Learn how to control process variation, including methods to design experiments that capture process behavior and understand means to control variability. 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.
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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 andmanagement 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.
This course is part of the Principles of Manufacturing MicroMasters 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
Manufacturing Process Control I is required unless there is a strong prior knowledge of statistical methods and SPC.
