Department of Industrial and Manufacturing Engineering
FAMU—FSU College of Engineering
Web Page: http://www.eng.fsu.edu/ime/
Chair: Okenwa Okoli; Professors: Awoniyi, Braswell (Emeritus), Liang, Okoli; Associate Professors: Liu, Vanli, Zeng, Zhang; Assistant Professors: Dickens, Park, Shrivastava, Wang, Yu; Adjunct Professors: Devine, Olawale
The Department of Industrial and Manufacturing Engineering offers two graduate degree programs: Master of Science (MS) and Doctor of Philosophy (PhD). Industrial Engineering is a broad discipline that encompasses education and basic/applied research concerning the design, improvement, and installation of integrated systems of people, material, information, equipment and energy. Graduate instruction and research are broadly grouped into three categories: manufacturing engineering, quality engineering, and industrial systems. Current research interests include manufacturing processes, and systems; statistical; quality control; failure and life cycle analysis; mathematical optimization of complex production systems; condition monitoring; reliability engineering; statistical machine learning; distributed sensor networks; manufacturing process monitoring and diagnosis; set-covering theory; simulation environments; polymeric materials; nanomaterials processing and applications; additive thin-film manufacturing; printed electronics; carbon nanotube based functional materials; advanced composites and multi-scale materials; simulation for material processing; composite material processing.
The Department of Industrial and Manufacturing Engineering provides an excellent environment for instruction and research. The department has the following laboratories housed in the College of Engineering: Materials and Product Property Characterization, Computer Integrated Manufacturing, Precision Manufacturing, and Quality Engineering. In addition, the students have access to the 44,000 sq. ft. state-of-the-art labs at Florida State University’s High-Performance Materials Institute (see http://hpmi.research.fsu.edu/), which houses the laboratories Mechanical Testing Lab; Chemical and Thermal Analysis; Additive Manufacturing Lab; Manufacturing Lab, Characterization Lab.
Each laboratory in the Department is equipped with state-of-the-art research and instructional equipment. Some of the available equipment in the labs are: 3D printers, Laser Scanner (Additive Manufacturing Lab), MTS Insight Testing System, MTS Landmark Servohydraulic System (Mechanical Testing Lab), Differential Scanning Calorimeter, Thermomechanical Analyzer (Chemical and Thermal Analysis), Ultrasonic Bath Sonicator, Scanning Electron Microscope (Materials and Product Property Characterization Lab) Autoclave, Laser Cutting Machine, Temperature-Humidity Test Chamber (Manufacturing Lab).
Students have access to computer facilities, which includes both, IBM-compatible PC’s and high performance engineering workstations. The Department offers access to a wide variety of software for CAD/CAM optimization, simulation and statistical analysis, including Matlab, Minitab, Design Expert, R, Arena and Simio computing environments. The statistical and simulation software and computing facilities are located in the Quality Engineering Lab, the solid modeling and CAD/CAM software are located in the Computer Integrated Manufacturing Lab. Technical support for software and hardware maintenance are provided by the Department and the College. In addition, the students have access to the Florida State University High Performance Computing (HPC) Cluster for intensive distributed-memory parallel computations.
Master of Science (MS)
The department offers a variety of Master of Science in Industrial Engineering (MSIE) program options to accommodate students’ needs and specializations. Students may pursue a traditional MS or an MS with specialization in engineering management. The traditional MS program is research based, requiring the students to write and defend a thesis in their chosen area. However, the specialization in engineering management does not require a thesis. The Industrial Engineering Graduate Handbook, which is available from the department, provides a complete description of all programs and requirements.
Candidates for admission to graduate study in industrial engineering must meet university and departmental criteria. In some cases, students may be admitted on a provisional basis pending successful completion of prerequisite work. In all matters concerning admission, decisions made by the departmental graduate committee are final. Students who do not have a bachelor’s degree in industrial engineering are required to complete the following prerequisite courses before undertaking graduate study:
EGN 3443 Statistical Topics in Industrial Engineering (3)
MAC 2313 Calculus with Analytic Geometry III (5)
MAS 3105 Applied Linear Algebra (4)
equivalent course as determined by the graduate committee.
