Doctor of Philosophy (Ph.D.) Major in Mechanical and Manufacturing Engineering (Entering with Bachelor's Degree)
The Doctor of Philosophy degree with a major in Mechanical and Manufacturing Engineering (MME) program is student-focused, multi-disciplinary, and collaborative. Applicants with a B.S. or M.S. degree in Mechanical Engineering, Manufacturing Engineering, Industrial Engineering, or closely related fields, are the main audience for this program.
Educational Objectives
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Application of disruptive technologies in the fields of mechanical and manufacturing engineering (MME).
Although these new skillsets are in high demand by industry and government sectors, they are rarely covered in a holistic way in conventional graduate programs in mechanical, manufacturing, or industrial engineering programs. By providing series of prescribed electives and depth courses in the curriculum, students will get an applied exposure to these technologies.
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Depth in the conventional MME fields.
The core courses and a set of prescribed electives will give required depth skills in conventional topics to students while elective courses provide cross-disciplinary and breadth exposure to other fields. These courses will also familiarize students with literature in the discipline.
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Development of professional skills to pursue career paths in academia, industry, or start-up companies dealing with or developing advanced or emerging technologies.
Courses related to commercialization and entrepreneurship and an elective course provide choices to students to enhance their professional skills and improve their employability in their desired career path.
Application Requirements
Application requirements consist of institutional and program requirements for applicable semesters of entry during the current academic year. Additional information and changes to admission requirements for semesters other than the current academic year can be found on The Graduate College's website.
Unless otherwise noted on The Graduate College program page, AI tools can only be used to correct spelling and grammar errors in application materials.
Institutional Requirements
Institutional requirements are the minimum standards for admission to any graduate program at Texas State. These include:
- Completed online application
- Nonrefundable application fee
- Degree Programs (Doctoral and Master’s)
- $55 fee, or
- $90 for applications with international credentials
- Post-Baccalaureate Programs (Certificate, Certification, Non-Degree, and Visiting)
- $20 fee, or
- $60 for applications with international credentials
- Degree Programs (Doctoral and Master’s)
- Official transcripts from each institution where course credit was granted. Final transcripts showing degree completion are required before the student may register for their second term of enrollment.
- GPA requirements (a higher GPA may be listed in the Program Requirements)
- Doctoral programs require a 3.00 overall GPA or a 3.00 GPA in your last 60 hours of undergraduate course work (plus any completed graduate courses).
- Master’s and Specialist programs require a 2.75 overall GPA or a 2.75 GPA in your last 60 hours of undergraduate course work (plus any completed graduate courses).
- Post-Baccalaureate programs require a 2.50 overall GPA or a 2.50 GPA in your last 60 hours of undergraduate course work (plus any completed graduate courses).
- Baccalaureate degree from a regionally accredited university. (Non-U.S. degrees must be equivalent to a four-year U.S. Bachelor’s degree. In most cases, three-year degrees are not considered. Visit our International FAQs for more information.)
Approved English Proficiency Exam Scores
Applicants are required to submit an approved English proficiency exam score that meets the minimum requirements below unless they have earned a bachelor’s degree or higher from a regionally accredited U.S. institution or the equivalent from a country on our exempt countries list. Some programs may restrict acceptable tests or require higher scores than the institutional scores; this will be noted in the Program Requirements.
- official TOEFL iBT scores required with a 78 overall if taken on or before January 21, 2026
- official TOEFL iBT scores required with a 4 overall if taken after January 21, 2026
- official PTE scores required with a 52 overall
- official IELTS (academic) scores required with a 6.5 overall and minimum individual module scores of 6.0
- official Duolingo scores required with a 110 overall
- official TOEFL Essentials scores required with an 8.5 overall
- official Texas State Intensive English Program score of 90% or higher in the highest-level course (level 5)
The institution does not offer admission if the scores above are not met.
Program Requirements
- Exceptional applicants with a bachelor’s degree in mechanical engineering, manufacturing engineering, industrial engineering, or a closely related discipline, from a regionally accredited university, will be considered for admission. These exceptional applicants will be required to complete an additional 24 semester credit hours of mechanical, manufacturing, or industrial engineering master’s level courses when admitted.
- A minimum cumulative GPA of 3.5 on a 4.0 scale in all completed undergraduate coursework.
- Official GRE General Test scores are required. Competitive scores are expected.
- Resume/CV outlining education, work experience, scholarships/grants, publications/presentations, and other accomplishments. This should provide evidence of research potential, such as:
- Undergraduate research projects
- Internships with a research component
- Publications or presentations
- Statement of purpose outlining the applicant’s personal history and goals that are relevant to obtaining this doctoral degree and explaining why the applicant wants to pursue this degree at TXST
- Three letters of recommendation evaluating the applicant's skill and potential for the degree program
- Interviews may be conducted with semifinalists as part of the admission process.
