Evaluating X Ray Beam Hardening Corrections in Dense Optical Polymers
Resin-specific polynomial linearization suppresses computed tomography cupping artifacts in dense optical polymers while preserving inline cycle times.
Polynomial Linearization
Meaning
Mathematical correction techniques apply non-linear equations to sensor data to remove distortions and align the output with a linear scale. The use of polynomial linearization is particularly effective in correcting beam hardening artifacts in X-ray systems where different materials attenuate energy unevenly. This process ensures that the greyscale values in the resulting image are proportional to the actual density of the scanned object.
Without this correction, the edges of a dense part might appear darker than the center, even if the material is uniform. This phenomenon can hide defects or create false alarms that disrupt the flow of a production line.
Distortion Correction
Raw data from a digital detector often contains non-linear responses caused by the physics of the X-ray source and the detector material. Applying polynomial linearization transforms these raw counts into a set of values that accurately represent the physical properties of the part. This step is a prerequisite for any automated defect recognition software that relies on consistent intensity levels to find cracks or voids.
Mathematical Precision
Calibrating the correction requires a series of scans of a material with a known thickness and composition to determine the coefficients of the equation. Once these coefficients are set, polynomial linearization can be applied during the reconstruction process without adding a large time penalty. The accuracy of this model directly impacts the ability of the system to distinguish between small differences in material density.
Data Alignment
Integrating these corrections into the production workflow allows for a higher yield by reducing the number of false rejects caused by image artifacts. As a part moves from a prototype scan to a high-volume run, polynomial linearization provides the stability needed to maintain a constant inspection standard. This mathematical foundation is what allows industrial ct to be used for quantitative analysis rather than just qualitative inspection.