Manufacturing Process Control II
Learn how to control process variation, including methods to design experiments that capture process behavior and understand means to control variability.
Course Information
Certificate Track
Learn for Free
About this Course
As part of the Principles of Manufacturing MicroMasters program, this course will build on statistical process control foundations to add process modeling and optimization.Building on formal methods of designed experiments, the course develops highly applicable methods for creating robust processes with optimal quality.
We will cover the following topics:
- Evaluating the causality of inputs and parameters on the output measures
- Designing experiments for the purpose of process improvement
- Methods for optimizing processes and achieving robustness to noise inputs
- How to integrate all of these methods into an overall approach to process control that can be widely applied
- Developing a data-based statistical ability to solving engineering problems in general
- The course will conclude with a capstone activity that will integrate all the Statistical Process Control topics.
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 earn the MicroMasters Credential and qualify to apply to gain credit for MIT’s Master of Engineering in Advanced Manufacturing & Design program
What you'll learn
- Multivariate regression for Input-output causality
- Design of experiments (DOE) methods to improve processes
- Response surface methods and process optimization based on DOE methods
- DOE-based methods for achieving processes that are robust to external variations
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).
