Meaning
Bi-directional data exchange between volumetric computed tomography scans and structural simulation models measures physical void geometry directly against theoretical stress distributions. Incorporating finite element defect mapping transforms CT scan voxel arrays into computational mesh models to evaluate actual part performance under load. The method governs spatial mesh generation around internal voids, local stress concentration factors, and yield predictions in structural thermoplastic components.
Its application stops at mechanical load simulation, omitting non-structural cosmetic evaluation and chemical resin characterization.
Structural Linkage
Computational mechanics tools mapping physical scan data onto stress solvers enable precise structural life prediction for critical components. Utilizing finite element defect mapping lets stress analysts isolate the exact load threshold where internal void clusters initiate matrix cracking.
Mesh Boundary
Standard mechanical testing assumes homogenous resin properties, ignoring internal sink marks and vacuum voids present in thick-walled structural moldings. Using finite element defect mapping bridges this gap by transferring voxel density gradients directly into localized material stiffness matrices within the finite element solver. High stress concentration zones around irregular shrinkage voids in long-fiber reinforced polyamides frequently cause premature part failure under fatigue loading.
Simulated failure locations align closely with actual test fractures when scan resolution captures true pore notch radii. Datasheet yield values overestimate load capacity when internal pore distributions are omitted from numerical simulations.
Stress Localization
Unmapped internal voids act as unrecorded notch points that reduce component fatigue life under cyclic mechanical loading. Incorporating finite element defect mapping into signoff protocols prevents field failures by identifying critical defect clusters before tooling signoff.