Doctor of Philosophy (Ph.D.) in Mechanical Engineering

Kurum: Koç Üniversitesi

Metod: Kurumda

Yerleşim yeri:

Program Türü: Doktora Programları

Program ücreti: İsteğe Bağlı

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Koç Üniversitesi

Doctor of Philosophy (Ph.D.) in Mechanical Engineering - Sarıyer - İstanbul

Doctor of Philosophy (Ph.D.) in Mechanical Engineering - Sarıyer - İstanbul

Program Tanımları:

Innovation in product design and manufacturing has become a major driver for industrial competitiveness and profitablity in recent years. As enabling technologies become more easily accessible, engineers are faced with increasing demands for designing and producing more complex mechanical devices to serve the needs of the society. Next generation engineering products will be 'smart' with many functionalities; they will be made of new materials; they will increase energy efficiency and reduce environmental impact; they will vary in size from nano to mega scales; and they will be more closely integrated with information processing systems. Also as mechanical systems are becoming increasingly complex to analyze and expensive to experiment, more emphasis will have to be placed on computer aided analysis, design, verification and manufacturing. Our research program in mechanical engineering responds to these trends and focuses on basic research related to materials science and process engineering, product design, and information integrated manufacturing processes. In doing so applications to different physical processes are studied (e.g. energy systems, bioengineering, metal forming, polymer processing, discrete part manufacturing to name a few).

Students can apply to the Ph.D. programs with a B.S. or M.S. degree. The Ph.D. degree requires successful completion of 14 courses beyond the B.S. degree or 7 courses beyond the M.S. degree. All students must pass the Ph.D. Qualifying Examination in the first year after they have been admitted to the Ph.D. program. Students are encouraged to begin research early. Students who have passed the Ph.D. qualifying examination are assisted in matters pertaining to their thesis research by a faculty thesis advisory committee. The research advisor serves as the chair of this committee. The committee meets with the student at least once each semester. Ph.D. students must submit a satisfactory written Ph.D. thesis proposal in their second year of study. At the completion of the Ph.D. research, the students must submit a written Thesis and pass an oral defense to complete the degree requirements.

Research Areas: Micro and Nanofabrication, MEMS-based Biosensors, Materials Behaviour, Engineering Mechanics

Cagatay Basdogan

Research Areas: Robotics, Mechatronics, Control Systems, Human-Machine Interfaces, Nanotechnology, Biomechanics, Computer Graphics, and Virtual Reality

Burak Erman

Research Areas: Computational studies of polymers, proteins and complex biological systems

Ismail Lazoglu

Research Areas: Automation and Mechatronics, Computer Aided Design (CAD) / Engineering (CAE) / Manufacturing (CAM), Dynamic Systems and Control, Artificial Organs, Development of Medical Assist Systems

Metin Muradoglu

Research Areas: Multiphase flows in bio/micro fluid systems, turbulent combustion, scientific computing. Thesis Projects

Research Areas: Manufacturing of composite materials; modeling and control of liquid composite molding (LCM) processe

Curriculum

Graduate curriculum consists of the following 3-credit courses:

- CMSE 501 Introduction to Computational Science
- CMSE 581 Selected Topics in Computational Chemistry
- CMSE 582 Selected Topics in Computational Physics
- CMSE 583 Selected Topics in Computational Biology
- ECOE 510 Computer Graphics
- ECOE 521 Photonics and Lasers
- ECOE 522 Micro-opto-electro-Mechanical Systems
- ECOE 554 Machine Learning
- ECOE 570 Bioinformatics and Algorithms in Computational Biology
- INDR 501 Optimization Models and Algorithms
- INDR 505 Manufacturing Systems
- INDR 508 Discrete Event Simulation
- INDR 510 Mathematical Statistics
- INDR 551 Advanced Optimization Methods
- INDR 560 Large Scale Optimization
- INDR 564 Dynamic Programming
- INDR 572 Reliability Theory
- MATH 503 Applied Mathematics
- MATH 504 Numerical Methods
- MATH 506 Numerical Methods II
- MASE 501 Structure of Materials(1,5 credits)
- MASE 502 Electrical & Optical Properties of Materials (1,5 credits)
- MASE 503 Thermodynamics & Kinetics
- MASE 504 Thermal Properties of Materials (1,5 credits)
- MASE 505 Mechanical Properties of Materials (1,5 credits)
- MASE 506 Synthesis, Characterization & Processing of Materials (4 credits)
- MASE 510 Synthetic Polymer Chemistry
- MASE 511 Introduction to Polymer Science
- MASE 522 Vibrational Spectroscopy
- MASE 530 Materials Behaviour
- MASE 532 Statistical Mechanics of Polymers
- MASE 534 Rubber Elasticity
- MASE 536 Multicomponent Polymeric Systems
- MASE 538 Intermolecular and Surface Forces
- MASE 540 Surface & Interface Properties of Materials
- MASE 542 Biomaterials
- MASE 544 Nanoparticle Science and Technology
- MASE 550 Optical Spectroscopy of Materials and Devices
- MASE 570 Micro and Nanofabrication
- MASE 571 Semiconductor Processing Methods

