Simulation: how machines are better problem-solvers (Authentise Weekly News-In-Review – Week 38)

Physical testing can only take us so far. New techniques in digital simulations enable us to experiment with every variable at play to guarantee the best desirable performance. This is the case, for example, when trying to pinpoint the reason for behind “material redistribution”, a phenomenon that leads to defects in printed metal parts. Simple observation and image recognition can only lead to partial understanding (although Nvidia’s GPUs have shown that huge strides have been made in that regard) as part of the reactions happen below the surface or in other unaccountable regions. Computer models of the system, coupled with high-speed monitoring of the same, can give unprecedented holistic vantage points when investigating these activities. Similarly, simulation can take researchers far in terms of understanding long-extinct animals’ behavior. Hydrodynamics, bone-structure, muscle arrangement, all this can be taken into account when determining the most plausible gaze for creatures that lived millions of years ago, in a system which can then be 3D printed and tested tangibly. Similarly, Canadian researchers want to take it a step further by merging AI simulation with manufacturing capabilities, creating a 3D printer which dissects a problem and finds the appropriate solution automatically.

Team finds reason behind defects in 3-D printing

LLNL finds reason behind defects in 3D printing

In a study published by Scientific Reports , LLNL scientists combined ultrafast imaging of melt-pool dynamics with high-resolution simulations, finding that particles of liquid metal ejected from the laser’s path during the powder-bed fusion additive manufacturing (PBFAM) process—commonly called “spatter“—is caused by the entrainment of metal particles by an ambient gas flow, not from the laser’s recoil pressure, as previously believed.

Read more at Phys.org

University of Southampton 3D Printers Solve Million Year Old Flipper Mystery

To determine the swim-path of plesiosaur flippers Southampton researchers, alongside partners at the University of Bristol, 3D printed models based on the dimensions of a fossil skeleton. According to the supporting paper, experiments show “that plesiosaur hind flippers generated up to 60% more thrust and 40% higher efficiency when operating in harmony with their forward counterparts, when compared with operating alone.”

Read the fully article here.

Canadian Researchers in Pursuit of Artificially Intelligent 3D Printers

Edward Cyr examines a 3D rendering of a lattice structure. Photo via The Star Phoenix

Edward Cyr’s research, funded by a McCain postdoctoral fellowship, aims to develop an AI system that will approach a problem and 3D print its solution after considering all the alternatives. Cyr acknowledged that a human problem solver would only be able to come up with an optimal design after testing thousands upon thousands of ideas.

A computer, on the other hand, “can actually model a total design space and tell us which one is the best, and it can even come up with things we might not even think of.”

Read the full article here.

 

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Evolving Scene of Metal AM (Authentise Weekly News-In-Review – Week 15)

This week was chock-full of news related to metal AM. The global scene is intensely researching the potential of metal AM and competitiveness is growing in all its aspects: metal powder production, CAD optimization, manufacturing method and much more. This week we saw exposed not one, but two new methods of metal AM! LLNL and the University of Sheffield both came out with novel techniques to produce produce metal objects additively and they both have their own unique benefits, being that increased speed or greater reliability. All the while we are making strides in understanding the complex physics involved in metal sintering processes: greater knowledge and improved optimization software is also crucial to manufacture metal parts reliably and efficiently.

 

Lawrence Livermore National Laboratory announces new metal 3D printing method

US federal research facility, Lawrence Livermore National Laboratory (LLNL) has announced the results of an ongoing three-year research project into direct metal 3D printing. The technology, referred to as ‘Direct Metal Writing’ (DMW) adds to existing metal additive solutions such as selective laser melting (SLM). […] The new approach uses semi-solid metal feed material, beginning with a heated ingot or small block of metal. Once heated to a semi-solid state, the metal is then pushed through the extruder in a paste-like consistency. The material is shear thinning, which means it forms as a solid when left to rest and acts more like a viscous liquid when in motion or when applied with force.

Read more about DMW here.

Significant Speed Up For 3D Metal Printing Developed

Researchers at the University of Sheffield have developed a unique 3D metal printing process that could dramatically speed up metal printing. […] they call “Diode Area Melting”, or “DAM”. Instead of a single (or small number of) lasers, the DAM approach involves using an array of low power laser diode emitters. These emitters are not directed to the powder by an arrangement of mirrors, but instead are positioned above the powder surface and apply their energy directly.

Read more about DAM right here.

Challenges in modeling and simulation for metal additive manufacturing

Commercial acceptance of AM for exacting applications still faces a technical challenge caused by the limited understanding of physical phenomena in the melt pool. Real-time observation of this physical phenomena is difficult since AM melt pools are inherently transient and involve complex physical interactions between energy beam-powder substrate. Moreover, the real-time measurements of thermal and fluid variables can typically be made only on the surface of the melt pool. In contrast, a numerical simulation of mass, momentum, and energy transfer in melt pools can provide approximation of the melt pool shape and some useful 3D fields such as the distributions of temperature, flow velocities, solidification temperature gradient and solidification rate. Ultimately, an understanding of the relationships between processing, structure, properties and performance is essential.

Check out both parts of the article, here and here.

 

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