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Artificial Intelligence Comes to CAD

The bane of modern engineering is complexity. One promise of artificial intelligence and machine learning is helping engineers to use complex tools and harness vast data sets effectively.

Setting Sights on Holy Grails in AM

As additive manufacturing (AM) moves from prototypes to mass production, manufacturers are setting their sights on the holy grails—the products and processes that will be game-changers. Many game-changers are already in play.

DoD Set to Award $10 Million to DMDII

The U.S. Department of Defense (DoD) is set to award $10 million in funding this year to the Digital Manufacturing and Design Innovation Institute (DMDII) here, UI Labs CEO Caralynn Nowinski Collens, said this morning. UI Labs is DMDII’s parent organization.

Detroit Tigers: Reverse-Engineered World Series Trophy Honors ’68 Tigers

Technology came to the aid of Detroit Tigers management when they hoped to recapture some of the magic of the 1968 Detroit Tigers’ World Series-winning season. The 50-year anniversary celebration, held September 7-9, 2018, included on-field festivities in which the 16 surviving members of the 1968 team were presented with replicas of the World Series’ trophy.

Data-Driven Manufacturing

In my capacity as the Chair of the Council of the Manufacturing USA institute directors, I often get asked about trends in U.S. advanced manufacturing.

Traditional IT firms, traditional OT firms try newfangled ties

Information technology and operations technology are unlikely candidates for a successful marriage. But to ensure that manufacturers thrive in the digital age, OT and IT must find ways to work together—or to at least, as on Tinder, swipe right to indicate interest.

Laser Scanners Demand Attention as Technology Improves

Metrology-grade laser scanners are expanding their range of applications. New users are finding the main attractions of laser scanners—speed and ease of use. What prevented more widespread use in the past were laser scanners’ perceived tradeoffs. Using one usually meant sacrificing accuracy or working with noisy data.