What is an S-N Curve?

The S-N curve, also known as the Wöhler curve, is a fundamental tool in materials science and structural engineering used to define the relationship between cyclic stress amplitude (S) and the number of cycles to failure (N). Fatigue occurs when a material is subjected to repeated loading and unloading. Even if the applied stress is well below the material's ultimate tensile strength, these cycles can cause microscopic cracks that eventually lead to catastrophic failure.

Understanding the Parameters

Our S-N Curve Calculator utilizes the Basquin Power Law equation, which is the most common mathematical representation of the S-N relationship in the high-cycle fatigue regime. The equation is typically expressed as: σ_a = σ'_f (2N_f)^b.

• Fatigue Strength Coefficient (σ'_f): This is approximately equal to the true fracture stress of the material.
• Fatigue Strength Exponent (b): Also known as Basquin's exponent, it usually ranges between -0.05 and -0.12 for most metals.
• Applied Stress (σ_a): The constant amplitude stress level the component is experiencing.
• Cycles to Failure (N): The resulting number of cycles the material can withstand before failing.

How to Use the Calculator

To estimate the fatigue life of your component, input the material properties (usually found in material handbooks or via testing) and the operational stress levels. If the applied stress is lower than the "Endurance Limit," the material may theoretically have an infinite life, provided there is no corrosion or high-temperature degradation.

Frequently Asked Questions

What is the difference between high-cycle and low-cycle fatigue?

High-cycle fatigue (HCF) typically involves stresses in the elastic range and lasts for more than 10,000 cycles. Low-cycle fatigue (LCF) involves plastic deformation and usually results in failure in fewer than 10,000 cycles.

Why is the S-N curve plotted on a log-log scale?

Because the range of cycles to failure can span several orders of magnitude (from 10^3 to 10^8), a logarithmic scale linearizes the power-law relationship, making it much easier to visualize and interpolate data.