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Years ago, when I worked in New York City, I bought a Sony Walkman for my daily commute. It was cutting edge technology and I was an early adopter. I imagined that I looked pretty cool, listening to tunes I organized myself on a mix tape. Fast forward to today, where music is everywhere, on every conceivable device.

Manufacturing Engineering asked thought leaders at five companies for their views on challenges and trends facing the metalworking industry.

There have been many process improvement trends in manufacturing over the decades, and none have had more significant ROI than machine monitoring. The increase in machine monitoring is owed in large part to the rise in popularity of the open and royalty-free interconnectivity standard MTConnect.

Artificial intelligence will go long way to imbue data management with trust.

A decade removed from the Great Recession, the U.S. job market is thriving. Because employers are struggling to fill empty positions, they must explore other ways to increase production needs.

Automotive manufacturers and their Tier One suppliers spend endless engineering hours developing the PAB (passenger airbag) system, from the airbag and its propellant to the construction of the materials used in the composite instrument panel.

Composite materials have clear benefits for manufactured parts in aerospace, medical, automotive applications and many other industries. Ensuring the highest part accuracy is critical. Force measurement and material testing are essential processes for product designers and manufacturers to gain insightful data to create high-quality composite components.

Fluence Analytics (formerly Advanced Polymer Monitoring Technologies), a manufacturer of smart industrial and laboratory monitoring systems, recently released the third generation of its ACOMP, an automated system that performs continuous, real-time monitoring and characterization of polymers for 3D printing and other uses during manufacturing and post-processing.

Additive will provide a simpler, more responsive supply chain for high-value parts, according to Velo3D CEO Benny Buller.

An engine manufacturer discovers there is a way to reduce 50 billion data points to 2 billion—a reasonable number from which the foundation for machine learning can be built.