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.
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Manufacturers are facing shrinking product lifecycles with frequently changing customer demands. As a result, they need agile production and flexible factory layouts that can easily be modified whenever needed.
As with any digital transformation process, the devil is in the details, and there are many potential pitfalls that can derail projects.
There is no shortage of competition in a global market. As a manufacturer trying to get ahead of the pack, automation can help with problems like a limited skilled labor force, quality control issues and suboptimal throughput. But the high initial cost and extended implementation time can be deterrents.
Cyber criminals are increasingly setting their sights on today’s digitized manufacturing industry as an entry point into government and commercial supply chains.
Artificial Intelligence is weaved in with capacity management, cybersecurity, data science, diagnostics, ERP-PLM integration, location analysis, machine learning, predictive maintenance, process optimization, situational awareness and supply chain management.
Smart sensors, already an integral feature of many manufacturing plants that are integrating IT and OT, are now making their way into the supply chain where they monitor reliability and shipping conditions, improve predictive maintenance and make just-in-time delivery (the innovation from the 1980s) easier.
The U.S. needs to build a national infrastructure in engineering and manufacturing R&D that parallels its scientific infrastructure. While it makes all the sense in the world, it is not happening.
On June 22-23, SME hosted a Smart Manufacturing Working Group meeting at Texas A&M University (College Station, TX) followed by an international workshop on Smart Manufacturing for the Factory of the Future.
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.