Thanks, d hello everyone, I’m Neel, with the PHD in mechanical engineering. I have journeyed from research and Engineering roles in our product management, so more skin in the game, so to speak. Here, I’m today representing intact. So imagine if you could virtually test your part at any stage in product development, get immediate feedback on your decisions you have to make, so that’s the question we’re asking, and how that would impact product development. At intact we have been developing such a simulation technology that allows you to do this kind of validation quickly, and in today’s talk I plan to share our vision of how this technology can truly change product development.

0:49 So simulation has been playing a critical role in product development, but today I would say mostly it’s used in the initial phas, prodct design, mainly used to validate design, to study design changes. But as we are seeing new trends in in in product development, the role of simulation will will evolve, and we think it’ll even get more critical. So what are the some of these Trends? So first, the reason why we are here, computational and generative design. So we we are now able to generate, as we all saw, these very complicated designs, but trying to simulate these through traditional M Bas systems is still very challenging. On top of that, we have large design spaces to explore, now we have hundreds of parameters, how do you quickly, effectively navigate these design spaces? We will need feedback from simulation, because we can’t print and test everything. And secondly, we also have now algorithms doing some of the design for us, but if the designs are not supposed to just be aesthetically pleasing, but also functional, you need some sort of physics feedback to be able to provide to the algorithm to do these designs effectively.

2:08 Another trend is, again, additive and composit Manufacturing. This is giving us tremendous freedom to make different kinds of geometry and material, but with that freedom comes the challenge of managing the process parameters, again, we have a lot of process parameters you need to decide, and these process parameters not only affect the geometry tolerance, but they also affect the material, so it does affect the performance. And what what if we are able to, when we are designing, think about the process itself and what kind of material you are getting, and incorporate that that in your design, this helps you to risk product development early on.

2:52 Another Trend, digital twin, digital thread, again everything is getting captured, scanned, simulated, manufacturing process, a lot of sensors, so you have all these data coming in, and we have a virtual twin of the as manufactured part, but these twins have to be grounded in physics, in reality, so simulation will play an important role there as well, and we have this all these data, and if you’re able to feed that back to design, and again do risk product development early on. And last but not the least, we have artificial intellig, we already saw today a lot of speakers talking about how it’s affecting their design processes, and we know that AI will rely heavily on vast amounts of data, and generating these physics based data through simulation will significantly enhance these AI models.

3:47 So simulation is critical, but what kind of simulation? So this simulation cannot be manual fiddling around, it has to be robust, automated, and interoperable. By in interoperable, I mean it has to be able to work with different types of representations seamlessly. So the question is, is the current simulation up to the task, and the short answer is no. Why? Because we have big automation barriers in in the name of meshing and pre-processing. So anyone who has analyzed, used FAA before, you know that even before you start simul ating, you have to clean up your geometry, simplify, remove features, and if you dealing with some of the new representations, implicit cities can, you have to first convert them into a boundary representation, a mesh, and then, if your complexity of your part is extremely high, then, for example, the exite bracket thanks Ryan for sharing the part, and also sharing the physical part actually. So these these simulations will just it just breaks old traditional simulation. And once you don’t have an automated robust simulation, you can’t do those things effectively, you can’t iterate quickly for exploring large design spaces, or automatically doing optimization Loops, digital thread, large scale data generation for AI. So that’s where we come in, that’s what we’re trying to do here.

5:24 So at intact we have developed this simulation technology that eliminates the the processing bottlenecks we talked about, through sort of separating the geometry from simulation. So the geometry lives in its own place, and at one time we have a a grid, the simulation space, which does all the heavy lifting. So we call this the common neutral format, so throughout your product life cycle, at every stage you will have your Digital model part, but in some format, some representation, the goal is that all of them should be able to be simulated directly, without conversion, cleanup, minimizing them as much as possible. So, and not only that, the way the common neutral format represents the data, it can be easily fed into existing CA solvers to to be basically solve your linear system and get your physics feedback. These solvers can be commercial, open source, we have our own solvers, and we also work with our customer solvers. And once everything is automated and robust, and that’s how you put everything in a loop, and you get continuous Improvement in product development.

6:47 So now that we have seen our technology, how IT addresses the limitations of existing simulation, let me take a moment to talk about our company, intact. So we have been around for a while, actually 25 years this this year, we have been mostly doing R&D work in engineering simulation, especially for Advanced manufacturing and and generative. So in our second generation we expanded to SDK and got into s partner products, but finally, this is our third generation now, we have our end user product, the big one is our automation product, where we have python API and command line interface face to do headless mode, as well as we have integration with our computational design platform Partners. We we’ll go into these in the in the next slides, but let’s see what kind of research we are doing to support simulation driven continuous engineering.

7:48 So for the first example here, we have our emerging technology, intact additive, where, out of many things, one of the things we’re doing is part pre-qualification, by integrating diverse data from every stage, from a lot of stages of the product development. So you have CAD data, process data, as well as test data, all fused together at runtime, to give a very accurate model of what your as manufactured part will be. So we taking the G-Code, the way the the material was deposited, and how much material was deposited, getting the orthotropic properties from testing, and aligning that material properties with the deposited material. So very, very high fidelity model of your manufactured part, and we’re able to fuse them together and get a very High Fidelity results.

