A working vocabulary for mapping LMD failure modes to process signals, AI visibility, inspection methods, and validation evidence.
Preliminary decision-support only. Final feasibility depends on base material, geometry, service conditions, inspection requirements, and expert review.
What can go wrong physically.
What monitoring or logs might show.
What AI may responsibly indicate.
What inspection or testing must prove.
Atlas
Possible process signals: Melt-pool instability, shielding disturbance, feed inconsistency, unusual brightness or plume behavior.
AI visibility: May flag anomaly clusters or signal drift, but cannot prove pore size, position, or acceptance.
Validation evidence: CT, suitable NDT, metallography, density evidence, or test coupons depending on risk.
Decision action: Treat AI as inspection prioritization, not release proof.
Possible process signals: Low energy input, poor overlap, surface contamination, path or standoff inconsistency.
AI visibility: Can detect parameter/signal combinations associated with risk when trained against validation data.
Validation evidence: CT, metallography, destructive cross-section, or qualified NDT method.
Decision action: Require stronger evidence for load-bearing or fatigue-sensitive use.
Possible process signals: Material mismatch, rapid thermal cycles, high residual stress, heat-sensitive base material.
AI visibility: May flag thermal histories or acoustic/visual patterns, but should not decide acceptability alone.
Validation evidence: PT, MT where applicable, metallography, hardness, residual-stress-aware review.
Decision action: Escalate material compatibility and preheat/postheat review.
Possible process signals: High heat input, slow travel, bead geometry change, unexpected melt-pool size.
AI visibility: Can track process windows and bead-shape indicators if linked to chemistry or cross-sections.
Validation evidence: Metallography, chemistry, hardness profile, deposit/base interface review.
Decision action: Review process route when functional surface properties matter.
Possible process signals: Heat accumulation, thin geometry, poor fixturing, long deposition time, sensitive tolerances.
AI visibility: Can forecast risk from geometry, heat input, and prior comparable jobs.
Validation evidence: Dimensional inspection, scan-to-CAD comparison, fixture and machining review.
Decision action: Plan machining allowance and in-process checks early.
Possible process signals: Track height variation, spatter, feed interruption, poor overlap, local access constraints.
AI visibility: Strong candidate for visual/process signal detection and triage.
Validation evidence: Visual inspection, 3D scan, dimensional inspection, machining allowance review.
Decision action: Separate cosmetic/process observations from final tolerance evidence.
Possible process signals: Wrong feedstock, unknown base material, heat treatment gap, dilution or hardness shift.
AI visibility: Can identify missing traceability and incompatible requirements, not certify properties.
Validation evidence: Material certificates, hardness testing, metallography, chemistry, mechanical testing.
Decision action: Stop firm recommendations if material grade or feedstock traceability is unknown.
How to use it
The atlas is strongest when paired with the LMD Quality Evidence Ladder and RFQ schema. It helps name the risk and request the right evidence before a claim becomes too confident.
Framework path