Additional Information
A committee that includes the doctoral program director will conduct a holistic review of all applications. Students will be assessed for readiness to enroll in our doctoral program based on their background in mechanical engineering, manufacturing engineering, or industrial engineering, interest in our program and faculty research, and potential for research.
The program will admit full-time and part-time students one time per year.
Degree Requirements
The Doctor of Philosophy (Ph.D.) degree with a major in Mechanical and Manufacturing Engineering requires 79 semester credit hours.
Course Requirements
| Code | Title | Hours |
|---|---|---|
| Required Courses | ||
| MMIE 7305 | Advanced Design of Experiments | 3 |
| MMIE 7310 | Machine Learning and Artificial Intelligence for Engineers | 3 |
| MMIE 7340 | Advanced Computer Aided Engineering | 3 |
| MMIE 7100 | MMIE PhD Seminar | 1 |
| Prescribed Electives | ||
| Entering with a Bachelor's | ||
| Choose 24 hours from the following or other courses with the approval of the program coordinator: 1 | 24 | |
| Probability, Random Variables, & Stochastic Processes for Engineers | ||
| Advanced Statistical Design of Experiments for Engineers | ||
| Modeling and Analysis of Manufacturing Systems | ||
| Advanced Quality Control and Reliability Engineering | ||
| Applied Deterministic Operations Research for Engineers | ||
| Non-Linear Optimization Techniques for Engineers | ||
| Advanced Optimization | ||
| Advanced Heuristic Optimization | ||
| System Thinking and Analysis | ||
IE 5398A | ||
IE 5398B | ||
IE 5398C | ||
| Continuum Mechanics | ||
| Mechanics of Composite Materials | ||
| Energy and Thermofluids Engineering | ||
| Advanced Computer Aided Design and Manufacturing | ||
| Additive Manufacturing | ||
| Polymer Nanocomposites | ||
| Advanced Robotics in Manufacturing Automation | ||
| Semiconductor Manufacturing | ||
MFGE 5398B | ||
| Disruptive Technologies | ||
| Choose a 6 hours from the following: | 6 | |
| Cyber-Physical Systems Architecture | ||
| Digital Twins | ||
| Advanced Robotics | ||
| Human-Robot Interaction | ||
| Advanced Additive Manufacturing | ||
| Cybersecurity for Mechanical and Manufacturing Systems | ||
| Applied Data Science I | ||
| Applied Data Science II | ||
| Commercialization and Entrepreneurship | ||
| Choose 6 hours from the following: | 6 | |
MGT 7314 | ||
| Marketing Management | ||
| Practical Skills in Commercialization and Entrepreneurship | ||
| Leadership Skills in Commercialization and Entrepreneurship | ||
| Principles of Technical Project Management | ||
| Domain (Depth) | ||
| Choose 6 hours from the following: | 6 | |
| Cyber-Physical Systems Architecture | ||
| Digital Twins | ||
| Advanced Robotics | ||
| Human-Robot Interaction | ||
| Advanced Additive Manufacturing | ||
| Cybersecurity for Mechanical and Manufacturing Systems | ||
| Applied Data Science I | ||
| Applied Data Science II | ||
| Advanced Solid Mechanics | ||
| Advanced Fluid Mechanics | ||
| Advanced Heat Transfer | ||
| Advanced Mechanical System Control | ||
| Advanced Finite Element Analysis | ||
| Computations in Fluid Mechanics and Heat Transfer | ||
| Advanced Micro and Nano Manufacturing | ||
| Advanced Polymer Nanocomposites | ||
| Time Series Analysis and Forecasting | ||
| Large-Scale Optimization | ||
| Stochastic Simulation | ||
| Network Flow Optimization | ||
| Multi-Objective Optimization | ||
| Modeling and Design of Net-Zero Manufacturing and Service Enterprises | ||
| Choose 3 hours from the following: | 3 | |
BIO 7360Y | ||
| Statistics and Experimental Design I | ||
| Statistics and Experimental Design II | ||
| Beginning Quantitative Research Design and Analysis | ||
| Intermediate Quantitative Research Design and Analysis | ||
| Intermediate Qualitative Design and Analysis | ||
| Advanced Data Mining | ||
| Advanced Machine Learning and Pattern Recognition | ||
| Bioinformatics | ||
| Seminar in Quantitative Research | ||
| Specializations in Professional and Technical Communication Topics | ||
MATH 7325 | ||
MATH 7335 | ||
| Nanoscale Systems and Devices | ||
| Nanoscale Systems and Devices | ||
| Materials Characterization | ||
| Nanocomposites | ||
| Biomaterials and Biosensors | ||
| Fluid Flow in Porous Media | ||
| Nanomaterials Processing | ||
| Advanced Polymer Science | ||
MSEC 7395C | ||
| Polymer Characterization and Processing | ||
MSEC 7395I | ||
MSEC 7395L | ||
| Semiconductor Devices and Processing | ||
| Dissertation | ||
| Choose a minimum of 24 hours from the following: | 24 | |
| Dissertation | ||
| Dissertation | ||
| Dissertation | ||
| Dissertation | ||
| Dissertation | ||
| Dissertation | ||
| Total Hours | 79 | |
- 1
Other ENGR, CE, EE, MSEC courses maybe be taken to satisfy this requirement. Please contact your advisor for approval.
