7 ms·

New AI tool calculates materials’ stress and strain based on photos

76 points by sizzle 6y ago

mygoodaccount 6y ago
Sorry, but I don't want a black box for stress testing bridges - or any other infrastructure for that matter. Do black boxes have their place in design? Sure. In validation? Absolutely not.
- mlavin 6y ago
  "Don't worry, the AI says the bridge is okay. We've got fresh filler and paint over all the recognizable cracks!"
  - elil17 6y ago
    We wrote “strong” on the bridge and now the AI thinks it’s indestructible
  - ineedasername 6y ago
    "Sorry about the broken skyscraper, a few workers through Mechanical Turk tagged polystyrene as indestructible"
- Guest42 6y ago
  True, it can be quite concerning, I know that PDE's have issues too but they have served very well over the years and will continue to do so. I think that there is no shortcut to having a solid understanding of the principles at play.
- deleted 6y ago
  [deleted]
- tryonenow 6y ago
  That's not the application. ML accelerated modeling allows for instantaneous iteration. It dramatically speeds up creative engineering and scientific work. You take the final product and run it through a classical simulator as a final QC - though in my experience (in a different domain, but similar principle) the ML model outputs tend to be smooth/continuous and ≈98% MSE accurate. Of course you need to carefully train your models to span the input space, but for finite element/finite difference modeling this is relatively straightforward.
  - dagw 6y ago
    ML model outputs tend to be smooth/continuous and ≈98% MSE accurate In my experience building and working with such models in similar domains, they tend to be right in all the easy and uninteresting cases where there are no problems and in all the cases where the problem is super obvious, and wrong in all the hard and subtle corner cases that you actually care about. The other problem is that traditional engineers hate models they don't understand. If you can't explain, at least roughly, why it gave the answer it gave, then they won't trust it and won't use it.
- elil17 6y ago
  I think (hope) techniques like this will provably be more useful for forensics and inspection than anything else.
  - LegitShady 6y ago
    agreed. in situations where you have pictures to analyze but no structure or greatly damaged structure, it makes sense. It might even be good to analyze inspection photos for regular inspections to draw attention to situations where there might be more strain than anticipated and a human could be flagged to inspect more closely.
- ampdepolymerase 6y ago
  A better way would be for the neural network to generate input parameters for Finite Element Method solvers.
- ineedasername 6y ago
  It doesn't appear to be stress testing anything, it's [edit: removed calculating] estimating where a structure or material is stressed. Stress testing is a physical process, and the article gives no indication that stress testing could or would be replaced but this. Edit: Further elaboration from the article: product designers, for example, could test the viability of their ideas before passing the project along to an engineering team This would seem to clearly indicate that the intended application is faster iteration in design, not a replacement for a rigorous engineering process that would include appropriate testing.
  - LegitShady 6y ago
    >calculating Estimating
    - ineedasername 6y ago
      thanks, corrected
- cartoonfoxes 6y ago
  They're useful for parallel construction. I.e. in identifying designs to be validated through more conventional and computationally-demanding methods.
- exporectomy 6y ago
  Then don't use it for that! Why so many naysayers dumping on interesting things. Engineers aren't idiots who're going to randomly rely on unknown techniques. Even traditional FEA is backed up by hand calculations. It's not even really a black box. It's learned a subset of Abaqus's functionality so it's scope is known.
tryonenow 6y ago
>The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time. This is pretty fresh tech, but industry is already using it. We are doing something approximately similar where I work. Instead of running compute intensive finite element/finite difference simulations of physically dependent systems, a neural network (typically something structured like a transformer) is trained to output the calculations up to 6 orders of magnitude faster in our applications. This changes this allows modeling dependent science and engineering solutions to be iterated over in real time - you can see the results of your edits as you manipulate your models. And the results, at least in our applications, have some ≈98 MSE. It isn't surprising in hindsight - deep neural networks are universal function approximations, and finite modeling is as close to pure mathematics as you can get in an industry setting. It feels like a perfect use case for deep neural nets.
- Chris2048 6y ago
  Couldn't the same thing be achieved with more approximate FIA?
  - tryonenow 6y ago