Courses are selected by the students from the above list and from other courses not listed here in accordance with their areas of specialization and subject to the approval of their advisors. In addition, each student has to take a seminar course, MECH 590 Seminar. Students also register for the thesis course.

- MECH 590 Seminar
- MECH 596 PhD Thesis
- TEAC 500 Teaching Experience

An introduction to methods and software tools used in scientific computing. Software development, data abstraction and the concept of object oriented programming. Hands-on exploration of some of the principal modern software tools of computational science including computing environments, symbolic computing, numerical libraries and software repositories. An introduction to high performance computing and parallel programming.

Theory and practice of 3D computer graphics. Topics covered include graphics systems and models; geometric representations and transformations; graphics programming; input and interaction; viewing and projections; compositing and blending; illumination and color models; shading; texture mapping; animation; rendering and implementation; hierarchical and object-oriented modeling; scene graphs; 3D reconstruction and modeling.

Prerequisite: COMP 202 or consent of the instructor.

Review of electromagnetism; electromagnetic nature of light, radiation, geometrical optics, Gaussian beams, transformation of Gaussian beams; electromagnetic modes of an optical resonator, interaction of light with matter, classical theory of absorption and dispersion, broadening processes, Rayleigh scattering, quantum theory of spontaneous and stimulated emission, optical amplification, theory of laser oscillation, examples of laser systems, Q switching and mode locking of lasers.

Prerequisite: ELEC 206 or consent of the instructor.

Introduction to microsystems and micro-electro-mechanical-systems (MEMS) and their integration with optics; microfabrication and process integration; MEMS modeling and design; actuator and sensor design; mechanical structure design; optical system design basics; packaging; optical MEMS application case studies; scanning systems (Retinal Scanning Displays, Barcode scanners); projection display systems (DMD and GLV); infrared imaging cameras; optical switching for telecommunications.

Prerequisite: ELEC 321 or consent of the instructor.

An introduction to the fields of machine learning and data mining from a statistical perspective. Machine learning is the study of computer algorithms that improve automatically through experience. Vast amounts of data generated in many fields from biology to finance to linguistics makes a good understanding of the tools and techniques of machine learning indispensable. Topics covered include regression, classification, kernel methods, model assessment and selection, boosting, neural networks, support vector machines, nearest neighbors, and supervised learning.

Prerequisite: Consent of the instructor.

Algorithms, models, representations, and databases for collecting and analyzing biological data to draw inferences. Overview of available molecular biological databases. Sequence analysis, alignment, database similarity searches. Phylogenetic trees. Discovering patterns in protein sequences and structures. Protein 3D structure prediction: homology modeling, protein folding, representation for macromolecules, simulation methods. Protein-protein interaction networks, regulatory networks, models and databases for signaling networks, data mining for signaling networks.

Convex analysis, optimality conditions, linear programming model formulation, simplex method, duality, dual simplex method, sensitivity analysis; assignment, transportation, and transshipment problems.

Prerequisite: Consent of the instructor.

This course will cover the basic concepts and techniques in hierarchical design, planning, and control of manufacturing systems. Topics include assembly lines, general serial systems, group technology and cellular manufacturing, flexible manufacturing systems, facility layout, material handling systems and warehousing.

Prerequisite: Consent of the instructor.

Topics on distribution fitting and generating random numbers and random varieties will be covered as well as the statistical analysis of simulation output, including some well-known analysis methods and variance reduction techniques. Recent developments in the area will also be discussed.

Prerequisite: INDR 503 or consent of the instructor.

