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Develop and Deploy Automated Object Detection Model for Steel Defect Inspection

spyro-soft.com

Manufacturing

Automotive

Construction

Inefficient and Error-Prone Manual Steel Defect Inspection

Currently, Acme Steel relies on manual visual inspection of steel products to identify defects. This process is time-consuming, prone to human error, and struggles to keep pace with increasing production volume. The manual inspection process also lacks consistent defect identification criteria, leading to variability in quality control. Identifying defects quickly is crucial to avoid costly rework and ensure product quality meets stringent industry standards.

About the Client

Acme Steel Manufacturing is a large-scale steel producer specializing in high-strength steel for automotive and construction applications. They are seeking to improve quality control and reduce defects in their steel products.

Project Goals

Automate the steel defect detection process using computer vision.
Improve defect detection accuracy and consistency.
Reduce inspection time and labor costs.
Enable real-time quality control during the manufacturing process.
Establish a baseline AI model for future defect detection enhancements.

Functional Requirements

Image ingestion from factory cameras/systems.
Automated defect detection and classification.
Defect identification with confidence scores.
Threshold adjustment for precision and recall optimization.
Reporting and visualization of defect data.
Integration with existing quality control systems (e.g., MES).

Preferred Technologies

Azure Custom Vision

Azure Cognitive Services

Python

Cloud Storage (Azure Blob Storage)

Required Integrations

Factory Camera System API
Manufacturing Execution System (MES) API

Non-Functional Requirements

Scalability to handle increasing image volumes.
Real-time or near real-time processing capabilities.
High accuracy and reliability.
Secure data storage and access control.
Maintainability and ease of updates.

Expected Business Impact

Implementation of this automated defect detection system is expected to significantly reduce production costs, improve product quality, and enhance operational efficiency. By automating the inspection process, Acme Steel can reduce labor costs, minimize rework, and improve overall manufacturing throughput. The improved defect detection accuracy will lead to fewer defective products reaching customers, enhancing brand reputation and reducing warranty claims.