    Not without an impractical reduction in accuracy. And even then you still have to iterate over some number of timesteps, whereas output from a deep net is a single evaluation.
nestorD 6y ago
The model takes pictures, encoding the material structure, as inputs and not photographs. Seing the pictures in the article, the model produces very coarse results from very simple inputs.
jameshart 6y ago
> New AI tool calculates materials’ stress and strain based on photos Surely not - more accurately: New AI tool intuits materials’ stress and strain based on photos or New AI tool guesses materials’ stress and strain based on photos An experienced engineer could probably also guess roughly what shape the stress and strain gradients might take in a shape, but you wouldn't call such a guess 'calculation'.
- LegitShady 6y ago
  'estimates' This can be useful if you use it to automate review of regular inspection photos - you might be able to get the computer to recognize strain that would be hard to detect without a strain gauge.
- ew6082 6y ago
  Guesses poorly too, based on the examples. The crack growth example is almost laughable. These are nowhere close to where real cracks would form. Cracks start on the inside corner of brittle joints. The bar stretch is also completely wrong, there are no stress concentrations at the top and bottom; it should be uniform stress or concentrated strain in the center depending on what they're attempting to show.
  - inglor_cz 6y ago
    The question is whether it can get better. Even small steps aggregate to great changes over time. First CGA monitors were bulky, energy hungry and blurry at the same time. 40 years of continuous development, and I am staring at a nice 4K screen that does not strain my eyes and is substantially more energy efficient.
- anonytrary 6y ago
  True, nothing stops me from wrapping a brick in a T-shirt and fooling the algorithm. Might be useful as a higher level classifer or bucketing heuristic.
- r-zip 6y ago
  Thank you for bringing sanity to the comment section. I'm getting really weary of all the "AI" snake oil (I'm a data scientist). There's no way that this will generalize to: 1. materials not in the training set 2. stresses substantially different from those in the training set So basically, it just memorizes a few patterns and can interpolate between the ones it's seen before. Big deal. A good rule of thumb for detecting this variety of bullshit is this: given infinite time and resources, would an intelligent enough human be able to perform the task given the input? In this case, the answer is probably no.
ddalex 6y ago
I'm thinking the current-generation AI (deep CNNs, not symbol-based reasoning) are enough to be paradigm-changers in the future by allowing us to record empiric knowledge. Computers are good at recording structured knowledge - who owns that, this merch goes where, things that were recorded on stone tablets since times immemorial. But human experts also have a different type of knowledge, empiric, accumulated through years of experience. Artisan crafters "know" how good some material is by sense of touch, smell, sight, without always being able to say WHY or HOW this wood is better than that wood for this table. This is why apprenticeship with a master was a key part of developing an artisan - you'd transfer SOME of this empirical knowledge from a master that took a lifetime to develop it. The humanity took a huge step forward by moving a lot of empirical knowledge to structured knowledge through the use of models (mathematical?) and books. Instead of an apprentinceship with a mason, a builder now goes to school and learns how to structurally design a building based on construction codes. This allows huge scaling of knowledge, at the expense of missing subtle details which are not modelled (spherical cows in a void, right?). With this article it just occured to me that Deep Learning may be the tool to record this type of empirical knowledge. And it's going to scale out - unlike the human version never did - because digital copying is cheap, and the knowledge doesn't die with the artisan that developed it - it only gets more accurate. The models we build about reality will get more and more accurate - the spherical cows will grow legs in the air, not in a void. Humanity will start doing more and more things without understanding why, but because "the computer said so", and things work out when the computer says they will. Black boxes will explode in usage, and God forgive those that may be on the side where The computer says No.
- Sleepytime 6y ago
  > Humanity will start doing more and more things without understanding why, but because "the computer said so", and things work out when the computer says they will. Black boxes will explode in usage, and God forgive those that may be on the side where The computer says No. This paragraph scares me. I'm not convinced that technology getting good enough to where it doesn't need to be understood to be used is a good thing in most cases. Especially when it's a black box.
  - 6y ago