ESI 3312C Operations Research I: Deterministic (3)
ESI 4313 Operations Research II: Nondeterministic (3)
equivalent course as determined by the graduate committee
a class in FORTRAN, PASCAL, or C (required as evidence of proficiency in programming).
Admission Requirements for Traditional MSIE
- A BS in industrial engineering (or a related field) from an accredited college or university, with a GPA of at least 3.0
- Minimum scores of at least 650 (151 in the new scale) on the quantitative portion and 400 (147 in the new scale) verbal portion of
- A minimum score of 80 (iBT) on the TOEFL or a minimum of 6.0 on the IELTS (international students only)
- Three letters of recommendation, addressed to the Director of Graduate Studies, assessing the applicant’s potential to do graduate work
- A statement of professional goals
Admission Requirements for MSIE with Specialization in Engineering Management
Requirements for admission to this program are identical to the MSIE admission requirements, except that applicants’ BS degree can be in engineering, computer science, mathematics, physics, or a related area as determined by the Director of Graduate Studies.
Each MSIE student who intends to complete a thesis is required to take a minimum of thirty semester hours (twenty-four semester hours of course work and six semester hours of thesis). At least eighteen semester hours of the course work hours must be taken in the Industrial and Manufacturing Engineering Department. Students must maintain an overall GPA of 3.0 or above in order to graduate.
When filing a degree plan, students must specify one of the department’s areas of concentration as their major: manufacturing systems and engineering, or quality engineering and industrial systems. If the desired area of concentration differs from the initial area assigned (based on the student’s graduate application), a petition to the Director of Graduate Studies must be submitted requesting the change.
There are three sets of courses under the traditional MSIE program: core course, specialization industrial engineering courses and electives:
Core Courses. Every student choosing the thesis option must take the following courses and receive a grade of “B” or better in each: ESI 5408, Applied Optimization; ESI 5247, Engineering Experiments; ESI 5525, Modeling and Analysis of Manufacturing and Industrial Systems; and EIN 5936, Graduate Seminar.
Specialization Courses. These courses are used in defining minimum requirements for each specialization area. Each student is required to take at least three from those courses listed in his or her chosen area of specialization. Substitutions may be made with the approval of the student’s advisory committee and the Director of Graduate Studies. Please refer to the departmental Web site at http://www.ie.eng.fsu.edu.
Electives. Elective courses provide program variation for students. An industrial engineering graduate course may be selected as an elective course. With the consent of the advisory committee, the student may take courses from other engineering departments, or other academic schools or colleges of the two universities.
Under exceptional circumstances, students may be allowed into the MSIE non-theses option. In such cases, students are required to complete a minimum of thirty-three semester hours of course work at the graduate level, at least twenty-four of which must be taken in the Department of Industrial Engineering. Each student must obtain an overall GPA of 3.0 or above in order to graduate. Students should contact the department to learn more about specific course requirements for this program.
Specialization in Engineering Management
Students are expected to complete thirty-three semester hours of course work, and will not complete a thesis. Industrial Engineering Core courses constitute eighteen credit hours, Management core courses constitute three credit hours, and the elective courses constitute twelve credit hours. At least six credit hours of the electives must be taken from the College of Engineering. Students must maintain a minimum GPA of 3.0 at all times while enrolled in the program in order to graduate. Students should contact the department to learn more about specific course requirements for this program.
