#Design shutterstock how to

#Design shutterstock how to

AraqevвЂ™s patented ML algorithms enable users to learn in fractions of a second how to print more accurately across new shapes and processes, resulting in less wasted scrap and machining time.ДЄrman Sabbaghi, associate professor in PurdueвЂ™s Department of Statistics in the College of Science, is AraqevвЂ™s CEO and president. Quality-control software for additive manufacturingДЄerospace, automotive, consumer products, medical devices, national defence and other sectors that use additive manufacturing (AM) and 3D printing technologies can benefit from quality-control software that is being commercialised by Araqev, a Purdue University-spin out company.ДЄraqevвЂ™s software helps end-users print products in only a few design iterations, leading to less scrap material and machining time, eliminating the frustrations with 3D printing, and improving satisfaction with the final printed products.ДЄraqev creates software solutions for closed-loop, smart control in additive manufacturing and 3D printing based on machine learning (ML). Software is also driving the use of new methods and materials such as the application of AM techniques to the production of solid rocket energetics or the use of machine learning to print AM designs more accurately. Increasingly, the market is seeing validation and quality control tools that can help designers and engineers to ensure that time and resources are not wasted on unnecccsary design iterations. Software plays a key role in the maturation of this market with rapid increases in the complexity of parts and the materials and methods used to create new components with AM.

Additive manufacturing (AM) is opening up new possibilities for engineers to create components and subsystems enabling rapid prototyping and novel designs that can help to accelerate innovation.