Use Cases for AI: AI for Quality Control in Manufacturing
While AI holds significant promise in the advancement of industrial automation, its adoption has been challenged by the ability to deploy AI as a production-grade solution that can scale across the manufacturing environment. One key application where this is changing is in Quality Control for asset manufacturing. AI Quality Control applications such as End-of-Line testing and predicting quality issues before more units are manufactured, are saving manufacturers millions of dollars in scrap, while improving the quality of the products shipped.
AI is enhancing existing quality control solutions by incorporating sensor data, such as vibration and acoustics, and helping to solve the challenge of processing large amounts of this data along with relevant environmental, test and production information, to provide the quality and performance of an asset in real-time, speeding up decision making at the production level, while reducing the burden on quality and manufacturing teams who typically have to analyze large amounts of data to extract conclusive information.
Automotive manufacturing -- specifically, compressor and compressor-based systems -- is one area where AI is having a significant impact on quality control testing of mechanical systems. AI that is capable of creating models specific to each compressor, quickly measuring and processing high bandwidth sensor data during production and providing quality scores that identify suboptimal products provides manufacturers of these assets an enhanced quality control solution that supports testing of a wide range of assets and scales across multiple production lines, further reducing defective and subpar quality assets from being shipped to key customers.
This presentation will provide more insights into AI for Quality Control solutions along with detailed examples where these solutions are being used in production applications.
Use Cases for AI: AI for Quality Control in Manufacturing
While AI holds significant promise in the advancement of industrial automation, its adoption has been challenged by the ability to deploy AI as a production-grade solution that can scale across the manufacturing environment. One key application where this is changing is in Quality Control for asset manufacturing. AI Quality Control applications such as End-of-Line testing and predicting quality issues before more units are manufactured, are saving manufacturers millions of dollars in scrap, while improving the quality of the products shipped.
AI is enhancing existing quality control solutions by incorporating sensor data, such as vibration and acoustics, and helping to solve the challenge of processing large amounts of this data along with relevant environmental, test and production information, to provide the quality and performance of an asset in real-time, speeding up decision making at the production level, while reducing the burden on quality and manufacturing teams who typically have to analyze large amounts of data to extract conclusive information.
Automotive manufacturing -- specifically, compressor and compressor-based systems -- is one area where AI is having a significant impact on quality control testing of mechanical systems. AI that is capable of creating models specific to each compressor, quickly measuring and processing high bandwidth sensor data during production and providing quality scores that identify suboptimal products provides manufacturers of these assets an enhanced quality control solution that supports testing of a wide range of assets and scales across multiple production lines, further reducing defective and subpar quality assets from being shipped to key customers.
This presentation will provide more insights into AI for Quality Control solutions along with detailed examples where these solutions are being used in production applications.
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