Candidacy Criteria
Students will advance to candidacy after they have completed all required and elective coursework (except for dissertation credit hours), passed their comprehensive exam, and successfully defended their dissertation proposal. Students are expected to complete their dissertation proposal by the end of year 2 if starting from an M.S. degree or by the end of year 3 if starting from a B.S. degree. Appropriate adjustments are made if students are part-time students. Once all requirements are met, the doctoral program director will forward the Application for Advancement to Candidacy form to the Dean of The Graduate College for review and approval.
Comprehensive Exam
Each doctoral student must pass a comprehensive examination. This should be done by the time the student has completed 37 semester credit hours if starting from an M.S. degree or 61 semester credit hours if starting from a B.S. degree and can only be done after identifying the dissertation committee and completing all required courses.
The comprehensive exam will be a written take-home exam. The dissertation committee will provide the student with a list of topics for the comprehensive exam. The topics in the list will be based on graduate courses that the student took at Texas State University. The exam will have four questions and the student will have 24 hours to complete the exam. Members of the dissertation committee will grade the exam questions. The answer to each question will be graded as satisfactory or unsatisfactory. To pass the exam, the student must receive a satisfactory grade in all the exam questions. Any student who does not pass the comprehensive exam by the time 45 semester credit hours have been accrued if starting from an M.S. degree or 69 semester credit hours have been accrued if starting from a B.S. degree will be dismissed from the program. If any section(s) of the comprehensive exam is not passed, the student will have the option of retaking the section(s) they failed a second and final time in the following long semester. Students will be dismissed from the program if they do not pass all sections of the comprehensive exam the second time.
Dissertation Proposal and Proposal Defense
Each Ph.D. student must prepare a written dissertation proposal and defend it orally. This should be done by the time the student has completed 37 semester credit hours if starting from an M.S. degree or 61 semester credit hours if starting from a B.S. degree and after identifying the dissertation committee and completing all required courses. Any student who does not defend his/her dissertation proposal by the time 45 semester credit hours have been accrued if starting from an M.S. degree or 69 semester credit hours have been accrued if starting from a B.S. degree will be dismissed from the program. If the proposal defense is not passed, the student will have the option of taking a second and final proposal defense in the following long semester. Students will be dismissed from the program if they do not pass the proposal defense the second time.
The dissertation proposal must outline the substance and scope of the planned dissertation research and explain its merits. It must include at least an introduction, the methodology to be used, a survey of the relevant literature, and preliminary results that demonstrate the feasibility. The goal of the proposal is to establish that the student has a sufficient grasp of the fundamentals of the chosen dissertation topic to execute the research.
The proposal defense entails a public presentation of the student’s dissertation proposal followed immediately by a closed defense of the proposal attended only by the student and his/her dissertation committee. The dissertation proposal must be approved by the student’s dissertation advisor and a majority of the remaining members on the dissertation committee. The student’s dissertation committee members must indicate their approvals on the doctoral Dissertation Proposal Form as well as on the Defense of Dissertation Proposal Form. These forms are available on The Graduate College’s website.
A final copy of the dissertation proposal, accompanied by the signed approval forms, must be turned in to the doctoral program director, who will forward them to the Dean of The Graduate College for review and final approval.
Candidacy and Dissertation
When all requirements for admission to candidacy have been met, the doctoral program director forwards the Application for Advancement to Candidacy to the Dean of The Graduate College for review and approval. This application form is available on The Graduate College’s website.
A minimum GPA of 3.0 on all coursework undertaken in the doctoral program is required for admission to candidacy. Grades below a “B” on any graduate coursework cannot be applied toward the doctoral degree. Incomplete grades must have been cleared before approval for advancement to candidacy can be granted. No more than six semester credit hours of dissertation research can be taken before advancing to candidacy. No credit will be applied toward a student’s doctoral degree for coursework completed more than five years before the date on which the student is admitted to candidacy if starting from an M.S. degree or more than seven years if starting from a B.S. degree. Appropriate time adjustments are made for part-time students. This time limit applies to course credit earned at Texas State as well as course credit transferred to Texas State from other institutions.
