Mechanical and Manufacturing Engineering (MMIE)

MMIE 7100. MMIE PhD Seminar.

This course introduces Ph.D. students to key resources and professional practices in the Mechanical and Manufacturing Engineering program. The scope includes strategies for literature search, effective use of labs and facilities, utilizing artificial intelligence tools, selecting a research topic and advisor, understanding copyright and plagiarism, and improving technical writing skills. The course is delivered through seminars, guest lectures, and guided exercises.

MMIE 7199. Dissertation.

This course provides Ph.D. students with the opportunity to conduct original research in mechanical and manufacturing engineering under the guidance of a dissertation advisor. The scope includes identification of research problems, formulation of hypotheses, experimental or computational investigation, data analysis, and preparation of the dissertation document. Students are expected to apply advanced engineering knowledge, research methodologies, and scholarly practices throughout the research process. The course is delivered through independent research, regular advisor meetings, and progress presentations.

MMIE 7299. Dissertation.

This course provides Ph.D. students with the opportunity to conduct original research in mechanical and manufacturing engineering under the guidance of a dissertation advisor. The scope includes identification of research problems, formulation of hypotheses, experimental or computational investigation, data analysis, and preparation of the dissertation document. Students are expected to apply advanced engineering knowledge, research methodologies, and scholarly practices throughout the research process. The course is delivered through independent research, regular advisor meetings, and progress presentations.

MMIE 7305. Advanced Design of Experiments.

This course aims to plan, design and conduct statistical experiments efficiently and effectively, and analyze the measurement data to derive valid conclusions and insights. Students use computer experiments and software tools to optimize manufacturing, energy and service industries based on both deterministic and stochastic models. Topics include full and fractional factorial designs, blocking and confounding design, regression model, response surface method and design, robust parameter design and Taguchi method. Through the course project, students apply the optimal experimental design methodology to improve a real manufacturing or service process at low cost and effect.

MMIE 7310. Machine Learning and Artificial Intelligence for Engineers.

This course examines fundamental artificial intelligence and machine learning methods for engineering design and related applications. Topics include search, constraint satisfaction, probability, data mining, pattern recognition, neural networks, and implementation issues. The course also explores applications in design representation, decision support, and automation. Through analytical study and applied problem solving, students learn how these methods are formulated and used in engineering contexts.

MMIE 7311. Cyber-Physical Systems Architecture.

This course examines principles for architecting Cyber-Physical Systems (CPS). Students learn to resolve ambiguity to define system goals and boundaries, while mastering the creative process of mapping form to function. The curriculum covers formal methods for system decomposition and re-integration, utilizing both heuristic and formal methods. By analyzing real-time constraints and emergent behaviors, students gain the essential frameworks necessary to manage the lifecycle of sophisticated, multi-domain systems from initial conceptualization to final deployment and maintenance.

MMIE 7312. Digital Twins.

This course introduces the concept of Digital Twins and describes how they are applied in engineering and what should be considered to implement this technology in products and manufacturing systems. Considerations include the required information technology infrastructure, enabling technologies, the business value of implementing Digital Twins, and what needs to happen across an entire organization to ensure successful implementation. Students learn the digital twin approach to the design, operation, and maintenance of industrial assets and products.

MMIE 7313. Advanced Robotics .

This course provides an advanced study of robotic technology and design techniques with emphasis on safe and effective human–robot interaction. The scope includes forward and inverse kinematics, velocity kinematics, introduction to dynamics and control theory, sensors and actuators, and fundamentals of computer vision. Modular robot programming and simulation-based applications are integrated to reinforce theoretical knowledge. The course is delivered through lectures, hands-on simulations, and applied problem-solving exercises.

MMIE 7314. Human-Robot Interaction.

This course provides an advanced study of human–robot interaction (HRI) and social robot learning, emphasizing the design and evaluation of robots in real-world human environments. The scope includes physical embodiment, mixed-initiative interaction, multi-modal interfaces, human–robot teamwork, learning algorithms, social cognition, and long-term interaction strategies. Students critically examine current research, design principles, and technical challenges in HRI. The course is delivered through lectures, case studies, and simulation-based projects.

