Metallurgy: processingDocument intelligence
Sulzer
CRANE

Lab compliance: PDF extraction and normative thresholds

A document pipeline that extracts chemical values from lab reports, applies ASTM/EN rules and accelerates the line decision.

−95 %

manual checks on the targeted reports

−47 %

lab → line decision lead time

ASTM/EN

thresholds applied automatically

Context

The problem

Unstructured lab PDFs

Supplier and internal reports in variable formats: manual extraction of chemical and mechanical values.

Inconsistent reject decisions

Interpretation of ASTM/EN thresholds depends on the shift technician: risk of inconsistent acceptance or rejection.

Bottleneck on material release

The line waits for lab validation: every hour of manual parsing is an hour of production blocked or run just-in-time.

No structured archive

Extracted values are not systematically historized for supplier trend analysis.

Solution

Deployed architecture

Extraction

Multi-layout Document AI

OCR + adaptive parsing on the most frequent lab PDF templates: progressive extension to new formats.

Rules

ASTM/EN compliance engine

Automatic application of normative thresholds by grade and by application. Explicit conforming / non-conforming decision.

Workflow

Targeted human-review queue

Only ambiguous or off-template cases go to the technician: with fields pre-filled.

History

Structured supplier database

Extracted values stored for trend analysis and material audit.

Results

Measured results

Measured in production conditions on the deployed scope.

−95 %

Manual checks

On the report types covered by the pipeline in production.

−47 %

Lab → line lead time

Time between report receipt and material-release decision.

100 %

Threshold application

Every value passed through the rules engine: no more missed threshold on the night shift.

Audit

Decision traceability

Source PDF, extracted values and applied rule retained per material batch.

Lessons learned

What we took away

Cover the 3–5 PDF templates that represent 80% of the volume first: do not aim for the universal on day 1.
The rules engine must be versioned and auditable: standards and customer grades evolve.
Human review on ambiguous cases is a feature, not a system failure.
Connecting the decision to the line (MES/ERP) avoids the 'PDF approved but line not informed' gap.

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