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AM is a manufacturing technology like many other but, unlike most, has numerous variables at play in making the final part. Most are controlled by the initial setup by the lab technician, but after that there is very little that goes in the way of making sure that the best result is achieved. In-print monitoring is crucial yet still hard to apply properly. Techniques like machine learning enable automated pinpointing of potential issues, stopping before precious time and resources are wasted. This will be made possible thanks to a slew of sensors that power computer-vision algorithms. The bandwidth required for these applications will be huge, something that coming 5G networks will be able to support, together with other IIoT applications previously impossible. In the future, self-correcting printers will make AM much more reliable and efficient. There is already so much that the data coming from printers can teach to improve operational performance. At Authentise we have developed smart analytical tools to help you leverage all that data, and are now moving towards letting you control printer directly, with remote and automated tools.
Machine Learning and Metal 3D Printing Combine for Real-Time Process Monitoring Algorithm
Two researchers from the College of Engineering at Carnegie Mellon University (CMU) have figured out how to combine 3D printing and machine learning for real-time process monitoring, a practice which can detect anomalies inside a part while it’s being 3D printed. Their research could one day lead to self-correcting 3D printers.
Read the full story here.
New whitepaper examines smart metrology for additive manufacturing
If factories are to become faster and more flexible, inspection is a bottleneck to overcome, especially in industries where 100% inspection is required. In this new whitepaper by Autodesk and Faro, smart metrology for the additive manufacturing industry. Components made by additive manufacturing technologies (AM) have more variables than machined parts. Faster inspection for additive manufacturing is more challenging because AM processes are not as accurate as cutting metal. Better metrology for AM will help reduce feedstock and costs.
Check out the whitepaper here.
How Will 5G Change Robotics and the IIoT?
As efficient and effective as 4G technology is, it pales in comparison to the faster, more reliable platform of 5G. If the new protocol meets its advertised speeds of 100 gigabits per second, this rates 5G at a speed of 1,000 times faster than 4G. Given the increasing size of datasets, the greater need for real-time data processing and more reliance on large-scale and long-term data storage, it’s easy to see how 5G benefits everyone.
Read the full article here.
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