Specialization in Engineering Management of Orthotics and Prosthetics
This is a specialization in Master of Science Program offered jointly by FAMU-FSU College of Engineering and St. Petersburg College. Through this program St. Petersburg College students earning a Bachelor of Applied Science in Orthotics and Prosthetics can then apply and matriculate to FSU to earn a Master of Science in Industrial Engineering, with a specialization in Engineering Management of Orthotics and Prosthetics. This is a course based program and students will not complete a thesis. Students will be required to complete thirty-two credit hours consisting of coursework and clinical rotation. Courses are delivered online in a synchronous format and require students to have some contact with the St. Petersburg College campus. Students should contact the IME department to learn more about specific course requirements for this program.
Doctor of Philosophy (PhD)
The PhD in industrial engineering is designed for students and professionals who wish to pursue academic careers or to achieve advanced standing in the field. The general requirement is a minimum of forty-five semester hours of work beyond the baccalaureate degree, excluding any credits earned for a master’s degree thesis, or a minimum of thirty-three semester hours beyond the master’s degree.
Typically, twelve of the forty-five semester hours will have been satisfied by a student who has earned a master’s degree in industrial engineering, or a closely related field. Of the remaining required hours, nine must be letter-graded course work combined with a minimum of twenty-four additional hours of dissertation research. The course work beyond the master’s consists of: 1) eighteen semester hours of breadth-requirement core courses, and 2) up to six or more semester hours of depth-requirement courses, as determined by the student’s doctoral supervisory committee. Residency and time-for-completion requirements are determined by the student’s university of enrollment. Students must maintain a minimum GPA of 3.4 at all times while enrolled in the program. Doctoral candidates must meet the department publication requirements before the viva voce of their dissertation.
Note: The following standards also pertain to students who wish to pursue a PhD but have not yet obtained their master’s degree.
Applicants must meet the following minimum requirements:
- Have a baccalaureate or master’s degree in industrial engineering (or related field) from an accredited college or university, with a grade point average (GPA) of at least 3.0 on a 4.0 scale, and at least 3.4 GPA on master’s degree work
- Have a minimum score of 700 (155 in the new scale) on the Quantitative portion and 450 (150 in the new scale) on the Verbal portion of the GRE
- Have a minimum score of 80 (580 paper based) on the TOEFL iBT or a minimum of 6.0 on the IELTS (international students only)
- Three letters of recommendation, addressed to the Director of Graduate Studies, assessing the applicant’s potential to do graduate work
- A statement of professional goals
Core Courses for PhD Students
All PhD students are required to take the following courses as soon as possible after their admission to the PhD program. These courses provide students with a common, solid background in mathematics, statistics, and industrial engineering.
During the first calendar year of the PhD program, students must select a single course from each of the Mathematics and Computational course groups, and must earn a grade of “B” or higher. Students who do not satisfy this requirement may be dismissed from the program.
Mathematics Course Group
MAA 5306 Advanced Calculus I (3)
MAP 5345 Elementary Partial Differential Equations I (3)
STA 5323 Introduction to Mathematical Statistics (3)
Computational Course Group
EIN 5930r Specialized Topics in Industrial Engineering (1–6)
MAD 5403 Foundations of Computational Methods I (3)
MAP 5395 Finite Element Methods (3)
EIN 5930 Special Topics in Industrial Engineering (1–6)
Note: The required topic is “Finite Elements Methods” for three (3) credit hours.
STA 5106 Computational Methods in Statistics I (3)
IE Core Course Group
The following courses are required if the student did not take them to fulfill requirements for the master’s degree: ESI 5247 Engineering Experiments; ESI 5408 Applied Optimization; ESI 5525 Modeling and Analysis of Manufacturing and Industrial Systems; EIN 5020 Research Methodology; and EIN 5936 Graduate Seminar.
Core courses cannot be taken on a pass/fail (S/U) basis.
Following completion of a major portion of the coursework as defined in the degree plan, and upon certification of the doctoral supervisory committee that the student has 1) maintained a minimum 3.4 GPA and 2) progressed sufficiently in the study of industrial engineering and its research tools to begin independent research in the area of the proposed dissertation, the student is ready to take the preliminary examination.
