Key Rational Selection Concepts
CES Selector provides tools that support a uniqiue rational selection methodology developed by Professor Mike Ashby and colleagues at the University of Cambridge. The method is the industry-standard approach to systematic materials selection. Below are some of its key concepts. The CES Selector software makes it fast and easy to apply these concepts to practical problems—and training is also available to help you get up-to-speed quickly.
For more information, see the book: "Materials Selection in Mechanical Design" (4th edition) by M.F. Ashby, Butterworth Heinemann, 2011.
The Ashby approach is design-led. It starts by asking
- 'What is the function of the component in the design?'
- 'What objectives need to be optimized?'
- 'What constraints must be satisfied?'
For instance, a car body panel (function) needs to be as light as possible (objective) for a specified stiffness and cost (constraint). Other constraints on the design might be acceptable resistance to mechanical impact and to contact with various environments.
The advantage of this approach is that it is systematic and unbiased in its focus on product objectives.
Have you ever entered choices on the web for a product, such as a vacation package or a car—or, for that matter, a material—only to get back the unhelpful message: "Sorry, nothing meets your criteria. Please try again"? Ashby's selection charts avoid this problem and do much more besides. They convey information about your options in pictures and quickly answer questions like "Why isn't the material I first thought of the best?"
Materials selection charts plot one property against another. Every material in the dataset that you are studying is represented as a point or (more typically) as an ellipse showing the range of its possible values. The first thing that the chart provides is a quick, visual indication of the relative position for all of the materials being considered. This can be sufficient to focus on likely candidates, and also helps to develop an intuitive feel for the relative performance of materials. But the chart's real power is that it provides a graphical environment in which to apply and analyze quantitative selection criteria, such as those captured in performance indices, and also to make trade-offs between conflicting objectives.
Material performance for a specific application is rarely governed by the individual properties found in handbooks or manufacturers datasheets, but by combinations of two or more of these properties. These combinations, derived through mathematical analysis of the engineering problem, are called performance indices. Finding a material with a high value of the index maximizes the performance of the component.
This often leads to surprising results. For instance, many would think the best material for a lightweight panel such as in a car body is given by specific stiffness, modulus/density (E/r). In fact, it is given by E(1/3)/r—a result that has significant implications for material choice. 50% glass-filled polymer is the stiffest material by weight, but, surprisingly, 30-35% of filler delivers the lightest panel for a given stiffness. (See chart, above).
With CES Selector, you can quickly identify the appropriate index by selecting from a set of standard engineering problems (see image, below)—or, for more advanced use, define your own index. Then apply this index in your selection study.
Trade-offs are endemic in both everyday life and materials selection. Perhaps the most common and obvious is the trade-off between performance and cost. Selection charts, with their ability to show materials performance relative to a combination of properties in a simple (yet quantitative) manner, are ideal vehicles for analyzing trade-offs and for then communicating them within an organization.
Such data is usually only available in the form of picture graphs, meaning it cannot be used in a quantitative selection process. Instead, the user must refer to the graphs individually, manually interpolating them for the relevant conditions. This can become very time consuming.
In model-based selection, such as that available within CES Selector, the curve is stored as a function within the materials property database so that the property value at any temperature can be extracted. Selection charts can thus be constructed for any conditions—the image below shows how a plot of density v modulus, and thus the selection decisions that it could drive, changes with temperature.