Dr. Max Fomitchev-Zamilov

So the smaller it is, the better the fit. So I measure, yeah, the radius and the deviation from the center, and that's the calibration standard, the bead that the EMS was so kind. Yeah, thanks, yes, to provide. So I ran it through the MATLAB code that I developed, and I was able to recover its stated radius within the error of 40,

So the smaller it is, the better the fit. So I measure, yeah, the radius and the deviation from the center, and that's the calibration standard, the bead that the EMS was so kind. Yeah, thanks, yes, to provide. So I ran it through the MATLAB code that I developed, and I was able to recover its stated radius within the error of 40,

So the smaller it is, the better the fit. So I measure, yeah, the radius and the deviation from the center, and that's the calibration standard, the bead that the EMS was so kind. Yeah, thanks, yes, to provide. So I ran it through the MATLAB code that I developed, and I was able to recover its stated radius within the error of 40,

four ten-thousandths of a millimeter, which is slightly worse than the resolving ability of the machine. But, you know, given all of the rounding errors and stuff, it's only two ten-thousandths of an inch. And that's how precise my analysis is. You know, I cannot see anything more precise than two two-thousandths of an inch.

four ten-thousandths of a millimeter, which is slightly worse than the resolving ability of the machine. But, you know, given all of the rounding errors and stuff, it's only two ten-thousandths of an inch. And that's how precise my analysis is. You know, I cannot see anything more precise than two two-thousandths of an inch.

four ten-thousandths of a millimeter, which is slightly worse than the resolving ability of the machine. But, you know, given all of the rounding errors and stuff, it's only two ten-thousandths of an inch. And that's how precise my analysis is. You know, I cannot see anything more precise than two two-thousandths of an inch.

— Yeah, 0.2 thousandths, right. So if you get a number that's less, You still have to say, well, it's probably 0.2 because the algorithm is not resolving better. And these are a couple of vases. And I'm just blown away. I'm sitting here at Matt's lounge and I'm looking at these vases and I look at them. They all look fine.

— Yeah, 0.2 thousandths, right. So if you get a number that's less, You still have to say, well, it's probably 0.2 because the algorithm is not resolving better. And these are a couple of vases. And I'm just blown away. I'm sitting here at Matt's lounge and I'm looking at these vases and I look at them. They all look fine.

— Yeah, 0.2 thousandths, right. So if you get a number that's less, You still have to say, well, it's probably 0.2 because the algorithm is not resolving better. And these are a couple of vases. And I'm just blown away. I'm sitting here at Matt's lounge and I'm looking at these vases and I look at them. They all look fine.

When I eyeball them, it's very hard for me to tell that some are well-made and some aren't well-made. In fact, I'm looking at them now and I say, wow, I cannot believe that the analysis showed that this is well-made, but that one isn't. Because when I look at them, they look fine. So when you look at the model, the model for a precise vase obviously looks better. It's more like symmetric.

When I eyeball them, it's very hard for me to tell that some are well-made and some aren't well-made. In fact, I'm looking at them now and I say, wow, I cannot believe that the analysis showed that this is well-made, but that one isn't. Because when I look at them, they look fine. So when you look at the model, the model for a precise vase obviously looks better. It's more like symmetric.

When I eyeball them, it's very hard for me to tell that some are well-made and some aren't well-made. In fact, I'm looking at them now and I say, wow, I cannot believe that the analysis showed that this is well-made, but that one isn't. Because when I look at them, they look fine. So when you look at the model, the model for a precise vase obviously looks better. It's more like symmetric.

but it's not that much better. Still, you know, when you have this rendering over an STL file, it's easier to see imperfections in shape because, you know, the colors are gone, you know, the shading is uniform, and you can say, yeah, I mean, that's more symmetric than the other. But, you know, those are just two examples of what I deem precise and imprecise.

but it's not that much better. Still, you know, when you have this rendering over an STL file, it's easier to see imperfections in shape because, you know, the colors are gone, you know, the shading is uniform, and you can say, yeah, I mean, that's more symmetric than the other. But, you know, those are just two examples of what I deem precise and imprecise.

but it's not that much better. Still, you know, when you have this rendering over an STL file, it's easier to see imperfections in shape because, you know, the colors are gone, you know, the shading is uniform, and you can say, yeah, I mean, that's more symmetric than the other. But, you know, those are just two examples of what I deem precise and imprecise.

And here are the points that I get from scans. On top is the precise ways, at the bottom is not so precise. And I did have to exclude the lugs, the lug handles. And because, and that's where I spent a lot of time on. Because when you cut through the handles, your slice is no longer circular and your analysis, you know, my analysis is invalidated.

And here are the points that I get from scans. On top is the precise ways, at the bottom is not so precise. And I did have to exclude the lugs, the lug handles. And because, and that's where I spent a lot of time on. Because when you cut through the handles, your slice is no longer circular and your analysis, you know, my analysis is invalidated.

And here are the points that I get from scans. On top is the precise ways, at the bottom is not so precise. And I did have to exclude the lugs, the lug handles. And because, and that's where I spent a lot of time on. Because when you cut through the handles, your slice is no longer circular and your analysis, you know, my analysis is invalidated.

And when you approach the handle, the quality of machining gets worse because where that handle meets the vase, you know, there is a ledge and it's not that well polished off. And initially I was just, you know, get rid of that manually. And then I started thinking, well, gee, you know, here's a typical example of a selection effect. You look at a data point, you don't like it, you discard it.

And when you approach the handle, the quality of machining gets worse because where that handle meets the vase, you know, there is a ledge and it's not that well polished off. And initially I was just, you know, get rid of that manually. And then I started thinking, well, gee, you know, here's a typical example of a selection effect. You look at a data point, you don't like it, you discard it.