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Surface Inspection

Cross-Plant Quality Inspection for Aluminum Die-Cast Parts

Cross-Plant Quality Inspection for Aluminum Die-Cast Parts

With Maddox AI, a centralized cloud platform was introduced where defect classes, reference standards (golden samples), and inspection rules are defined once and then deployed as identical inspection logic to every location.

Product

Product

Die-Cast Parts

Industry

Industry

Automotive

Total savings / year​

Total savings / year​

280-330k €

Black metal oil pan on a factory workbench.

Customer Request

Customer Request

Our customer manufactures identical automotive die-cast parts at four plants (Western Europe, Eastern Europe, and North America) running three shifts. Despite the same drawings and requirements, quality inspection was long handled site by site: Each plant had its own reference standards, defect catalogs, and “OK/NOK” interpretations Decisions depended heavily on the inspector, shift, lighting, and local experience Rare defects (e.g., specific cold-shut patterns) were addressed locally only Surface variation increased false scrap and drove recurring re-sorting and re-inspection effort With the Maddox AI inspection system, the goal is to reliably detect key defects—especially flash/burr, cold shut (flow lines), and near-surface porosity (pitting/pores)—to reduce customer complaints and manual re-inspections and to establish consistent quality standards across all plants.

Before

Before

Previous Inspection System

Previous Inspection System

Previous Inspection System

Initial inspection relied on traditional rule-based systems, often followed by manual checks across all three shifts to re-verify scrap and false scrap.

Four lines meant four different standards—defect definitions, thresholds, and training were locally developed.

Rare defect patterns triggered customer complaints because they weren’t “known” at other sites.

After

After

With Maddox AI

With Maddox AI

With Maddox AI

Initial inspection relied on traditional rule-based systems, often followed by manual checks across all three shifts to re-verify scrap and false scrap.

100% inspection runs robustly through normal production variation (surface changes, reflections, minor process drift), significantly reducing false scrap.

All plants and lines use the same inspection logic (same classes, same decision rules), deployed as a unified model/configuration version.

When a rare defect appears at one plant, it is labeled once and used for training—then all other plants benefit immediately from the updated model.

Four lines meant four different standards—defect definitions, thresholds, and training were locally developed.

All plants and lines use the same inspection logic (same classes, same decision rules), deployed as a unified model/configuration version.

Rare defect patterns triggered customer complaints because they weren’t “known” at other sites.

When a rare defect appears at one plant, it is labeled once and used for training—then all other plants benefit immediately from the updated model.

65%

65%

65%

Less pseudo scrap

Unified

Unified

Unified

Inspection criteria worldwide

18 Mio.

18 Mio.

18 Mio.

Parts inspected per year (across all plants)

Want to learn more about our solution?

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Would you like to learn more about our solution?

Try it now for free!

Would you like to learn more about our solution?

Try it now for free!