MMIE 7315. Advanced Additive Manufacturing.

This course provides an advanced study of additive manufacturing (AM) technologies, emphasizing theoretical principles, current standards, techniques, and applications. The scope includes process development, safety considerations, contemporary technologies, and emerging trends in AM. Students engage in literature reviews and team-based projects to explore futuristic ideas, biomimicry, materials, and process innovations. The course is delivered through lectures, applied projects, and research-driven discussions.

MMIE 7316. Cybersecurity for Mechanical and Manufacturing Systems.

This course provides an advanced study of cybersecurity principles applied to mechanical and manufacturing systems. The scope includes protection of information assets, integration of technical controls with organizational policies and best practices, and identification of external and internal security threats. Students examine risks to people, processes, data, facilities, and technologies in highly connected manufacturing enterprises. The course also covers implementation and management of security architectures and methods for testing and defending against attacks. Delivered through lectures, case studies, and applied projects, the course prepares students to secure complex manufacturing and engineering systems.

MMIE 7317. Applied Data Science I.

This course introduces applied data science methods for engineering and decision making applications with an emphasis on supervised machine learning. Topics include data preprocessing, feature selection, model training and validation, performance evaluation, and software tools for implementation. The course emphasizes practical use of data driven methods to analyze data, build predictive models, and support decisions in real world settings. Through computational problem solving and application focused examples, students develop the ability to implement, assess, and interpret machine learning models across engineering and management domains. Emphasis is placed on understanding model assumptions, performance tradeoffs, and limitations relevant to applied engineering contexts Prerequisite: MMIE 7305 with a grade of "B" or better.

MMIE 7318. Applied Data Science II.

This course examines applied data science methods for learning from unlabeled and high dimensional data. Topics include clustering, dimensionality reduction, generative models, feature learning, and deep unsupervised learning, with emphasis on model formulation, interpretation, and computational implementation. The course focuses on extracting patterns, structure, and insights from complex data arising in engineering and management applications. Through analytical study and software based implementation, students develop the ability to select, apply, and evaluate unsupervised learning methods. Emphasis is placed on interpretability, computational considerations, and the practical use of unsupervised models in real world problem settings. Prerequisite: MMIE 7317 with a grade of "B" or better.

MMIE 7320. Advanced Solid Mechanics.

This course examines advanced principles of solid mechanics, including elasticity theory, stress–deformation analysis, and boundary value problem formulation. Students analyze tensor representations, coordinate transformations, alternative measures of strain, stress measures, and elastic constitutive equations. The topics covered encompass analytical and computational methods for solving practical elasticity problems in engineering components, including thick-walled cylinders, rotating disks, and thin plates. Topics further include energy methods, stress function approaches, and assessment of material behavior under mechanical and thermal loading. Applications emphasize the evaluation and prediction of structural performance in mechanical, aerospace, and manufacturing systems, illustrating how theoretical models are applied to real-world engineering problems.

MMIE 7322. Advanced Fluid Mechanics.

This course provides advanced study of fluid dynamics, emphasizing the governing principles and mathematical models used to analyze fluid behavior in engineering systems. Topics include conservation of mass, momentum, and energy; incompressible inviscid and viscous flows; the Navier–Stokes equations; similarity and dimensional analysis; boundary layers and separation; circulation and vorticity theorems; laminar and turbulent boundary layers; and high-speed flows. Through analytical study and advanced problem solving, students develop the ability to examine complex flow phenomena using rigorous methods.

MMIE 7324. Advanced Heat Transfer.

This course offers specialized knowledge in advanced heat transfer principles and applications. It covers topics such as conduction in complex geometries, convective heat transfer, heat exchanger design, and radiative heat transfer. Emphasis is placed on real-world engineering applications, including thermal management in electronics and renewable energy systems. Students engage in theoretical lectures, practical exercises, and numerical simulations. The course explores cutting-edge research topics in heat transfer and it also enables students to solve complex engineering problems and optimize thermal systems.

MMIE 7326. Advanced Mechanical System Control.

