AI-Powered Quality Control and Production Efficiency

AI-driven systems are transforming quality control by using deep learning technologies to identify glass defects, including scratches and inclusions, with greater speed and precision. These solutions help manufacturers reduce rework, minimize material waste, and maintain consistent product quality.

Beyond quality inspection, AI can analyze production data to identify inefficiencies, optimize resource utilization, and streamline manufacturing workflows. These improvements support greater operational efficiency, reduced energy consumption, and lower CO₂ emissions.

Smarter Order Processing with A+W's MIRA

A+W Software has developed MIRA, an AI-powered assistant designed to simplify and automate one of the most time-consuming tasks in glass processing: order entry.

MIRA can interpret order information from emails, PDFs, and written notes, transferring relevant data into existing software systems. By learning from user corrections, the assistant continuously improves its accuracy and adapts to individual workflows.

Trained specifically for each customer's operational requirements, MIRA supports more efficient order management while reducing manual administrative work.

As part of the broader A+W Smart Factory vision, MIRA represents another step toward intelligent, automated glass production, where AI supports greater efficiency, accuracy, and connectivity across manufacturing operations.

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This article is based on information published by Glass on Web, highlighting the role of artificial intelligence in flat glass processing and A+W Software's MIRA solution.