Classic methodologies of DOE are widely applied in design, manufacture, quality
management and related fields. The resulting data can be analysed with linear modeling
methods such as multiple regression which generates a set of equations, Y = F(X), that
enable us to understand how varying the mean of one or more inputs changes the mean of one
of more responses. To develop, scale-up and transfer robust processes to manufacturing we
also need to set the control tolerances of each critical X and understand the extent
to which variation in the critical X’s propagate through to variation in the
Y’s and how
this may impact performance relative to requirements (or specifications). Visual tolerance
analysis provides a simple way to understand and reduce propagation of variation from
X’s to
Y’s using
models developed from DOE’s or historical data. This paper briefly introduces the concept
of tolerance analysis and extents this to visual tolerance analysis through defect
profiles and defect parametric profiles. With the help of visual tolerance analysis,
engineering and statistical analysts can work together to find the key factors responsible
for propagating undesired variation into responses and how to reduce these effects to
deliver a robust and cost effective process. A case study approach is used to aid
explanation and understanding.