Viewing a single comment thread. View all comments

BeardySi t1_j8mg3i8 wrote

Not exactly new, and if NASA are still CNC machining these they're missing a trick.

We've been printing this sort of thing in titanium for aerospace customers at work for years.

https://i.imgur.com/iRbw6Xa.jpg

227

bohemica_ t1_j8moupm wrote

Maybe in part due to size limitations. The pictures show parts manufactured in a powder bed. The article then goes on to say they‘re still designed around conventional milling. So who knows.

62

shifted1119 t1_j8njs82 wrote

Your example is still post-machined. Usually grown then wired then machined. If you can just machine it, it’s probably faster depending on quantities.

50

bohemica_ t1_j8nqljp wrote

Depending on how it’s designed, you might actually not be able to machine it due to inaccessible inner structures. It‘s not exactly time or cost-effective, though.

20

BeardySi t1_j8nvas9 wrote

Indeed. The real benefit of printed parts is you can make geometries that are not possible to machine.

Plus, I dread to think of the time and cost involved in machining that kind of thing out of a 250x250x400 titanium billet. Not many have NASA'a deep pockets!

12

Extra-Cap2029 t1_j8nn55q wrote

Why do so many articles get an “Akshuuually” response here? They’re confidently incorrect responses half the time. So weird.

26

Diriv t1_j8nqaxx wrote

Higher concentration of enthusiastic hobbyists/workers that like to talk about the things they think they know to people who, actually might, listen.

19

imaverysexybaby t1_j8o1ngo wrote

NASA engineers are famous for their lack of ingenuity, also jets and rockets are totally the same problem space.

/s

9

Zsem_le t1_j8odmae wrote

Maybe because most of the things wrote in an article anywhere is mostly for making advertisement money and not for informing anyone.

3

OverlordQ t1_j8oepsl wrote

This is Reddit, home of Dunning–Kruger

0

kou_uraki t1_j8nrsys wrote

3D printed metal has limitations that machined parts don't when it comes to strength. It has gotten better, but it still is not as good.

13

Angdrambor t1_j8n4n47 wrote

>if NASA are still CNC machining these they're missing a trick.

Was that in the article?

11

mnic001 t1_j8n5faw wrote

Yes, it specifically talks about the desire to switch to additive manufacturing

19

tearfueledkarma t1_j8ooq11 wrote

Safe to say NASA has much higher standards of quality they must meet. When you're designing something that cannot break for months or years.. after riding on a rocket.

1

Ranger5789 t1_j8mgp5b wrote

Different methods, yours created by algorithm, their's by ai.

−10

C-D-W t1_j8mybav wrote

The line between a procedural algorithm and AI is pretty blurry. The term AI is thrown around a LOT to describe things that would have never been described as AI 10 years ago despite being the exact same code.

54

nitrohigito t1_j8mq429 wrote

If you mean they're using topology optimization instead of generative design, how would you know?

20

CW3_OR_BUST t1_j8mypbw wrote

Kinda indistinguishable if it works all the same. Either way, nobody knows how the heck it works, but it works.

9

HazHonorAndAPenis t1_j8n6rdi wrote

I mean, it's creating a defined mesh of the part and the forces for each connection point on said mesh are calculated utilizing a defined matrix and some good old fashioned math, which you could do by hand but would take an untold amount of lifetimes for a single part.

But yeah, Topological optimization and generative design are really the same thing.

15

BeardySi t1_j8nu6g8 wrote

This. Design optimisation is design optimisation however you get there.

3

TrumpetSC2 t1_j8n1852 wrote

The NASA one is created by a genetic algorithm, not AI or machine learning

5

Randommaggy t1_j8njz0p wrote

Genetic algorithm is more accurately described as AI than most "AI" Tools out there.

6

TrumpetSC2 t1_j8nl8mf wrote

IDK I work in a lab that does genetic algorithms work and I think most of the grad students/profs working on GAs would oppose that point of view

7

Randommaggy t1_j8oi1yp wrote

How is a genetic algorithm that optimizes for a set of constraints fundamentally different from a GAN or reinforcement learning model except in implementation details and resource-efficiency?
The discriminative network in a GAN is the provider of constraints aka part of the training dataset or the measurer of fitness.
The generative network proposes solutions and refines it's weights based on the fitness of the output.

There are differences but the premise is more similar than dissimilar.

Your funding would also likely be better if you could convince people that it is a form of AI maybe branded as a subcategory of supervised reinforcement learning.

1

TrumpetSC2 t1_j8onhk8 wrote

There is a big terminology issue going on here.

A GA is fundamentally different from AI because a GA does a very specific thing: It evaluates a set of solutions (called a population) and uses some method to choose some of those to reproduce (selection) and then recombines some of them (crossover), and applies random changes (mutation) to generate the next population, and iterates hopefully increasing fitness over time. It is an algorithm for optimizing solutions, and is not specific to things like learning systems or neural nets.

GANs are neural networks trained in a specific process, where there are networks that are solving the problem and networks that are trying to generate difficult input, to put it simply.

Reinforcement learning is a broad learning approach that covers a ton of different learning algorithms all with their own secret sauce, and it can be applied to decision making agents of many kinds, including neural nets and AI systems, but also other things like simple robots with state machines.

It would be incredibly disingenuous to say GAs are AI/ML, equivalent to GANs or a kind of reinforcement learning because those things are all very different and specific in ways that they aren't compatible ideas.

For example, some GA researchers use GAs to generate patches to buggy code. This has nothing to do with learning, there is never a model of the program, the evolved solution is purely a patch description of code. It bears no resemblance to these other methods and has nothing to do with neural nets/ai/etc. It makes no sense to try to lump these things together when some are concepts, some are algorithms, some are specific neural network designs, all with different components, purposes, and applications.

Now they can be used in conjunction. Like if you have ever heard of NEAT, it is a GA for evolving neural networks, and the neural networks are AI/ML. Also you can evolve an agent for a reinforcement learning process, but they would be separate steps. Neither is a subset of the other.

4

ThirdEncounter t1_j8nsn96 wrote

Where's the intelligence in genetic evolution? Intelligence is an outcome of evolution, not part of it.

2

Randommaggy t1_j8ohpv5 wrote

Where is the intelligence in the glorified inverted indexes with result blending bolted to them that are paraded about these days?
Inventing authors and papers that sound plausible when asked for citations is a strong indication that the smoke and mirrors make people ascribe a lot of intelligence that is simply not there.

0