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.Artificial Intelligence - AIFabrication

What is AI ?

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

We have decades of experience meeting the stringent regulations and certifications for the automotive, aerospace and medical device industries, which serve as benchmarks for all of the products we manufacture.

Business Challenges

In manufacturing, you’re under pressure to continuously improve quality while reducing costs and increasing productivity. You also strive to right-size inventory and boost profitability while driving year-over-year cost improvements. Finding new ways to extract value from the deluge of sensor and IoT data would enable you to move from a reactive to a proactive approach to minimizing unplanned downtime, reducing scrap and rework, and developing innovative new revenue streams.

Managing the unexpected is a constant challenge. Traditional approaches – Six Sigma, line-level reporting, MES systems – are no longer sufficient for gaining insights from data to improve decision-making. Finding new ways to harness the value of industrial data is essential to enabling modern manufacturers to manage today’s data volume, velocity, and variety.

How AI Can Help

Advances in AI enable us to automate complicated tasks and find useful signals in data that was previously too large or complex to tackle. From quality and equipment performance, to supply chain and spare parts optimization, to service improvements and monetization of IoT data, AI techniques can unlock new insights across the spectrum of manufacturing data, enabling you to:

Find early indicators of potential quality issues. AI capabilities go far beyond what simple rule-based systems can do, continuously learning to automatically detect patterns in data that a human would likely never see.
Avoid costly scrap and rework. Use image recognition to identify flaws during the manufacturing process so you can address them promptly.
Identify areas for improvement. Text analytics, including natural language processing, lets you link customer sentiment, service comments, and other written records to quality and production variables to identify areas for improvement.
Improve yield. Apply deep learning in industrial operations to optimize product composition and production techniques, combining audio, video, text, and other data at efficiency levels that were previously unimaginable.

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