Vision systems equipped with artificial intelligence (AI) capabilities have the ability to learn and distinguish between objects, as well as recognize defects or irregularities in products or processes.
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The key difference between preventive maintenance and predictive maintenance is that the latter means completing maintenance only when needed, as opposed to scheduled intervals.
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With advancements in artificial intelligence (AI) and machine learning (ML), manufacturers are now embracing innovative solutions to enhance quality control processes. AI is revolutionizing quality control in manufacturing, improving efficiency, accuracy, and overall product quality.
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The Karlsruhe Institute of Technology (KIT) has developed a process that automatically evaluates the surface conditions of objects using video images, neural networks and machine learning. Intelligent surface inspection can help to improve defect testing.
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According to a report released by MIT Sloan Management Review and Boston Consulting Group, an increasing number of companies are using AI to make KPIs more forward-looking and connected, dramatically improving legacy performance metrics.
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Siemens is releasing a new generative artificial intelligence (AI) functionality into its predictive maintenance solution – Senseye Predictive Maintenance.
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The amount of compute happening at the edge is growing fast because that is where data is generated. In addition, many edge computing deployments are incorporating AI.
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Intenseye’s enterprise-ready computer vision AI platform delivers 24/7 visibility into unsafe acts and conditions that precede work-related injuries or illnesses.
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AI-powered visual systems can detect defects, anomalies and deviations in real-time. They ensure the continuous high-quality production of products.
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ProKI is a Germany-wide network for the use of artificial intelligence (AI) in production
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