76 points by sizzle 6y ago

Sorry, but I don't want a black box for stress testing bridges - or any other infrastructure for that matter. Do black boxes have their place in design? Sure. In validation? Absolutely not.

mlavin 6y ago
"Don't worry, the AI says the bridge is okay. We've got fresh filler and paint over all the recognizable cracks!"
- elil17 6y ago
  We wrote “strong” on the bridge and now the AI thinks it’s indestructible
- ineedasername 6y ago
  "Sorry about the broken skyscraper, a few workers through Mechanical Turk tagged polystyrene as indestructible"
Guest42 6y ago
True, it can be quite concerning, I know that PDE's have issues too but they have served very well over the years and will continue to do so. I think that there is no shortcut to having a solid understanding of the principles at play.
deleted 6y ago
[deleted]
tryonenow 6y ago
That's not the application. ML accelerated modeling allows for instantaneous iteration. It dramatically speeds up creative engineering and scientific work. You take the final product and run it through a classical simulator as a final QC - though in my experience (in a different domain, but similar principle) the ML model outputs tend to be smooth/continuous and ≈98% MSE accurate. Of course you need to carefully train your models to span the input space, but for finite element/finite difference modeling this is relatively straightforward.
- dagw 6y ago
  ML model outputs tend to be smooth/continuous and ≈98% MSE accurate In my experience building and working with such models in similar domains, they tend to be right in all the easy and uninteresting cases where there are no problems and in all the cases where the problem is super obvious, and wrong in all the hard and subtle corner cases that you actually care about. The other problem is that traditional engineers hate models they don't understand. If you can't explain, at least roughly, why it gave the answer it gave, then they won't trust it and won't use it.
elil17 6y ago
I think (hope) techniques like this will provably be more useful for forensics and inspection than anything else.
- LegitShady 6y ago
  agreed. in situations where you have pictures to analyze but no structure or greatly damaged structure, it makes sense. It might even be good to analyze inspection photos for regular inspections to draw attention to situations where there might be more strain than anticipated and a human could be flagged to inspect more closely.
ampdepolymerase 6y ago
A better way would be for the neural network to generate input parameters for Finite Element Method solvers.
ineedasername 6y ago
It doesn't appear to be stress testing anything, it's [edit: removed calculating] estimating where a structure or material is stressed. Stress testing is a physical process, and the article gives no indication that stress testing could or would be replaced but this. Edit: Further elaboration from the article: product designers, for example, could test the viability of their ideas before passing the project along to an engineering team This would seem to clearly indicate that the intended application is faster iteration in design, not a replacement for a rigorous engineering process that would include appropriate testing.
- LegitShady 6y ago
  >calculating Estimating
  - ineedasername 6y ago
    thanks, corrected
cartoonfoxes 6y ago
They're useful for parallel construction. I.e. in identifying designs to be validated through more conventional and computationally-demanding methods.
exporectomy 6y ago
Then don't use it for that! Why so many naysayers dumping on interesting things. Engineers aren't idiots who're going to randomly rely on unknown techniques. Even traditional FEA is backed up by hand calculations. It's not even really a black box. It's learned a subset of Abaqus's functionality so it's scope is known.

tryonenow 6y ago

>The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time. This is pretty fresh tech, but industry is already using it. We are doing something approximately similar where I work. Instead of running compute intensive finite element/finite difference simulations of physically dependent systems, a neural network (typically something structured like a transformer) is trained to output the calculations up to 6 orders of magnitude faster in our applications. This changes this allows modeling dependent science and engineering solutions to be iterated over in real time - you can see the results of your edits as you manipulate your models. And the results, at least in our applications, have some ≈98 MSE. It isn't surprising in hindsight - deep neural networks are universal function approximations, and finite modeling is as close to pure mathematics as you can get in an industry setting. It feels like a perfect use case for deep neural nets.

