Purpose-built solution AI and machine vision solution on Intel® edge processors are helping manufacturers successfully adapt the defect detection technology and expand it to address broader Industry 4.0 use-cases.
Swedish philosopher Nick Bostrom once said, "machine intelligence is the last invention that humanity will ever need to make". AI decision making, with real time communication and data analytics, has the ability to transform the way manufacturers understand machines.
Without making wholesale production changes, factories can improve process efficiency with machine learning (ML). ML can increase production capacity by up to 20% while lowering material consumption rates by 4%.
Artificial intelligence (AI) is one of the fastest-growing technologies in the business world today. AI is a broad umbrella, covering many specific technologies, and machine learning is one of the most promising of these.
New data- and ML-based business models will become more commonplace, so early adopters will have a competitive advantage. Machine learning will happen both in the cloud and on the edge, i.e., directly on or next to production machines and sensors.
There are a number of ways to maintain industrial machinery. If you search around, you will most likely come across these terms: preventive maintenance and predictive maintenance.
We are entering a new manufacturing era defined by AI, machine learning and vision, enabling us to teach machines to adapt and learn plans and move us toward a true "Lot Size 1" utopia. Automation's next era will be defined not around volume, but around intelligence.
Autonomous Maintenance is one of eight pillars that make up Total Productive Maintenance (TPM). It's also one of the most important activities that eventually determine the success of any TPM implementation project.
Iota has developed a framework called the Sustainability Process Blueprint that allows businesses to easily monitor, measure, and curate data pertaining to their sustainability performance.
Industry 4.0, or the fourth industrial revolution, has called for a merger between automated solutions and smarter, more effective operations through the application of real-time data collection.
Machine learning and big data are both very difficult, in practically every way. The necessary data science skills are rare among industry professionals, the data volumes are immense, and the infrastructure required to work on that data is complex.
Machine learning is a continuance of the perceptions around predictive analytics, except that the AI system is able to make assumptions, test them and learn autonomously.
The proven impact of machine learning models has pushed more investment toward their development. Still there are plenty more gains to be realized.
Every great business has well thought of goals and strategies for the future. Thanks to the AI in your ERP (Enterprise Resource Planning) systems) it will help your users identify "next best actions"
Manuel Nau for IoT Business News: Oracle is taking the IoT to a new level with the announcement that its IoT cloud service will now incorporate artificial intelligence and machine learning to provide clients with better business data insights than ever.
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The FAULHABER stepper motor AM3248 raises the bar in terms of performance and dimensions. Offering up to 10,000 rpm, it achieves five times the speed of comparable stepper motors. Combined with a gearhead reduction of 100:1, it supplies a torque of 5 Nm. With a diameter of just 32 mm, it is suited for a wide range of applications in areas such as aerospace, laboratory automation, the semiconductor industry, robotics and 3D printing. Learn more!