Robust Design

Engineers are taught to create designs that meet customer specifications. When creating these designs, the focus is usually on the nominal values rather than variation. Robustness refers to creating designs that are insensitive to variability in the inputs. Much of the literature on robustness is dedicated to experimental techniques, particularly Taguchi techniques, which advocate using experiments with replications to estimate variation. This course presents mathematical formulas based on derivatives to determine system variation based on input variation and knowledge of the engineering function. If the function is unknown, experimental techniques are presented to efficiently estimate a function.

The concept of designing for both nominal values and variability is expanded to multiple outputs and designing to minimizing costs. Traditionally, if the output variation is too large to meet requirements, the tolerances (variation) of the inputs are reduced. Using the approach presented in this course, the equations presented can be used to identify the contribution of each of the inputs to the output variation. The variation of the components with the largest contribution can be reduced which will reduce output variation. At the same time, the variation of the components contributing the least to the variation of the output can be increased which will reduce costs. A system of equations can be created that will allow an optimization routine to create a design optimized for total cost including the cost of poor quality and component cost.

Participants should bring a laptop computer for in-class exercises.

The book, “Probabilistic Design for Optimization and Robustness for Engineers'  by Bryan Dodson, Patrick Hammett, & Rene Klerx is included in the course materials.

What Will You Learn

By attending this seminar, you will be able to:
  • Create designs that have a minimal sensitivity to input variation
  • Reduce design costs
  • Determine which design parameters have the largest impact on variation
  • Optimize designs with multiple outputs

Is This Course For You

This course is relevant to design and manufacturing engineers, researchers and those interested in cost reduction. This methodology can link manufacturing to engineering design and help design engineering solve manufacturing problems.

Materials Provided

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Course Requirements

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  • Basics of Variation - unique problems facing engineers; small sample sizes and the inability to obtain random samples; techniques for overcoming these problems
  • Distributions
    • Normal, Lognormal, and Weibull
  • Process Capability
    • Measuring process capability
    • Process capability indices
    • Estimating process capability for design inputs
  • Robustness Concept
    • Statistical bias that results from input variation in a non-linear system
    • Modeling output variation
    • Circuit exercise
    • Projectile exercises
  • Simulation
    • Determining the variability of the inputs
    • Random number generators
    • Verification & validation
    • Simulation modeling
  • Minimizing the Variance of a Single Output
    • Polynomial exercise
  • Identifying Critical Parameters
    • Ranking the contribution to the output variation
    • Identifying parameters that are constrained
    • Pipe flow exercise
  • How to Model and Optimize Multiple Outputs
    • Combustion exercise
  • Adding Cost to the Design Model
    • Minimizing the total system cost including component, scrap and process costs
    • Electronics exercise