Quality assurance will guarantee AM’s future (Authentise Weekly News-In-Review – Week 25)
AM is transforming the way engineers approach a design problem with enhanced manufacturing possibilities. Nonetheless, there are some crucial steps that need to be taken in order to make AM safe and reliable enough to meet industry standards. Already the scene is making giant strides in its effort to assure quality and the main areas to consider are three: CAD models preparation, AM material inspection and in-print monitoring. Better hardware and dedicated software by Nvidia is making dealing with complex designs much more efficient, unconstrained by performance issues and with new tools to approach AM-specific design issues. Powder micro-structure needs to be within certain parameters for optimal sintering: Carnegie Mellon developed a machine-vision system to classify AM metal powders. For in-print monitoring, GE published new patents to determine the quality of a print from acoustic signatures during the process.
Authentise has developed platforms that take advantage of every major monitoring device. Companies like Nike and Ricoh are using this data-enriched perspective to make smarter decisions on their manufacturing operations.
Read more about it here!
How GPUs Can Kick 3D Printing Industry Into High Gear
At last month’s GPU Technology Conference, HP Labs and NVIDIA described how they’ve worked together to overcome these challenges using NVIDIA’s new GVDB Voxel open source software development kit. […] Hoetzlein said the SDK is designed for simple efficient computation, simulation and rendering, even when there’s sparse volumetric data. It includes a compute API that generates high-resolution data and requires minimal memory footprint, and a rendering API that supports development of CUDA and NVIDIA OptiX pathways, allowing users to write custom rendering kernels.
Read more on NVIDIA’s blog.
Carnegie Mellon develops machine vision autonomous system for metal 3D printing
Research from Carnegie Mellon University’s (CMU) College of Engineering has developed an autonomous system for classifying the metal powders used for 3D printing. Using machine vision technology, the system can identify specific microstructures in the additive manufacturing metal powders with an accuracy of greater than 95%. Metal powders are used in powder bed fusion 3D printers. Understanding the quality of the material is essential to the integrity of the resulting parts. The CMU engineers expect their system to be applied by the 3D printing industry within the next five years as part of the Carnegie Mellon University’s NextManufacturing Center aims.
Read the full article here.
GE publishes patents for powder bed fusion acoustic monitoring processes to qualify metal 3D printed parts
GE has published two patents for additive manufacturing acoustic monitoring processes. Referring specifically to powder-bed fusion techniques, GE hopes to simplify the qualification of printed parts with an in-situ monitoring method using acoustic waves. In turn, the company intends to improve the workflow of 3D printing functional metal parts. […] According to the patent, the acoustic monitoring process may take place upon completion of the build or it, “may take place in real time.” It uses a “known good” (fig. 4)workpiece as comparison, which means the acoustic profile generated by the sensors is compared to the profile of the already qualified part.
Read more about the patent here.
Don’t forget to come back next week for another News-In-Review and to check our Twitter feed for more AM and IIoT related news and Authentise service updates!