Today, laser technology in manufacturing touches all of our lives on a daily basis; lasers cut air bag material and weld air bag detonators for our in-car safety; lasers weld the batteries in many of our mobile devices; lasers drill aero-engine components for planes; lasers cut the glass for our smart phones and tablets screens; lasers weld the drivetrains in our cars and trucks; lasers cut medical stents that increase and enhance our lives, just to name a few.
With the rapid pace of digitalization, operations are becoming faster and leaner than ever.
Closed-loop manufacturing (CLM) is emerging as a viable approach to address tighter delivery timelines and supply chain challenges.
Having a plan for maintaining and improving the performance and reliability of every machine on a shop floor is vital to manufacturing operations. Reliable machines make short-notice production runs possible. And the more flexible manufacturers are, the more new customers they’ll attract.
Most manufacturers have relied on third-party vendors to make parts that are then incorporated into the final product. From automakers sourcing stereos and aircraft makers contracting for jet engines to a small bakery ordering plastic bags or a woodshop buying nails, producers of all types have supplemented their internal capabilities through a painstakingly developed supply chain of external vendors.
Just getting familiar with the digital thread? You’ve come to the right place to learn what it is and why you need it for your products.
Additive will provide a simpler, more responsive supply chain for high-value parts, according to Velo3D CEO Benny Buller.
Robots simply are not used as widely as they could be, due to persistent barriers.
EV manufacturers must overcome a unique set of challenges to meet future customer expectations. Among them is the challenge to create innovative designs that meet safety requirements, performance criteria and keep costs down in the face of growing competition and a widening skills gap.
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.
