Plot 30 material families on log-log axes. Switch X/Y axes freely to explore stiffness-weight, strength-weight, and fracture toughness trade-offs with performance index guidelines.
Stiff lightweight beam: $M = E^{1/2}/\rho$
Strong lightweight beam: $M = \sigma_y^{2/3}/\rho$
Stiff lightweight tie: $M = E/\rho$
Materials above-left of a guideline line outperform those below-right.
The core idea is to derive a Performance Index (M) that isolates the material properties for a given design goal (like minimum mass). For a lightweight beam in bending, we minimize mass while meeting a stiffness constraint.
$$ M = \frac{E^{1/2}}{\rho}$$Here, $E$ is Young's Modulus (stiffness), and $\rho$ is density. A higher $M$ means a lighter beam for the same stiffness. On a log-log plot of $E$ vs. $\rho$, lines of constant $M$ have a slope of 2, because $\log E = 2 \log \rho + \text{constant}$.
For a different loading case, like a lightweight tie (strut) in tension, the objective changes. Here, we minimize mass while meeting a strength (yield stress) constraint, leading to a different index.
$$ M = \frac{\sigma_y^{2/3}}{\rho}$$Here, $\sigma_y$ is the yield strength. For a stiff tie, where deflection is limited, the index becomes $M = E/\rho$, producing a slope of 1 on the Ashby chart. The slope of the performance line is the key visual filter for selection.
Aerospace Structural Frames: Airframe ribs and spars are beams in bending. Engineers use the $E^{1/2}/\rho$ index on Ashby charts to select materials like aluminum alloys, titanium, or carbon fiber composites to minimize weight while maintaining rigidity, directly impacting fuel efficiency.
High-Performance Bicycle Frames: The main triangle of a bike frame acts primarily as a beam. Material selection charts help designers choose between advanced aluminum, titanium, or carbon fiber, balancing the stiffness-to-weight ratio (governed by $E^{1/2}/\rho$) against cost and manufacturability.
Tension Members in Bridges & Cranes: Cables and tie rods are pure tension members. For these, the performance index is $E/\rho$ for stiffness or $\sigma_y/\rho$ for strength. This is why steel cables are common, but charts show where high-strength polymers or composites could offer weight savings.
Automotive Control Arms: Suspension components like control arms experience complex loads but often have bending-dominated behavior. Using the strong beam index ($\sigma_y^{2/3}/\rho$), engineers compare forged aluminum to high-strength steels to reduce unsprung mass, improving vehicle handling and efficiency.
When you start using this chart, there are a few common pitfalls to watch out for. First, don't judge a material solely by its position on the plot. The chart is primarily a first-pass screening tool. For example, while CFRP appears clearly superior to aluminum alloys on the lightweight stiff beam index, in actual design, factors not shown on the chart—like cost, manufacturability, corrosion resistance, and reliability data—are often decisive. That's why aluminum is frequently chosen.
Next, beware of pitfalls in axis selection. Even when we say "strength," there are different types: tensile strength, yield strength, fatigue strength, etc. For static loads, yield strength might be fine, but for components under cyclic loading, plotting with fatigue strength data is essential. When selecting "Strength" in this tool, be clear about the specific failure mode you are dealing with. Also, remember that the data represents typical values, and real materials have variability.
Finally, understand the meaning of the performance index slopes. The material ranking can reverse between a slope of 2 and a slope of 1.5. For instance, the index for a "lightweight stiff plate" is $M = E^{1/3} / \rho$, resulting in a line with a slope of 3. The optimal material changes between a beam and a plate. Therefore, clearly identify from the start whether your design is a "beam," a "plate," or a "tie," and use the corresponding guideline line.
The concepts behind this visualizer are applied in many fields beyond CAE. First, it pairs exceptionally well with Topology Optimization. After topology optimization calculates a "lightweight, high-stiffness shape," the next step is deciding which material to manufacture it with. At this stage, judging the force flow inherent to the optimal shape (whether it behaves like a beam or a plate) and selecting materials using the corresponding performance index ensures design consistency.
Another is Sustainable Engineering and LCA (Life Cycle Assessment). Nowadays, environmental impact is a crucial selection criterion alongside performance. As an extension of this tool, creating charts with axes like "CO2 emission intensity" or "recyclability" could visualize the trade-off between environmental and mechanical performance. For example, comparing aluminum and steel on a "strength vs. manufacturing CO2" plot enables a completely different discussion.
Furthermore, it can be considered a gateway to Materials Informatics. Research is advancing to explore new materials located in specific performance regions on this plot or to use machine learning to predict "what elemental composition would fill this blank region." Your simple click on a material bubble on the plot can be the first step towards data-driven materials development.
If you become interested in the principles behind this tool, I recommend next following the derivation process of the performance indices yourself. Opening a textbook is the quickest way, but the core idea is "formulating design requirements mathematically." For a lightweight stiff beam, the goal is to "minimize weight while maintaining constant bending stiffness." Beam bending stiffness is $EI/L^3$ (E: Young's modulus, I: second moment of area), and weight is $\rho A L$ (A: cross-sectional area). By fixing the shape (the relationship between I and A) and rearranging into a function of only material properties (E and ρ), the index $E^{1/2}/\rho$ emerges naturally. Understanding this "rearrangement" process will enable you to create your own new performance indices.
A practical next step is to use this tool to narrow candidates down to 2-3 types, then verify them using more detailed material databases. For instance, even if a polymer looks promising on the chart, you need to dig deeper: how much does its strength drop within the actual service temperature range (glass transition temperature)? What is its creep data like? The tool is a "map"; actually checking the "terrain" is a separate task.
Mathematically, understand why performance indices appear as straight lines on a log-log plot from the properties of logarithmic calculation. Taking the logarithm of both sides of an index-form equation like $M = E^{a} / \rho^{b}$ gives $\log M = a \log E - b \log \rho$. Setting M constant yields a linear equation in $\log E$ and $\log \rho$—the equation of a straight line. The values of "a" and "b" determine that line's slope. Getting comfortable with this transformation should change how you see the chart, from mere "shapes" to "meaningful equations."