The purpose of the preliminary examination is to test the adequacy of a student’s background related to the student’s area of concentration, and to determine if the student is adequately prepared to formulate and undertake acceptable dissertation research. The procedures are available from the department.
Proposal and Dissertation
After completion of the preliminary examination, the student is admitted to formal candidacy for the PhD. After a period of preliminary research as determined by the doctoral committee, a research proposal must be successfully presented to the committee by the doctoral candidate.
The research proposal is a description of the research which the student intends to undertake and which will be reported in a detailed, comprehensive fashion in the completed dissertation. The research proposal must be submitted to the supervisory committee after the student passes the preliminary exam (usually one year after the preliminary exam) and before beginning dissertation research. The student must also provide an oral presentation to the committee at least one week after submitting the proposal. The proposal offers the student an opportunity to convince the supervisory committee of the appropriateness of the research topic, as well as of his/her capability to pursue the projected topic to a successful conclusion.
Subject to approval of the doctoral candidate’s committee confirming the candidate’s readiness to defend his/her dissertation, and upon meeting the department publication requirements, the candidate may proceed to defend their dissertation research. A doctoral dissertation then must be completed on a topic approved by the candidate’s doctoral supervisory committee. To be acceptable, it must be an achievement in original research constituting a significant contribution to knowledge and represent a substantial scholarly effort on the part of the student. The doctoral supervisory committee, department chairperson, and such other members of the faculty as appointed by the academic dean or specified by university regulations will conduct the examination. Publication of the dissertation shall conform to the regulations of the university in which the student is registered.
During the dissertation defense, all committee members and the student must be physically present. In cases where this is not possible, the department allows no more than one member to participate in the defense in real time via distance technology. The distance technology must allow two-way audio and visual links.
Definition of Prefixes
EIN 5020. Research Methodology (3). This course provides a structured and easily understandable step-by-step approach for students to learn the key components that compromise a sound research process.
EIN 5182. Engineering Management (3). Prerequisite: EIN 5353. Course in modeling existing and future organizations, with emphasis on organizations for the 21st century. Special consideration is given to flat matrix models.
EIN 5328. Environmentally Conscious Design and Manufacturing (3). Prerequisite: Graduate standing. This course offers a review of basic concepts and fundamentals of environmentally conscious design and manufacturing. The topics include ecology and environment; review of environmental laws and regulations pertaining to design and manufacturing; the global picture of environmental concerns; integration of environmentally conscious design and manufacturing within a company; and life-cycle analysis for product and process design.
EIN 5353. Engineering Economic Analysis (3). Prerequisites: EGN 3443 and MAP 3305. This course includes feasibility science, mathematics and engineering focused on the engineering economic analysis of design and system alternatives for high technology operations.
EIN 5392. Manufacturing Processes and Systems (3). Prerequisite: EGN 4000. Material forming, material removal and material joining processes. Shop floor layout topics. Material flow topics. Information system topics. System integration topics. Manufacturing system evaluation topics. Case studies and design exercises.
EIN 5398. Manufacturing Materials Processing (3). Prerequisite: EIN 5392. Review of basic concepts and fundamental results of materials science. Fundamentals of casting processes and applications. Nontraditional methods in materials processing. Microscale material processing, with applications to microelectronics and similar structures. Industrial byproduct processing. Automation issues. Case studies and design exercises.
EIN 5445C. Technology Entrepreneurship and Commercialization (3). This course simulates, in an academic environment, the process of creating and analyzing business models and commercialization plans for technology-based products or services.
EIN 5459. Concurrent Engineering (3). Prerequisite: Graduate or senior standing with instructor permission. Concurrent product and process design. Product life cycle attributes. Design for manufacturing. Quality function deployment. Concurrent engineering project management topics. Case studies and design exercises.