The second R&D is in the generative area, where we have, again, we we are leveraging our the simulation space separate from geometry. So we are able to do very high fidelity simulation for optimization, which can give very accurate stresses, which is a problem in density based methods, and the result is that we get very smooth geometry that requires minimal cleanup and and design translation. And not only that, compliance minimization often gives you high stress regions, because it doesn’t really model stresses correctly, so without technology, you can also do stress minimization problems.

9:23 And our last research example is a a a funding we just got from DARPA, where we are we have proposed to do concurrent part and process design. So traditionally, when you do a optimization of your part, you get something like at the first top image, but now we’re also looking at how we can design both the geometry and the process at the same time, because in this example is for powerit Fusion, where the laser parameters affect the material, and in in and in those kind of processes, the material is heterogeneous, you have internal Zone, boundary Zone, and and and depends on the laser parameters. So when you incorporate those with the geometry, you get very different design, as you see on the far side.

10:16 So those are some of the researches we research we’re doing, but let’s let’s go into our product. So some of the research we have now turned into product, prod, at intact. One of them is intact Simulation, which is a fast, automated, and integrated simulation for computational design. The first one is our automation product, so again, the python API and the command line interface allows you to very easily integrate, bring simulation in your existing design workflows. So an example here is our friends at mod laab connected, they created this architected metam material Pipeline and brought and integrated with us to get the physics, physics feedback to help them navigate the design space for better materials. U, and all of this is done in the dini as as the base.

11:13 And yeah, and the second example is, again, a batch processing of a large data set done very quickly, it’s very fast, it will it will allow you to get design insights Within hours. So for example, you can see these simulations runs in a few seconds, and on my laptop, and so you almost generate thousands of studies in an hour. We also brought Integrations with several of the leading computational platforms, as we saw, Rhino grasshopper is very popular, and this was our first platform we we came to. So now industrial designers can have simulation in the which which is native to grasshopper, so you don’t have to bring external simulation, it’s fully integrated, all the blocks are there, and you can create your automated design pipelines extremely easy.

12:07 So here, this is a pipeline to design shoe, shoe SS, you bring in pressure data from sensors, customized individuals, map them to boundary conditions, have different types of boundary conditions for different stages of walking, use those results and fine-tune the design of the so, for that particular person. So all of that is feasible because it’s automated, native to the grasshopper.

12:37 Another integration, our second integration, is for Sena, we heard earlier this morning from Andrew. So Sena is a process automation tool for engineers, it’s they are trying to automate all these complex workflows, but the machine challenge Still Remains, if the machine breaks when they are doing design of experiments with hundreds of designs, you can’t really automate that. So, so we brought intact natively inside, again, CA, you can see how simple it is, the component just takes the cad, whatever geometry type it is, there’s no mesh involved, feed in the material, the boundary conditions are from Sena, we take those in, and within seconds you have results, and you can do large design studies very quickly.

13:26 And last but not least, we just today released our integration with the enta, it was it is out of beta now and now commercially available. So we know antop, you can generate complex implicit models without meshing. U, and now we have an intact inside mtop, again, you make your design changes and results are quickly shown within end up. So basically, they’re blazing fast implicit engine, now paired with our lasing fast simulation engine. So special thanks to and for helping us build this integration, as well as Blake, to get this collaboration started.

14:15 And finally, we’ll look at some of our what work our customers are doing. So first example is from Moon rabbit, so the moon rabbit adaptive lab, they talked actually at CD famam in Berlin, so they have been pushing design using simulation, and especially using intact within grasshopper. The first example is, again, high performance shoe design for structural design, and, again, on the far side is a helmet where simulation was used to identify compliance zones, again, it’s a lce structure, quite complicated inside. So Moon rabbit has been doing some great work.

15:03 This morning you saw a presentation from slice live, Arthur and Diego, they have been designing thads, and again, intact has been helping them test, virtually test, these designs to help decide on the parameters for for better performance. We have friends from sine here too, so they have been also struggling with meshing problem, again, because they they are special, they have specialized in adaptive density minimal surfaces, metam material, so they can they’re conformal and they generate quite isotropic stresses, but they’re also adaptive, so you can’t really s simplify this into one material, so you have to analyze the whole thing, and then trying to mesh, this is just nightmare. But we have been helping them simulate performance of the parts, the high performance parts they’re designing, and we can can share some of the other cool studies, but some special shout out to aloid and CDs for their collaboration too.

16:07 So to conclude, automated and robust simulation will be critical, given all these new trends we are seeing, and a testing framework with automated simulation in the background will enable this continuous Engineering in product development as well, the way it has done in software, for example. So we we saw saw how intact has addressed the bottlenecks of existing simulation, and basically help us realize the vision of continuous engineering, and we have a lot of research going on, in addition to the product I already showed you. So with that, I’ll conclude my talk. Thank you.