statistics for monitoring. Nonetheless, the proposed statistics still correspond to the conceptual objective of Hotelling’s T2 and Squared Prediction Errors (SPE). The first framework corresponds to the original -
University Master´S Degree in Systems and Control Engineering
UNIVERSITY MASTER´S DEGREE IN SYSTEMS AND CONTROL ENGINEERING English Version University Master’s Degree in Systems and Control Engineering INFORMATION IDENTIFYING THE QUALIFICATION Name and status of awarding institution Universidad Complutense de Madrid y la Universidad Nacional de Educación a Distancia. Public universities. Name of qualification and title conferred in original language Máster Universitario en Ingeniería de Sistemas y de Control por la Universidad Complutense de Madrid y la Universidad Nacional de Educación a Distancia. Status National validity. Approved by Accord of the Council of Ministers on July 1st, 2011. Main field(s) of study for the qualification The study is included in the field of Engineering and Architecture. Language(s) of instruction/examination The degree is taught in Spanish. INFORMATION ON THE LEVEL OF THE QUALIFICATION Level of qualification Level 3 (Master) in the Spanish Framework of Higher Education (MECES) is equivalent to level 7 of European Qualification Framework (EQF). Official length of programme The official length of programme is 60 ECTS and 1 year full time. Access requirements Engineering or Bachelor’s Degree in Computer Sciences or scientific- technological degrees related to Systems, Automation, Electronics, Communications or Computer Engineering. Doctorate on Automatic or Computer Sciences 2 INFORMATION ON THE CONTENTS Mode of study Blended learning to full time. Programme requirements The programme of studies is composed of 42 selective ECTS, 6 external practices and 12 Master's Dissertation -
Lecture 9 – Modeling, Simulation, and Systems Engineering
Lecture 9 – Modeling, Simulation, and Systems Engineering • Development steps • Model-based control engineering • Modeling and simulation • Systems platform: hardware, systems software. EE392m - Spring 2005 Control Engineering 9-1 Gorinevsky Control Engineering Technology • Science – abstraction – concepts – simplified models • Engineering – building new things – constrained resources: time, money, • Technology – repeatable processes • Control platform technology • Control engineering technology EE392m - Spring 2005 Control Engineering 9-2 Gorinevsky Controls development cycle • Analysis and modeling – Control algorithm design using a simplified model – System trade study - defines overall system design • Simulation – Detailed model: physics, or empirical, or data driven – Design validation using detailed performance model • System development – Control application software – Real-time software platform – Hardware platform • Validation and verification – Performance against initial specs – Software verification – Certification/commissioning EE392m - Spring 2005 Control Engineering 9-3 Gorinevsky Algorithms/Analysis Much more than real-time control feedback computations • modeling • identification • tuning • optimization • feedforward • feedback • estimation and navigation • user interface • diagnostics and system self-test • system level logic, mode change EE392m - Spring 2005 Control Engineering 9-4 Gorinevsky Model-based Control Development Conceptual Control design model: Conceptual control sis algorithm: y Analysis x(t+1) = x(t) + -
On Simulation of Cross Correlated Random Fields Using Series Expansion Methods
ENGINEERING MECHANICS 2009 National Conference with International Participation pp. 1431–1443 Svratka, Czech Republic, May 11 – 14, 2009 Paper #139 ON SIMULATION OF CROSS CORRELATED RANDOM FIELDS USING SERIES EXPANSION METHODS M. Vorechovskˇ y´ 1 Summary: A practical framework for generating cross correlated fields with a specified marginal distribution function, an autocorrelation function and cross cor- relation coefficients is presented in the paper. The approach relies on well known series expansion methods for simulation of a Gaussian random field. The proposed method requires all cross correlated fields over the domain to share an identical au- tocorrelation function and the cross correlation structure between each pair of sim- ulated fields to be simply defined by a cross correlation coefficient. Such relations result in specific properties of eigenvectors of covariance matrices of discretized field over the domain. These properties are used to decompose the eigenproblem which must normally be solved in computing the series expansion into two smaller eigenproblems. Such a decomposition represents a significant reduction of compu- tational effort. Non-Gaussian components of a multivariate random field are proposed to be sim- ulated via memoryless transformation of underlying Gaussian random fields for which the Nataf model is employed to modify the correlation structure. In this method, the autocorrelation structure of each field is fulfilled exactly while the cross correlation is only approximated. The associated errors can be computed before performing simulations and it is shown that the errors happen especially in the cross correlation between distant points and that they are negligibly small in practical situations. 1. Introduction The random nature of many features of physical events is widely recognized both in industry and by researchers. -
Integrated Reliable and Robust Design
Scholars' Mine Masters Theses Student Theses and Dissertations Spring 2011 Integrated reliable and robust design Gowrishankar Ravichandran Follow this and additional works at: https://scholarsmine.mst.edu/masters_theses Part of the Mechanical Engineering Commons Department: Recommended Citation Ravichandran, Gowrishankar, "Integrated reliable and robust design" (2011). Masters Theses. 4860. https://scholarsmine.mst.edu/masters_theses/4860 This thesis is brought to you by Scholars' Mine, a service of the Missouri S&T Library and Learning Resources. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [email protected]. INTEGRATED RELIABLE AND ROBUST DESIGN by GOWRISHANKAR RAVICHANDRAN A THESIS Presented to the Faculty of the Graduate School of the MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE IN MECHANICAL ENGINEERING 2011 Approved by Xiaoping Du, Advisor Arindam Banerjee Shun Takai iii ABSTRACT The objective of this research is to develop an integrated design methodology for reliability and robustness. Reliability-based design (RBD) and robust design (RD) are important to obtain optimal design characterized by low probability of failure and minimum performance variations respectively. But performing both RBD and RD in a product design may be conflicting and time consuming. An integrated design model is needed to achieve both reliability and robustness simultaneously. The purpose of this thesis is to integrate reliability and robustness. To achieve this objective, we first study the general relationship between reliability and robustness. Then we perform a numerical study on the relationship between reliability and robustness, by combining the reliability based design, robust design, multi objective optimization and Taguchi’s quality loss function to formulate an integrated design model.