EIN 5524. System Modeling and Simulation (3). Prerequisites: CGS 3460, EGN 3443, and ESI 3443. Discrete event, continuous, and process simulation. Combined discrete/continuous simulation. Manufacturing systems modeling. Event graphs. Simulation languages and systems. Experimentation with models. Introduction to simulation-specific statistical problems. Model validation and verification issues. Design exercises.
EIN 5622. Computer-Aided Manufacturing (3). Prerequisite: EIN 3390C. CAD/CAM. Numerical Control (NC) and Computer Numerical Control (CNC). Programmable automation. Computer-aided process planning.
EIN 5623. Computer-Aided Process Planning (3). Prerequisites: CGS 3408, EGN 2123, EIN 3390C, and EIN 4312. Course covers the role of process planning and computer-aided process planning (CAPP), development of CAPP, configuration of CAPP systems, input approaches of CAPP systems, process routing planning, machining operations design, variant CAPP systems, generative CAPP systems and artificial intelligence in CAPP.
EIN 5905r. Directed Individual Study (1–3). (S/U grade only). Prerequisite: Instructor permission. May be repeated to a maximum of six semester hours.
EIN 5930r. Special Topics in Industrial Engineering (3–6). Prerequisite: Instructor permission. This course discusses topics in industrial engineering with particular emphasis on recent developments. May be repeated to a maximum of six semester hours.
EIN 5931. Leadership and Communications (3). Prerequisites: Graduate standing and EGN 3613. Course topics include leadership theories, motivation, goal setting, planning, proposal writing and technical presentations. Presentations given by business leaders are planned.
EIN 5936r. Graduate Seminar (0). (S/U grade only). Research presentations by faculty, students, and guests from industry.
EIN 6901r. Master’s Thesis (1–6). (S/U grade only). Prerequisite: Approval by department. Each master’s thesis shall be supervised by a master’s degree supervisory committee. Completed master’s thesis shall be presented to the department in the form of a written report and a seminar. May be repeated to a maximum of nine semester hours.
EIN 8976r. Master’s Thesis Defense (0). (P/F grade only.)
EMA 5015C. Nanomaterials and Nanotechnology (3). This course is designed to provide students the basic understanding and up-to-date knowledge on nanostructured materials, characterization methods, nano-devices, and nano-fabrication through class lectures, literature reading, and hands-on lab experiments.
EMA 5182. Composite Materials Engineering (3). Prerequisite: Instructor permission. Course provides basic understanding of composite materials. Topics include introduction to composite materials, properties and forms of constituent materials, consideration of composite behavior and failure modes, characterization of material performance and testing, introduction to available manufacturing techniques, laboratory demonstrations, and case studies.
ESI 5223. Statistical Process Control (3). Prerequisite: ESI 4234. Advanced methods of statistical process control for univariate and multivariate processes, methods for change point detection and estimation, control chart performance comparisons, process capability studies.
ESI 5228. Introduction to ISO 9000 (3). Prerequisite: Instructor permission. Introduction to the ISO 9000 quality system standards. Quality auditing. Audit report writing. Documenting the requirements. Case studies and demonstrations.
ESI 5243. Engineering Data Analysis (3). Prerequisite: EGN 3443 or equivalent. Analysis of experimental and observational data from engineering systems. Course focuses on empirical model building using observational data for characterization, estimation, inference and prediction.
ESI 5247. Engineering Experiments (3). Prerequisites: EGN 3443 and ESI 5243. This course provides an introduction to designing experiments and analyzing the results. It is intended for engineers and scientists who perform experiments or serve as advisors to experimentation in industrial settings. Students must have an understanding of basic statistical concepts. A statistical approach to designing and analyzing experiments is provided as a means to efficiently study and comprehend the underlying process being evaluated. Insight is gained that leads to improved performance and quality.
ESI 5249. Response Surfaces and Process Optimization (3). Prerequisite: ESI 5247. This course explores combined statistical experiment designs, empirical model building, and optimization methods. Topics include restrictions on randomization, mixture experiments, and robust design. Emphasis is placed on software tools to build designs and perform appropriate analyses.
