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Why don’t more manufacturers in the United States use smart manufacturing technologies like AI and machine learning to reduce waste, achieve predictive maintenance and enhance their automation systems? Five CESMII roundtable panelists share their insights.

AP&T provides a case study of the work it did at a plant in Italy of Fiat Chrysler Automobiles (now Stellantis).

As cobots work their way into machine shops, users are finding many new jobs for them.

Vecna Robotics’ David Clear and SVT Robotics’ TJ Fanning go into reasons manufacturers might want to consider automation and AI. They also look at how to test assumptions and scale with so many variables changing moment by moment. The adage “change is the only constant” has never been more apropos. So, it’s a great time to hear what separates a complex system from a complex adaptive system.

Catalytic CEO Sean Chou explains the difference between process automation and robotic automation, as well as what it looks like to use automation to augment existing workflows. Importantly, he describes how manufacturers can use automation to do more with less—to lessen supply chain pressures that have grown because of globalization and the Covid-19 crisis. And he details which processes manufacturers can automate to optimize resources and productivity.

The IUPUI Mechanical Engineering Technology program concentrated on Mastercam and the online benefits of Mastercam University for its students. This approach paid big dividends on a unique Indy Car project for the Indianapolis Motor Speedway.

Software AG (Frankfurt, MDAX: SOW) announced an original survey of over 125 North American manufacturers in the heavy industry and automotive sectors that revealed they are unable to scale their Industrial Internet of Things (IIoT) investments across their enterprises, and therefore are losing millions of dollars in potential profits while falling behind competitors that have invested in enabling technologies that support IIoT across the enterprise.

Some of the key trends in manufacturing are brought about by convergence of the design and manufacturing industries. With manufacturers under more pressure than ever to deliver better products faster and at lower cost, the need to connect and automate design and manufacturing processes to reduce iterations, errors, and delivery times is becoming critical.

For decades, plant personnel have performed insulation resistance tests with handheld megohmmeters to prevent motor failures that lead to costly unplanned shutdowns, failure-to-produce penalties and rewinding repairs. However, these tests only provide a “snapshot” of motor health. In a matter of days, motor windings and cables exposed to moisture, chemicals, contaminants or vibration can be compromised and fail at startup.

Amid vigorous growth in their industry, product lifecycle management (PLM) software developers are exploiting the cloud and machine learning to manage data and enhance the users’ experience.