All doctoral students must complete a dissertation that consists of original research and demonstrates mature scholarship and critical judgment in addition to familiarity with tools and methods in the chosen area. The dissertation project must adhere to the dissertation proposal and cover the topic approved by the student’s dissertation committee.
After being admitted to candidacy, students must be continuously enrolled for dissertation hours each fall and spring semester until the defense of their dissertation. At least 24 semester credit hours of dissertation research must be taken after having advanced to candidacy. If a student is receiving supervision on a dissertation during the summer or if the student is graduating in the summer, the student must be enrolled in dissertation hours for the summer. All candidates for graduation must be enrolled in dissertation hours during the semester in which the degree is to be conferred, even if they have already satisfied the minimum dissertation hours.
Dissertation Committee
The initial dissertation committee chair assignment, and its continuation, is subject to the approval of both parties. A dissertation committee chair can be changed with the approval of a student’s assigned dissertation committee chair, a student’s new dissertation committee chair, and the doctoral program director. If a dissertation committee chair withdraws mentorship, the student must secure a new dissertation committee chair within one long semester to stay on track in the program. Failure to do so will result in dismissal from the program.
The Dissertation Committee will be responsible for administering the Comprehensive Exam and the Dissertation Proposal Defense and will oversee the research and writing of the student’s dissertation. The committee will consist of 5 members, including the student’s dissertation committee chair who must be a regular graduate faculty member in the program, three other graduate faculty members from the Ingram School of Engineering (note that the majority of faculty members must come from the program), and one graduate faculty from another department at Texas State University or from another university, or a Ph.D. holder in industry or a government agency. The student’s dissertation committee chair will chair the committee. The student, the dissertation committee chair, and the Dean of The Graduate College will approve the composition of the dissertation committee.
As per The Graduate College policy, the Dissertation Committee Chair Assignment form and the Dissertation Committee Request form must be completed and approved by the Dean of The Graduate College to form the dissertation committee. Any changes to the dissertation committee must be submitted using the Dissertation Committee Chair/Committee Member Change Request form for approval of the dissertation committee chair, the doctoral program director, and the Dean of The Graduate College. Committee changes must be submitted no later than 60 days before the dissertation defense.
Dissertation Defense
Once the dissertation has been completed, a final exam (referred to as the dissertation defense) on the dissertation must be conducted. The dissertation defense cannot be scheduled until all other academic and program requirements have been fulfilled. A complete draft of the dissertation must be given to the members of the dissertation committee at least one month before the defense. However, students are highly encouraged to provide drafts earlier so that the committee members can provide feedback, which the student, in consultation with the dissertation advisor, will address in later drafts to ensure that the dissertation is defendable, and each committee member is satisfied before the dissertation defense takes place.
The dissertation defense consists of two parts. The first part is a public presentation of their dissertation research. The second part of the defense immediately follows the public presentation. It is restricted to participation of the student’s dissertation committee and entails an oral examination of the dissertation research. Approval of the dissertation requires positive votes from the student’s dissertation advisor and from the majority of the remaining members of the dissertation committee. Notice of the defense presentation will be publicly posted at least two weeks in advance.
If the dissertation defense is not approved, the student will have the option of taking a second and final dissertation defense in the following long semester. Students who do not pass the dissertation defense the second time will be dismissed from the program.
The results of the dissertation defense must be recorded in the Dissertation Defense Report Form and submitted to The Graduate College before the Dean of The Graduate College can give final approval of the dissertation. This form can be downloaded from The Graduate College’s website. The student must submit his/her dissertation to The Graduate College for final approval. The guidelines for submission and approval of the dissertation can be obtained from The Graduate College.
Students must pass the dissertation defense by the time 30 semester credit hours of dissertation have been accrued. The doctoral program will review each student annually to ascertain his/her progress towards the degree and will consult the student’s dissertation advisor and dissertation committee on this matter as needed.
Doctoral level courses in Mechanical and Manufacturing Engineering: MMIE
Courses Offered
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.
1 Credit Hour. 1 Lecture Contact Hour. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Standard Letter
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.
1 Credit Hour. 1 Lecture Contact Hour. 0 Lab Contact Hours.Grade Mode: Credit/No Credit
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.
2 Credit Hours. 2 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 1 Lab Contact Hour.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
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.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
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.
5 Credit Hours. 5 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
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.
6 Credit Hours. 6 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
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.
9 Credit Hours. 9 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