ESI 5408. Applied Optimization (3). Prerequisite: ESI 3312C. Optimization topics relevant to industrial operations and systems. Emphasis on basic modeling assumptions and procedure implementation. Topics shall include linear programming, nonlinear programming, discrete optimization and large-scale optimization software. Design exercises.
ESI 5451. Project Analysis and Design (3). Prerequisites: EGN 3613 and ESI 3312C. Project analysis and evaluation, utilizing networks and graph theory, advanced engineering economy, simulation procedures and other evaluation software. Project implementation topics, including resource shortfalls and expediting. Case studies and design exercises.
ESI 5458. Optimization on Networks (3). Prerequisite: ESI 3312C. Review of basic combinatorics. Basic concepts of graph theory. Matching and covering, and applications. Traversability and path problems on networks and applications. Tree problems. Network flows and applications. Eulerian paths, Hamiltonian paths, and applications. Location problems on networks. Design exercises.
ESI 5524. Advanced Simulation Applications (3). Prerequisite: ESI 4523 or EIN 5524. Application of simulation to complex systems, including material handling systems, real time scheduling, high speed/high volume production, modern manufacturing techniques, health-care delivery and logistics. Concurrent use of simulation and other analysis techniques. Use of experimental design, output analysis and validation techniques. Case studies.
ESI 5525. Modeling and Analysis of Manufacturing and Industrial Systems (3). Prerequisites: EIN 4333, ESI 3312C, ESI 4523, ESI 5408, and ESI 5524. Modeling and analysis of material flow systems, flow-shop and job-shop scheduling, material handling system analysis, mathematical and simulation modeling for general manufacturing and industrial systems.
PRO 5132. Advanced Materials in Prosthetics and Orthotics (3). Prerequisite: EMA 5182. This course focuses on design and manufacturing issues pertaining to the cost effective production of prosthetic devices using advanced composite materials. The course considers service elements as comfort, aesthetics, customization, durability, ease of use, costs, and service life, and their manufacturability with a materials engineering perspective. The course strongly emphasizes the hands-on experience and is class and lab based. The lab component of this class is denoted as PRO 5132L-Advanced Materials in Prosthetics and Orthotics Lab (2).
PRO 5132L. Advanced Materials in Prosthetics and Orthotics Lab (2). This course provides students with a basic knowledge of the prescription and fabrication of various prosthetic and orthotic devices using specified medical composite materials. The prerequisite of the lab is PRO 5132-Advanced Materials in Prosthetics and Orthotics. The lab is designed to encourage students to apply the principles of practical material science to biomechanics, case study, and actual clinical practice.
PRO 5803. Orthotics and Prosthetics Clinical Rotation (3). This course combines traditional classroom meetings on-site with clinical rotation hours off-site, in which students observe, assist, and practice patient care and device fabrication in an environment that prepares them for an orthotic or prosthetic residency. Students study traditional and emerging topics related to the O&P field and are then placed at a clinical agency to practice skills under close supervision of an American Board Certified (ABC) prosthetist/orthotist.
EIN 6629. Tolerancing and Metrology for Precision Manufacturing (3). Prerequisites: EIN 5398, 5408. Theory and applications of tolerancing techniques in precision machining. Topics include tolerance representation, tolerance stack-up, tolerance analysis and synthesis, statistical tolerancing, coordinate measuring machines, form error evaluation algorithms, and advanced topics in form error assessment. Case studies and design exercises.
EIN 6980r. Dissertation (3–24). (S/U grade only). Prerequisite: Doctoral candidate standing. Mandatory class for all PhD seeking students. May be repeated to a maximum of forty-eight semester hours.
EIN 8964. Preliminary Doctoral Examination (0). (P/F grade only.) Prerequisite: Doctoral candidate standing.
EIN 8985r. Dissertation Defense (0). (P/F grade only.) Prerequisite: Doctoral candidate standing.