Electron Backscatter Diffraction

Signal Origin

Diffraction occurs at a steep tilt inside the chamber where the beam hits the sample and spreads outward in a cone shaped projection. In a session using electron backscatter diffraction, a detector captures these patterns known as Kikuchi lines and translates them into orientation data via specialized software. The intersection of these lines reveals the symmetry of the phase under the probe and identifies whether the spot is a carbide or a steel matrix.

This identification happens at thousands of points per minute to build a statistical view of the entire sample. Precise sample preparation is necessary because even a small layer of oxidation will block the signal coming from the lattice.

Data Utility

Mapping results show where groups of atoms share common directions and where they collide at sharp interfaces between grains. Using electron backscatter diffraction allows metallurgists to quantify the amount of deformation or stress stored in the sample after a forging or extrusion step. It also identifies the presence of preferred orientations known as texture that can cause a material to be stronger in one direction than another.

This knowledge is essential for predicting how a sheet will behave during stamping or how a wire will stretch during drawing.

Application Range

Microstructure optimization in high speed steel production relies heavily on the detailed reports generated by these scans. When testing a new alloy, electron backscatter diffraction confirms that the grain size is within specification and that secondary phases are located correctly relative to the matrix. It differentiates between specific carbides that might look identical under traditional light microscopes but behave differently under load.

Quality control laboratories use these patterns to confirm the effectiveness of heat treatment cycles. Standard maps help researchers document the evolution of the structure from the initial casting to the final machined tool. Accuracy in orientation data supports the creation of more predictable material performance models.