The Impact of AI and Machine Learning Technologies in Manufacturing

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.

Challenges of AI

As the benefits of main AI elements such as Machine Learning, Data Analysis, and Predictive Analysis are undeniable, what are the few biggest challenges that companies may face while introducing AI to their day-to-day operations?

Why CNC Machining and AI Make a Perfect Match

What's next for the world of CNC operators? An increasing move to incorporate artificial intelligence, or AI, into operations. Over the next several years, CNC machining could see something of a revolution that includes machines that respond to Alexa-like voice commands.

The Next Era of Automation Is Intelligence

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.

Digital Manufacturing and IIoT: A Platform for Success

To truly maximize IIoT, manufacturers need a single solution that supports scalable-IIoT deployments and creates a collaborative environment that's data-driven and provides transparency across the entire production process.

Camera Specs - Not the Frontier in the Era of AI - Autonomous Machine Vision (AMV) Offers a New Approach to Visual QA

Machine vision engineers are pushing the frontiers of camera specifications to offer solutions with incredible resolution. Yonatan Hyatt explains why Autonomous Machine Vision (AMV) is pushing the boundaries of artificial intelligence, not camera specifications.

Edge AI technology In Manufacturing Operations

A downside of so much automation is that there is now a heavier dependence on machinery/robots to perform action. For some lines, if one of robot goes down, the entire line does.

50 Ways to Impact Your Business with AI

Sometimes the most difficult thing about AI is simply knowing where to start. Identifying potentially impactful use cases is one of the most cited roadblocks for organizations seeking to leverage AI in their business.

Different Ways Industrial AI is Revolutionizing Manufacturing

Most companies are already seeing and will see positive effects from AI, with a predicted revenue increase of 22.6%. In fact, many manufacturing companies are already implementing AI practices

Digital Twin Technologies, AI, & Automation

"3rd Annual Internet of Manufacturing is a very important conference for the construction and support of the industrial the ecosystem. It's a great pleasure to come and compare new ideas and projects and to meet with and discover new suppliers and manufacturers."

Data Requirements for Artificial Intelligence in Manufacturing

In the manufacturing context, an example of a pattern might be the ways in which a set of parameters contained in that data, which are related to a process in a factory, vary together.

Revolutionary Technology Addresses Industrial Downtime

New technology is poised to transform the way machines operate in the industrial environment. Costly consultants and confusing spreadsheets are out, and easy to understand dashboards are in.

Is Your Data Telling the Whole Story?

Even when you can connect directly to whatever it is that you are measuring and design the system yourself, you might encounter issues. Inconsistencies can arise from power failures, wifi outages, faulty or damaged equipment.

Empowering the Manufacturing Industry Through Decentralised AI

AI is big business-US venture capital investment in the sector reached $6.6 billion in the first three quarters of 2018, compared to $3.9 billion in the same period the year before.

AI-powered Cameras in Factories to Find Defects

Instrumental is attacking the 20 to 35 cents of every $1 spent in manufacturing that is wasted. That waste comes from literal scrap at the various factories in the process, returns, mistakes, extra experimentation, travel, and engineering time that wasn't used efficiently..

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