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ARRAYMATIC

ArrayMatic Technologies

B-23, B Block, Sector 63, Noida, Uttar Pradesh 201301

[email protected]

+91-9555505981

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HomeIndustriesManufacturingAI for Quality Control & Inspection

Manufacturing

AI for Quality Control & Inspection

Computer vision systems deployed on production lines that inspect parts and surfaces for defects at speeds and accuracies that exceed manual inspection — with zero inspection fatigue.

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Industry overview

Computer vision inspection systems trained on manufacturing defect images that identify surface flaws, dimensional deviations, and assembly errors on production lines in real time.

At a glance

  • Computer vision model training on production-specific defect images
  • In-line camera integration with automatic non-conformance ejection
  • Defect classification and root cause categorisation

Manual quality inspection is slow, inconsistent, and does not scale. Human inspectors miss defects at high line speeds, and inspection fatigue compounds the problem over a shift. For high-mix production environments, training inspectors on every new variant is a continuous challenge. Machine vision solves all three.

What we build

We develop and deploy computer vision models trained on production-specific defect catalogues. Camera hardware is integrated with line control systems so non-conforming parts are automatically ejected or flagged. Classification models identify defect type and root cause to support process improvement. Statistical process control integration monitors defect rates over time — alerting process engineers when trends indicate a developing problem before yield falls significantly.

Key capabilities

What we deliver

Engagements are scoped to your business context — these are the core capabilities we bring to manufacturing clients.

Computer vision model training on production-specific defect images

In-line camera integration with automatic non-conformance ejection

Defect classification and root cause categorisation

Statistical process control (SPC) integration and alerting

Inspection audit logging and traceability records

False positive rate tuning to balance throughput and quality

Built with

PythonTensorFlowAWSDocker

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