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.
Mark 2020 as the year the U.S. government chose to stand up a Manufacturing Innovation Institute focused solely on cybersecurity.
Before the coronavirus pandemic upended normal life and essentially shut down commercial airliners, the aviation industry had a projected need for 40,000 new aircraft—planes, helicopters, air taxis, and unmanned aerial vehicles—in the next 20 years.
Precision grinding operations cover all applications that require dimensions with tight tolerances and low Ra surface finish requirements, including cylindrical external grinding (OD), internal grinding (ID), surface grinding and creepfeed grinding.
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.
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.
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.
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.
Machine vision is proving ideal in helping humans perform tedious but crucial manufacturing tasks. That is why it is poised to grow significantly in the next few years.
