The value-add of blockchain for businesses is estimated to grow into the trillions by 2030. Experts believe product recalls alone—estimated to cost $8 million today—could be practically eliminated through improved track and traceability enabled by blockchain.
The low temperature intrinsic to solid-state printing processes allows manufacturers to weld layers of dissimilar metals without fear of metallurgical incompatibility issues.
What is good about inventory? Being able to find a part when you need it. What’s not so good about inventory? Where to even start…
The next cycle of technology disruption is upon us. Artificial Intelligence (AI) is taking hold in every industry and manufacturing is no exception. AI enables companies—from medical device and electronics manufacturers to pharmaceutical firms—to leverage their Big Data and IoT investments to see new patterns and insights and to perform tasks more efficiently and quickly than ever before.
Nearly a year ago, the world became aware of a new computer virus known as WannaCry. Many institutions were affected by the ransomware. It encrypted and locked a Microsoft Windows computing system and demanded payment.
Companies like ABB, Balluff and Sick would be within their rights to film a commercial with exuberant sensor product managers breaking out in a song of cheer.
When it comes to materials, artificial intelligence can automate the screening process, simulate the performance of different materials and identify the best option.
Imagine hearing the news that manufacturers are producing a proven and safe vaccine for COVID-19 and shipping it your way. It will be music to the world’s ears.
The increased use of CT scanning for metal powder bed fusion parts is usually associated with high-value parts and elevated quality requirements. There are increased requests for CT scanning on parts made of engineering-grade polymers like PEEK, PEKK or ULTEM and for fiber-reinforced composites like Nylon 12 CF.
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.
