Working framework Interactive score checker

LMD-AI Readiness Score

Is an LMD workflow ready for useful AI-assisted monitoring?

Useful AI in Laser Metal Deposition depends on traceable process data, inspection outcomes, labels, repeated examples, and feedback loops. The score below makes those foundations visible before model talk starts.

Framework brief

Use readiness as a data-foundation check

Preliminary decision-support only. Final feasibility depends on base material, geometry, service conditions, inspection requirements, and expert review.

Definition

A preliminary score for whether an LMD workflow has enough connected data for useful AI-assisted monitoring.

Problem it solves

AI projects often start before process data, inspection outcomes, labels, and feedback loops are linked.

Who it is for

Manufacturing teams, engineers, AI developers, and students evaluating data foundations before model work.

Inputs required

Process images, machine logs, parameter history, traceability, CAD/path linkage, inspection outcomes, labels, repeated jobs, and operator feedback.

Output

A readiness band and the weakest data foundations to improve next.

Evidence needed

Linked job IDs, source data, inspection records, label definitions, model validation, and review rules.

Limitations

A high score does not qualify a model, approve a process, or prove part quality.

Example use case

Checking whether a monitoring workflow is ready for anomaly triage or only basic data capture.

Thesis connection

Sense connected data, model only after traceability, decide support scope, and verify against inspection outcomes.

Interactive