This course covers dynamic modeling, numerical simulation, and advanced control techniques as applied to mechanical systems. Topics include state-space representation, controllability and observability, state and output feedback, state estimation and observers, full-state and reduced order observers, quadratic regulator theory, phase plane analysis, limit cycles, bifurcation and Lyapunov stability theories, feedback linearization, Kalman filtering, and selected techniques for non-linear control design. Students examine the properties of control techniques such as adaptive control, robust control, and model-predictive control to justify their implementation in various unique real-world systems.

MMIE 7330. Advanced Finite Element Analysis.

This course covers variational and weighted residual approaches to the formulation of finite element equations with emphasis on solid mechanics and heat transfer problems in solid materials. Topics include element formulation, numerical integration, imposition of constraints, convergence, error estimation, and an introduction to more advanced topics such as geometric nonlinearities, material nonlinearities, contact problems, and the solution of dynamic problems and time integration. Students are introduced to existing finite element analysis codes that have the capability to solve the different types of problems considered in the topics covered.

MMIE 7332. Computations in Fluid Mechanics and Heat Transfer.

This course covers advanced computational fluid dynamics (CFD) methods for complex fluid-flow problems in engineering and research. Topics include finite volume and finite element methods, along with numerical concepts such as conservation, consistency, and stability. Additional topics include turbulence modeling, Reynolds-averaged models, Large Eddy Simulation, and flows with moving boundaries. Through analytical study and computational modeling, students examine advanced CFD formulations and solution strategies.

MMIE 7340. Advanced Computer Aided Engineering.

This course provides an advanced study of computer-aided engineering (CAE) techniques for product development, focusing on simulation, analysis, and optimization. The scope includes integration of computer-aided design (CAD) with finite element analysis (FEA), computational fluid dynamics (CFD), multiphysics simulations, and engineering calculations. Students learn to model and simulate components, assemblies, and systems to validate performance under operating conditions and optimize design characteristics such as weight, strength, and efficiency. The course is delivered through lectures, software-based exercises, and applied projects.

MMIE 7341. Advanced Micro and Nano Manufacturing.

This course provides an advanced study of micro- and nano-manufacturing processes, techniques, and applications. The scope includes semiconductor manufacturing processes and materials, lithography, oxidation, etching, ion implantation, physical and chemical vapor deposition, atomic layer deposition, chemical mechanical planarization, thin film and surface technologies, microelectromechanical systems (MEMS), and material characterization techniques for micro- and nano-scale fabrication. Students explore process–structure–property relationships and the challenges of precision manufacturing at small scales. The course is delivered through lectures, technical discussions, and applied design exercises.

MMIE 7342. Advanced Polymer Nanocomposites .

This course examines materials, processing methods, characterization techniques, and applications of thermoset and thermoplastic polymer nanocomposites. Topics include engineered nanoparticles and dispersion techniques, manufacturing processes, and detailed morphological, thermal, mechanical, ablative, magnetic, and electrical characterization. Applications such as fire-resistant, fatigue-resistant, impact-resistant, bio-based, electrically conductive, magnetic, and high-temperature composites are discussed. Emphasis is placed on critical analysis of structure–property–processing relationships and research-based evaluation of performance.

MMIE 7362. Time Series Analysis and Forecasting.

This course introduces the theory and application of statistical and machine learning methods for time series analysis and forecasting. Topics include model identification, parameter estimation, diagnostic analysis, and forecasting techniques for stationary and non-stationary univariate and multivariate time series. The course examines Box–Jenkins methods such as ARIMA models along with modern approaches including deep learning techniques for sequential data modeling. Additional concepts such as trend, seasonality, and autocorrelation are analyzed to understand time-dependent data patterns. Data analysis and model implementation are conducted using state-of-the-art software and real-world datasets.

MMIE 7367. Large-Scale Optimization.

This course examines decomposition algorithms for solving large scale mathematical optimization problems encountered in engineering. Topics include scalable problem formulation, database system integration, monitoring optimization performance, and computational implementation using contemporary optimization software. Instruction emphasizes theoretical foundations alongside practical applications through lectures, case studies, and hands on computational exercises using Python, cloud based platforms, and high performance computing environments.

