Manufacturing Process Control I
Manufacturing Process Control I
Course Information
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About this Course
Randomness is inherent in all processes including manufacturing. The fundamental concepts taught in this course will help learners develop powerful statistical process control methods that are the foundation of world-class manufacturing quality.
As part of the Principles of Manufacturing MicroMasters program, this course will introduce statistical methods that apply to any unit manufacturing process. We will cover the following topics:
- Recognizing inherent variability in continuous production
- Identifying sources of process output variation
- Describing variation in a structured manner
- Applying basic probability and statistics concepts to characterize process variation
- Differentiating between design specifications and process capability
- Synthesizing novel approaches to unfamiliar situations by extending the core material (i.e. go beyond the “standard” uses).
- Assessing the appropriateness of various statistical methods for a variety of problems
Develop the engineering and management skills needed for competence and competitiveness in today’s manufacturing industry with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-ranked Mechanical Engineering department in the world. Learners who pass the 8 courses in the program will earn the MicroMasters Credential and qualify to apply to gain credit towards MIT’s Master of Engineering in Advanced Manufacturing & Design program.
What you'll learn
- Variation modeling using the theory of Random Processes
- Statistical Process Control (SPC) foundations and applications
- Xbar, EWMA, CUSUM and discrete event methods for detecting process problems
- Methods for analyzing process changes by looking at general process physics
- How to apply these methods to achieve world-class quality in unit manufacturing processes
Prerequisites
Engineering Undergraduate preparation; some knowledge of basic manufacturing processes. Knowledge or probability theory is helpful but not necessary.
Meet your instructors
David Hardt
Ralph E. and Evelyn F. Cross Professor of Mechanical Engineering
Professor Hardt is a graduate of Lafayette College (BSME, 1972) and MIT (SM, PhD, 1978). He has been a member of the Mechanical Engineering faculty at MIT since 1979. His disciplinary focus is system dynamics and control as applied to manufacturing. His research has been on flexible automation, and process control, with an historical emphasis on welding and forming processes, and a current focus on polymer micro embossing. In welding, he pioneered the use of multivariable control techniques for modeling and control of GMAW, and demonstrated the use of adaptive control in these systems. In the forming processes, he concentrated on the use of in-process measurements and real-time modeling to reduce sensitivity to machine and material variations, and has developed a flexible tooling and closed loop shape control that has implemented in the aerospace industry with specific uses for repair part manufacture. His more recent work has been in the hot micro-embossing process for micro-fluidic device manufacture and scale-up of soft lithography methods using roll to roll processes. In both cases the theme of the work is novel equipment design and overall equipment and process statistical control Prof. Hardt has taught classes in both Mechanical Engineering and Manufacturing, and has led the creation of a new graduate degree: Master of Engineering in Manufacturing" at MIT. This is the first professional degree offered by the ME Department at MIT, and is the culmination of many years of course and curriculum development. Prof. Hardt served as Director of the MIT Laboratory for Manufacturing from 1985 - 1992 and as Engineering Co-Director for the MIT Leaders for Manufacturing Program from 1993 to 1998. From 1999 to 2011 he was the MIT Chair of the Singapore MIT Alliance (SMA) Program: "Manufacturing Systems and Technology", a research and teaching collaboration with Nanyang Technological University in Singapore. He was a member of the MIT Commission on Productivity in an Innovation Economy, and served on the Workforce team on the Advanced Manufacturing Partnership program (AMP 1.0).
