你将学到什么
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
课程概况
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 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.
预备知识
Manufacturing Process Control I is required unless there is a strong prior knowledge of statistical methods and SPC.
常见问题
For more information, please see the POM FAQ Page.