Review of descriptive statistics, important population statistics and their distributions. Point estimation, estimation methods and minimum-variance unbiased estimators. Testing hypothesis, Neyman-Pearson lemma and likelihood ratio tests. Estimation and testing in linear regression modes. Analysis of variance models. Non-parametric statistics methods. Bayesian testing and analysis.

Prerequisite: INDR 252 or consent of the instructor.

Combinatorial optimization, structure of integer programs, pure integer and mixed integer programming problems, branch and bound methods, cutting plane and polyhedral approach, convexity, local and global optima, Newton-type, and conjugate gradient methods for unconstrained optimization, Karush-Kuhn-Tucker conditions for optimality, algorithms for constrained nonlinear programming problems, applications in combinatorial and nonlinear optimization.

Prerequisite: INDR 501 or consent of the instructor.

Methods for the solution of complex real world problems modeled as large-scale linear, nonlinear and stochastic programming, network optimization and discrete optimization problems. Solution methods include Decomposition Methods: Bender's, Dantzig-Wolfe, Lagrangian Methods; Meta-heuristics: Local search, simulated annealing, tabu search, genetic algorithms; Constraint Programming. Applications in transportation and logistics planning, pattern classification and image processing, data mining, design of structures, scheduling in large systems, supply-chain management, financial engineering, and telecommunications systems planning.

Prerequisite: INDR 501 or consent of the instructor.

Theory and practice of dynamic programming, sequential decision making over time; the optimal value function and Bellman's functional equation for finite and infinite horizon problems; Introduction of solution techniques: policy iteration, value iteration, and linear programming; General stochastic formulations, Markov decision processes; application of dynamic programming to network flow, resource allocation, inventory control, equipment replacement, scheduling and queueing control.

Prerequisite: INDR 501 and INDR 503 or consent of the instructor.

Basic concepts and definitions of system reliability. Series, parallel, k-out-of n systems. Structure functions, coherent systems, min-path and min-cut representations. System reliability assessment and computing reliability bounds. Parametric families of distributions, classes of life distributions and their properties. Shock and wear models. Maintenance, replacement and repair models. Current issues on stochastic modeling of hardware and software reliability.

Prerequisite: INDR 503 or consent of the instructor.

Review of Linear Algebra and Vector Fields: Vector Spaces, Eigenvalue Problems, Quadratic Forms, Divergence Theorem and Stokes’ Theorem. Sturm-Liouville Theory and Orthogonal Polynomials, Methods of Solution of Boundary Value Problems for the Laplace Equation, Diffusion Equation and the Wave Equation. Elements of Variational Calculus.

Review of Linear Algebra: linear spaces, orthogonal matrices, norms of vectors and matrices, singular value decomposition. Projectors, QR Factorization Algorithms, Least Squares, Conditioning and Condition Numbers, Floating Point Representation, Stability, Conditioning and Stability of Least Squares, Conditioning and Stability Analysis of Linear Systems of Equations.

Numerical Solution of Functional Equations, the Cauchy Problem and Boundary Value Problems for Ordinary Differential Equations. Introduction to the Approximation Theory of One Variable Functions. Finite - difference Methods for Elementary Partial Differential Equations. Monte Carlo Method and Applications.

Structure of materials; atomic structure and bonding, crystalline solids, symmetry, lattice and unit cell, determination of crystal structures; imperfections, defects in metals, vacancies, substitutional and interstitial impurities, dislocation defects in ionic solids.

Electrical properties of materials, band theory of solids, electrical conductivity, metals, semiconductors, and dielectrics; magnetic phenomena, ferromagnetism and diamagnetism, superconductors; optical properties of materials, refractive index, dispersion, absorption and emission of light, nonlinear optical properties, second- and third-order susceptibilities, Raman effect.

Classical thermodynamics: enthalpy, entropy, free energies, equilibria; introduction to statistical thermodynamics to describe the properties of materials; kinetic processes; diffusion of mass, heat, energy; fundamentals of rate processes in materials, kinetics of transformations.

Thermal properties of metals, polymers, ceramics and composites in relation to their structure & morphology; change in microstructural mechanisms and macroscopic behaviour with temperature; crystallization, melting & glass transition.

Mechanical Properties of Materials

Mechanical properties of metals, polymers, ceramics and composites in relation to their structure & morphology; stress-strain behaviour; elastic deformation, yielding, plastic flow; viscoelasticity; strengthening mechanisms, fracture, fatigue, creep.

