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Manufacturing Process Control II
Learn advanced methods for controlling process variation, designing experiments, and optimizing manufacturing processes in this MIT course.
Learn advanced methods for controlling process variation, designing experiments, and optimizing manufacturing processes in this MIT course.
This advanced manufacturing course, part of MIT's MicroMasters program, focuses on process modeling and optimization. Students learn sophisticated methods for controlling process variation and designing experiments to improve manufacturing quality. The curriculum covers multivariate regression, design of experiments (DOE), response surface methods, and techniques for creating robust processes. Through practical applications and a capstone activity, students develop statistical abilities for solving engineering problems. The course emphasizes creating optimal, robust processes with high-quality outputs. It builds on statistical process control foundations to provide comprehensive process improvement methodologies.
Instructors:
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What you'll learn
Master multivariate regression techniques for analyzing input-output relationships
Implement design of experiments methods to improve manufacturing processes
Apply response surface methods for process optimization
Develop robust processes that minimize sensitivity to external variations
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This advanced manufacturing course builds upon statistical process control foundations to teach sophisticated methods for process modeling and optimization. The curriculum covers essential topics including multivariate regression for analyzing input-output relationships, design of experiments (DOE) methodologies for process improvement, response surface methods for optimization, and techniques for creating robust processes resistant to external variations. Students learn to integrate these methods into comprehensive process control strategies and develop strong statistical problem-solving abilities for engineering applications. The course culminates in a capstone activity that synthesizes all Statistical Process Control topics.
Fee Structure
Instructors
9 Courses
Manufacturing Innovation Pioneer and Educational Leader at MIT
David Hardt has shaped manufacturing education and research at MIT for over four decades since joining the faculty in 1979 as the Ralph E. and Eloise F. Cross Professor of Mechanical Engineering. After earning his BSME from Lafayette College in 1972 and his SM and Ph.D. from MIT in 1978, he pioneered groundbreaking work in manufacturing process control and automation. His research spans multiple areas, from developing multivariable control techniques for gas metal arc welding to creating flexible tooling systems for aerospace applications. More recently, he has focused on polymer micro-embossing and large-scale additive manufacturing using recycled materials for low-cost housing. As Director of the MIT Laboratory for Manufacturing (1985-1992) and Engineering Co-Director of the Leaders for Manufacturing Program (1993-1998), he has significantly influenced manufacturing education. He led the development of MIT's first professional Master of Engineering in Manufacturing degree and the MITx MicroMasters Program in Principles of Manufacturing, which has awarded over 3,400 certificates. His international impact includes chairing the Singapore-MIT Alliance Program in Manufacturing Systems and Technology (1999-2014) and serving on the MIT Commission on Productivity in an Innovation Economy. His current research focuses on novel equipment design, process statistical control, and sustainable manufacturing solutions, while continuing to shape the future of manufacturing education through innovative programs and teaching methods.
8 Courses
Leader in Semiconductor Manufacturing and International Education
Duane Boning has established himself as a pioneering figure in semiconductor manufacturing and educational leadership at MIT, where he currently serves as Vice Provost for International Activities (effective September 2024) and the Clarence J. LeBel Professor in Electrical Engineering and Computer Science. After completing all his degrees at MIT (S.B. in EE and CS in 1984, S.M. in 1986, and Ph.D. in 1991), he briefly worked at Texas Instruments before joining the MIT faculty in 1992. His research focuses on machine learning and statistical methods for modeling semiconductor and photonic manufacturing processes, with over 300 publications to his credit. Throughout his career at MIT, he has held numerous leadership positions, including Associate Head of EECS (2004-2011), Director of the MIT Skoltech Initiative (2011-2013), and Faculty Director of the MIT/Masdar Institute Cooperative Program (2011-2018). As Engineering Faculty Co-Director of the MIT Leaders for Global Operations program since 2016, he has led the formation of MIT's Machine Intelligence for Manufacturing & Operations initiative. His contributions to semiconductor manufacturing have earned him IEEE Fellowship, while his expertise spans statistical modeling, process control, and variation analysis in IC, photonics, and MEMS processes. Currently, as Vice Provost for International Activities, he oversees MIT's global engagements while continuing his research in semiconductor manufacturing and design.
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