More and more companies choose to work in depth with tolerance analysis to obtain a more robust design. Tolerance analyzes is a good way to detect any complications in the product during product development and with this knowledge you can optimize the design before production begins.
This module helps from a data-driven perspective to develop new products and processes. You will be able to predict variation in a product or process – seen as tolerance chains. This is done by establishing relationships between the different input parameters and outputs, and it is thus easier to predict the variation in volume production.
Tolerance Analysis is a prerequisite for robust design. With tolerance analysis and a robust design you achieve getting a handle on the variation, increase the ramp-up time, reduce scrap rates and increase quality. In other words, you can design a set-up where performance and functionality remain unaffected by the built-in variation.
This course differs in particular from the classical tolerance chain analysis by working with “process tolerances”, where the process is not assumed to be centered in the window of specification.
Target group for Robust design
This module is intended primarily for employees and specialists working in R&D, product development and design.
What will you learn?
In this course you will learn to
- Work with tolerance chains in terms of the worst case, statistical-, and process tolerance chains
- Calculate a theoretical failure rate for the output of a tolerance chain using capability indices on inputs
- Identify critical input parameters (red X’s)
- Make simple optimizations on inputs to reduce the error rate on output
Content in bullets
- Types of tolerances
- Sign convention for tolerance chains
- Introduction to VarTran software
- Calculating the worst-case (tolerance stack up), statistical and process-tolerance chains
- Capability and defining requirements for the input parameters
You should be aware that the course requires that you have participated in Intro to statistics and SAS JMP / Minitab, if you have not previously worked with the statistical programs SAS JMP and Minitab.
Do you want stronger statistical competencies in design and development, then you may want to take Robust design with tolerance analysis 2, which builds on the course content. If you have not already taken Data Driven Root Cause Analysis or Design of Experiments (DOE) 2 this course will also give you a strong profile.
Do you want a stronger methodological understanding you may want to take one or more of these courses, Design for Six Sigma (DfSS), Quality Function Deployment (QFD) and Risk Management in Development Projects.