Experimental projects in the laboratory including topics from polymer synthesis & processing, composite materials, inorganic material/ceramic processing, metal processing, optical properties, electrical & magnetic properties, interfacial properties.

Introduction to polymers (nomenclature, tacticity, molecular weight, physical state, properties & applications); Synthesis of polymers and macromolecular structures: step growth polymerization, chain growth polymerization; polymer reactions.

Prerequisite: Consent of the instructor.

Differences between the small molecules and macromolecules, thermosets and thermoplastics, and structure-property relationships in polymers. Introduces main polymer families. Also discusses supramolecular structures, blends, composites and IPNs.

Prerequisite: Consent of the instructor.

Molecular symmetry, group theory, reducible and irreducible representation, character tables, introduction to vibrational spectroscopy, Raman effect, infrared absorption, selection rules, pure rotational spectroscopy, normal modes, prediction and interpretation of the vibrational spectra of polyatomic species.

Materials behavior using phenomenological and microstructure-based approaches. Topics include plasticity, fracture, fatigue and micromechanics.

Statistical mechanics of the single chain, configurational averages, polymer solution statistics and thermodynamics, dilute and concentrated polymer solutions, the bulk state of polymers, critical phenomena and phase equilibria; numerical techniques for polymeric systems.

Classical theories of rubber elasticity, elasticity of the single chain, intermolecular effects, effects of entanglements, relationships between stress and strain, swelling of networks, critical phenomena and phase transitions in gels, thermoelastic behavior of elastomers, computational aspects.

Block and segmented copolymers, polymer blends and composites; design, preparation, properties and applications of multicomponent polymeric materials; phase separation in polymeric systems; structure-morphology-property relations in multicomponent polymers.

Prerequisite: CHEM 410 and MASE 510 and MASE 511

Intermolecular forces which govern self-organization of biological and synthetic nanostructures. Thermodynamic aspects of strong (covalent and coulomb interactions) and weak forces (dipolar, hydrogen bonding). Self-assembling systems: micelles, bilayers, and biological membranes. Computer simulations for "hands-on" experience with nanostructures.

Prerequisites: CHEM 301 or consent of the instructor.

Fundamental physico-chemical concepts of surface and interface science; interaction forces in interfacial systems; surface thermodynamics, structure and composition, physisorption and chemisorption; fluid interfaces; colloids; amphiphilic systems; interfaces in polymeric systems & polymer composites; liquid coating processes.

Materials for biomedical applications; synthetic polymers, metals and composite materials as biomaterials; biopolymers, dendrimers, hydrogels, polyelectrolytes, drug delivery systems, implants, tissue grafts, dental materials, ophthalmic materials, surgical materials, imaging materials.

Prerequisite: At least one semester of organic chemistry or consent of the instructor.

Size related properties of nanoparticles; synthetic strategies, main characterization tools, challenges and solutions, surface functionalization, technological applications and current trends.

Prerequisite: Consent of the instructor.

Interaction of electromagnetic radiation with atoms and molecules, rotational spectroscopy, vibrational spectroscopy, electronic spectroscopy, spectroscopic instrumentation, lasers as spectroscopic light sources, fundamentals of lasers, nonlinear optical spectroscopy, laser Raman spectroscopy.

Prerequisite: Consent of the instructor.

Fabrication and characterization techniques for micro and nano electro mechanical systems, MEMS & NEMS (including: microlithography; wet & dry etching techniques; physical & chemical vapor deposition processes; electroplating; bonding; focused ion beams; top-down approaches - electron-beam lithography, SPM, soft lithography - ; bottom-up techniques based on self-assembly). Semiconductor nanotechnology. Nanotubes & nanowires. Biological systems. Molecular electronics.

Prerequisite: MECH 202 or consent of the instructor.

Introduction, material properties, crystal growth, epitaxy, ion implantation, cleaning, wet etching, photolithography, non-optical lithography, plasma processing, dry etching, metal deposition, diagnostic techniques.

A series of lectures given by faculty or outside speakers. Participating students must also make presentations during the semester.

Independent research towards Ph.D. degree.

Provides hands-on teaching experience to graduate students in undergraduate courses. Reinforces students' understanding of basic concepts and allows them to communicate and apply their knowledge of the subject matter.

This is a writing course specifically designed to improve academic writing skills as well as critical reading and thinking. The course objectives will be met through extensive reading, writing and discussion both in and out of class. Student performance will be assessed and graded by Satisfactory/Unsatisfactory.

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