To get to smart manufacturing, the industry needs integration, simulation and analysis.
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When I graduated with an engineering degree some decades ago, I learned that the organizations I was going to work for had internal communication problems. This was especially true for those that designed and manufactured complex machinery such as engines, aircraft, or automobiles.
SME’s Smart Manufacturing Hub will be part of IMTS this year. Smart Manufacturing asked past Hub speakers to imagine what manufacturing will look like in 2030. Here are their visions:
My instincts tell me we need a sense of urgency around the use of artificial intelligence (AI) in manufacturing. The urgency is driven by how quickly technology can move today, and how an unexpected breakthrough can quickly dominate.
New systems, software and processes are replacing so-called islands of automation with seamless, automated manufacturing lines that boost overall equipment effectiveness (OEE) from 30 to 80% or more.
In the near absence of academic programs to teach undergraduate engineering students additive manufacturing, a California-based startup has stepped in to help fill the void through internships.
My original intention for this column was to discuss a phrase getting a lot of buzz lately, artificial intelligence (AI). By any measure, interest in AI is expanding exponentially, both in the number of articles one can read on the subject and, according to Google Trends, the number of searches for those articles.
Demand for automation and robots is surging in multiple industries, including automotive, writes the CEO of Thomas.com.
Living with the day-to-day reality of COVID-19 can be challenging for individuals. Running a business in this pandemic era is an order of magnitude harder.
A new breed of turbochargers constructed of super tough alloys operates at higher temperatures and rotational speeds than ever before, resulting in greatly increased output in a smaller package for gas and diesel engines alike.