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.
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.
As with any digital transformation process, the devil is in the details, and there are many potential pitfalls that can derail projects.
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.
As I walked through the DMG Mori factory in Davis, Calif., during the company’s Manufacturing Days event in October, there was something noticeably different about it compared to other factories I’ve visited: it was brightly lit and quiet.
For years, companies have struggled to understand how additive manufacturing (AM) can add value to their businesses. This makes sense because for a long time, additive tech didn’t meet the threshold for producing industrial-grade parts.
With an influx of investment in digital factories, the playing field is changing and the ROI for digitizing production is becoming ever more apparent. However, restraints, such as company size and a disconnect between IT and OT, means the road to a successful digital transformation is one very few will be able to do alone.
There is an ever-increasing demand for the individualization of products from today’s consumer. When consumers are able to get exactly what they want (shape, size, color) they are more satisfied and more likely to do repeat business. But how do you scale custom part production?
The Pittsburgh region is a hotbed of activity in robotics and AI. This activity includes research and technology companies that commercialize academic research and solve real world problems.
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.