Chris2048 6y ago
Couldn't the same thing be achieved with more approximate FIA?
- tryonenow 6y ago
  Not without an impractical reduction in accuracy. And even then you still have to iterate over some number of timesteps, whereas output from a deep net is a single evaluation.

nestorD 6y ago

The model takes pictures, encoding the material structure, as inputs and not photographs. Seing the pictures in the article, the model produces very coarse results from very simple inputs.

jameshart 6y ago

> New AI tool calculates materials’ stress and strain based on photos Surely not - more accurately: New AI tool intuits materials’ stress and strain based on photos or New AI tool guesses materials’ stress and strain based on photos An experienced engineer could probably also guess roughly what shape the stress and strain gradients might take in a shape, but you wouldn't call such a guess 'calculation'.

LegitShady 6y ago
'estimates' This can be useful if you use it to automate review of regular inspection photos - you might be able to get the computer to recognize strain that would be hard to detect without a strain gauge.
ew6082 6y ago
Guesses poorly too, based on the examples. The crack growth example is almost laughable. These are nowhere close to where real cracks would form. Cracks start on the inside corner of brittle joints. The bar stretch is also completely wrong, there are no stress concentrations at the top and bottom; it should be uniform stress or concentrated strain in the center depending on what they're attempting to show.
- inglor_cz 6y ago
  The question is whether it can get better. Even small steps aggregate to great changes over time. First CGA monitors were bulky, energy hungry and blurry at the same time. 40 years of continuous development, and I am staring at a nice 4K screen that does not strain my eyes and is substantially more energy efficient.
anonytrary 6y ago
True, nothing stops me from wrapping a brick in a T-shirt and fooling the algorithm. Might be useful as a higher level classifer or bucketing heuristic.
r-zip 6y ago
Thank you for bringing sanity to the comment section. I'm getting really weary of all the "AI" snake oil (I'm a data scientist). There's no way that this will generalize to: 1. materials not in the training set 2. stresses substantially different from those in the training set So basically, it just memorizes a few patterns and can interpolate between the ones it's seen before. Big deal. A good rule of thumb for detecting this variety of bullshit is this: given infinite time and resources, would an intelligent enough human be able to perform the task given the input? In this case, the answer is probably no.

ddalex 6y ago

I'm thinking the current-generation AI (deep CNNs, not symbol-based reasoning) are enough to be paradigm-changers in the future by allowing us to record empiric knowledge. Computers are good at recording structured knowledge - who owns that, this merch goes where, things that were recorded on stone tablets since times immemorial. But human experts also have a different type of knowledge, empiric, accumulated through years of experience. Artisan crafters "know" how good some material is by sense of touch, smell, sight, without always being able to say WHY or HOW this wood is better than that wood for this table. This is why apprenticeship with a master was a key part of developing an artisan - you'd transfer SOME of this empirical knowledge from a master that took a lifetime to develop it. The humanity took a huge step forward by moving a lot of empirical knowledge to structured knowledge through the use of models (mathematical?) and books. Instead of an apprentinceship with a mason, a builder now goes to school and learns how to structurally design a building based on construction codes. This allows huge scaling of knowledge, at the expense of missing subtle details which are not modelled (spherical cows in a void, right?). With this article it just occured to me that Deep Learning may be the tool to record this type of empirical knowledge. And it's going to scale out - unlike the human version never did - because digital copying is cheap, and the knowledge doesn't die with the artisan that developed it - it only gets more accurate. The models we build about reality will get more and more accurate - the spherical cows will grow legs in the air, not in a void. Humanity will start doing more and more things without understanding why, but because "the computer said so", and things work out when the computer says they will. Black boxes will explode in usage, and God forgive those that may be on the side where The computer says No.

Sleepytime 6y ago
> Humanity will start doing more and more things without understanding why, but because "the computer said so", and things work out when the computer says they will. Black boxes will explode in usage, and God forgive those that may be on the side where The computer says No. This paragraph scares me. I'm not convinced that technology getting good enough to where it doesn't need to be understood to be used is a good thing in most cases. Especially when it's a black box.
- 6y ago