MMIE 7370. Stochastic Simulation.

This course examines simulation modeling techniques and programming methods used to analyze complex systems. Topics include stochastic simulation, probability foundations of simulation models, and the design and analysis of simulation experiments. Programming approaches using general-purpose tools such as VBA for Excel and specialized simulation environments including Simio and @Risk are introduced. Emphasis is placed on model development, simulation optimization, and interpretation of simulation results for decision-making. Applications of simulation modeling are analyzed in areas such as manufacturing systems, financial systems, logistics operations, and service systems to evaluate performance and support operational improvements.

MMIE 7372. Network Flow Optimization.

This course introduces network flow optimization with an emphasis on shortest path, maximum flow, and minimum cost flow problems. Students study mathematical formulations, graph based algorithms, and computational methods for solving structured optimization problems on networks. The course examines engineering applications in transportation, logistics, production, infrastructure, and supply chain systems relevant to mechanical, manufacturing, industrial engineering, and engineering management. Through analytical problem solving and computational implementation, students develop the ability to formulate, solve, and interpret network optimization models. Emphasis is placed on understanding algorithmic properties, computational performance, and the role of network models in supporting complex engineering decision making.

MMIE 7374. Multi-Objective Optimization.

This course examines multi-objective optimization methods and the integration of data science techniques for modeling and solving engineering problems. Topics include problem formulation, algorithm design, performance evaluation, and analysis of trade offs among competing objectives in engineering applications. Instruction includes lectures, case studies, and hands-on computational experiences with Python and contemporary optimization software. Prerequisite: MMIE 7310 or MMIE 7317 with a grade of "B" or better.

MMIE 7379. Modeling and Design of Net-Zero Manufacturing and Service Enterprises.

This course teaches students to design and operate carbon neutral or zero-energy manufacturing, transportation, and service infrastructure through the integration of renewable energy resources. Students use statistics and probability theory, design of experiments, discrete event and agent-based simulation, and stochastic optimization to solve large scale, multi-layer manufacturing and logistics supply chain problems. Through the semester-long team project, students make both strategic and operational decisions on managing production, warehousing, transportation, and microgrid generation in the nexus of manufacturing, energy and climate.

MMIE 7399. Dissertation.

This course provides Ph.D. students with the opportunity to conduct original research in mechanical and manufacturing engineering under the guidance of a dissertation advisor. The scope includes identification of research problems, formulation of hypotheses, experimental or computational investigation, data analysis, and preparation of the dissertation document. Students are expected to apply advanced engineering knowledge, research methodologies, and scholarly practices throughout the research process. The course is delivered through independent research, regular advisor meetings, and progress presentations.

MMIE 7599. Dissertation.

This course provides Ph.D. students with the opportunity to conduct original research in mechanical and manufacturing engineering under the guidance of a dissertation advisor. The scope includes identification of research problems, formulation of hypotheses, experimental or computational investigation, data analysis, and preparation of the dissertation document. Students are expected to apply advanced engineering knowledge, research methodologies, and scholarly practices throughout the research process. The course is delivered through independent research, regular advisor meetings, and progress presentations.

MMIE 7699. Dissertation.

This course provides Ph.D. students with the opportunity to conduct original research in mechanical and manufacturing engineering under the guidance of a dissertation advisor. The scope includes identification of research problems, formulation of hypotheses, experimental or computational investigation, data analysis, and preparation of the dissertation document. Students are expected to apply advanced engineering knowledge, research methodologies, and scholarly practices throughout the research process. The course is delivered through independent research, regular advisor meetings, and progress presentations.

MMIE 7999. Dissertation.

This course provides Ph.D. students with the opportunity to conduct original research in mechanical and manufacturing engineering under the guidance of a dissertation advisor. The scope includes identification of research problems, formulation of hypotheses, experimental or computational investigation, data analysis, and preparation of the dissertation document. Students are expected to apply advanced engineering knowledge, research methodologies, and scholarly practices throughout the research process. The course is delivered through independent research, regular advisor meetings, and progress presentations.