2026 ASEE Annual Conference & Exposition

Preliminary Testing of an Educational Assembly Cell Designed to Study Flexible Manufacturing Operations

Presented at DSAI-Session 14: Research Methods, Collaboration Networks, and Learning Analytics

Manufacturing systems increasingly demand scheduling methods that are both efficient and resilient to variability in task times, rework, routing order, and resource availability. Most existing Manufacturing Execution Systems (MES) and related manufacturing teaching approaches are limited to static routings, offering little opportunity to allow routing sequence flexibility as learning and production evolve. This work-in-progress study involves preliminary testing of an educational assembly cell designed to allow students to investigate flexibility in manufacturing of an Arduino-based robot. This work is motivated particularly by the assumption that exploring task relationships using Allen temporal relations will allow operators to better understand allowable variation, thereby improving overall process efficiency. Preliminary work successfully configured the assembly cell, work instructions, alternate routings, process logging, and measurement techniques. A proposed Assembly Sequence Difference Index (ASDI) was introduced to measure overall deviation relative to a baseline routing. Initial testing designed to assess the cell and metrics yielded an experimental dataset comprising 10 pilot trials conducted by the researcher and a single undergraduate student trial. Initial results show promise that the education assembly cell and the ASDI metric are useful for learning insights about manufacturing flexibility. Results showed that assembly tasks can be successfully reordered. Several alternative routing trials achieved shorter cycle times than the baseline. Further, precedence deviation appears higher in robot disassembly than assembly. ASDI of disassembly is higher at the beginning of trials when the operator is new to the system than it is in assembly; however, as the trial progresses the ASDI between the two operations becomes similar. The preliminary station was limited to simple sequence changes (before, after, mets, etc). In addition to capturing trials for more student operators, ongoing work involves development of a wiring harness assembly cell and a quality testing station that allow study of more complicated temporal relations (overlaps, during, starts, etc).

Authors

Seyi Oluwadare Clarkson University [biography]

Seyi Oluwadare is a doctoral student at Clarkson University in Potsdam, NY. He has a dual master's degree in mechanical engineering, with one focus on thermofluids and the other in computational modeling. His current research focuses on integrating Allen’s Interval Algebra and temporal reasoning analytics to model sequence dependencies in both manufacturing operations and curriculum structures, with the broader goal is to advance intelligent systems that enhance efficiency, flexibility, and equity across engineering and educational domains.
Dr. Carl David Hoover Clarkson University [biography]

Dr. Carl D. Hoover is Associate Professor and Director of Laboratories in the Department of Mechanical and Aerospace Engineering at Clarkson University where he teaches engineering courses and researches data science applications. Dr. Hoover's MAE lab courses Measurement & Instrumentation, Experimental Methods, and Test Engineering are at the heart of Clarkson's hands-on engineering education. Dr. Hoover oversees several engineering research facilities in the Center for Advanced Materials Processing (CAMP) building at Clarkson including our wind tunnel facility and several mechanical testing labs. Carl leads the Academic Test Equipment Asset Management & Safety (A-TEAMS) committee which he founded. Working with industry partners, Dr. Hoover co-developed Solinsky Challenge manufacturing-focused microcredential courses in Statistical Process Control, Basic Statistics for Manufacturing, Design for Manufacturing and Assembly, and Manufacturing Assembly Methods. Carl brings over 15 years of industry experience to the classroom with experience ranging from aerospace test engineering to executive leadership. Prior to academia, Carl served as CTO of Elder Research, where he now consults as a Senior Advisor. Carl has managed R&D, led a world-class team of scientists, and served on several DoD projects. Dr. Hoover previously taught NGA College's Program of Study in Data Science graduate courses through Mizzou. Classes he's taught include Introduction to Data Science, Data Science for Managers, Data Visualization, Statistical & Mathematical Foundations of Data Analytics, and Data Analytics from Applied Machine Learning. Dr. Hoover's research interests are in machine learning and testing applications in engineering, manufacturing and measurement systems. Current projects include temporal relational dependence models in STEM and manufacturing digital twins.

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