[acyou]

Nice! I have so many questions.. How stable is the injection molding process once it's fully proven out, up and running? Is it a bathtub curve shape, do defects keep randomly popping up?

What do you use on your end to label the ejector pin locations, parting lines, etc? Does this process use Hexagon software inputs to make that easier?

If you're not relying so much on a skilled operator, would you be using a CMM for dimensional inspection anyways, and then would this be better solved with a CMM? How can you get quality parts if you don't have a skilled operator anyways to set up the machine correctly and correct the defects? Are you ever going to be able to replace a good machine operator? Or this just helps reduce the inspection toil and burden? Do they usually need 100% inspection, or just periodic with binning?

Why do you want to target injection molded parts and not machined parts?

Don't most of these machines have the parts just fall in a bin, with no robot arm? Doesn't this seem like instead of paying a good injection mold tech, now you're paying for an injection mold tech and a robotics tech, if you have to program the arm path for every part setup?

How many defects are "dimensional" and how many are "cosmetic" ?

Can a defect detection model accept injection mold pressure curves as input? Isn't that a better data source for flash and underfilling?

Is this supposed to get retrofit, or go on new machines?

[lasermatts]

> Nice! I have so many questions.. How stable is the injection molding process once it's fully proven out, up and running? Is it a bathtub curve shape, do defects keep randomly popping up?

They tend to pop up randomly -- mold wear is a big one -- and that's a function of material selected for the mold itself (resin vs aluminum vs steel.)

> What do you use on your end to label the ejector pin locations, parting lines, etc? Does this process use Hexagon software inputs to make that easier?

Right now we have an in-house tool for this - but it's a bit painful on our end so we're always looking for good alternatives!

> If you're not relying so much on a skilled operator, would you be using a CMM for dimensional inspection anyways, and then would this be better solved with a CMM? How can you get quality parts if you don't have a skilled operator anyways to set up the machine correctly and correct the defects? Are you ever going to be able to replace a good machine operator? Or this just helps reduce the inspection toil and burden? Do they usually need 100% inspection, or just periodic with binning?

Injection molding is usually for mass manufacturing - think multiple parts coming in bursts every minute or so - which makes CMM a tough to integrate without way slowing down your line. There's also the case of big objects like bumpers and chairs that might not be easy to CMM. We're not shooting to replace machine operators - just make their lives easier. With injection molding our customers so far usually really want 100% inspection instead of sampling.

> Don't most of these machines have the parts just fall in a bin, with no robot arm? Doesn't this seem like instead of paying a good injection mold tech, now you're paying for an injection mold tech and a robotics tech, if you have to program the arm path for every part setup?

Depends on the shop! Some have automated packaging systems that someone has to stare at. Some are trying to add in automated packaging a build out a defect plan. Keep in mind you don't necessarily need a full robot to get bad parts off your line - a little shoving arm to just boot the bad parts off a conveyor works fine in some cases.

> How many defects are "dimensional" and how many are "cosmetic" ?

Varies wildly by design - but I'd say we see more cosmetic than dimensional. Maybe because the ratio of cosmetic surface to interface surface is fairly high.

> Can a defect detection model accept injection mold pressure curves as input? Isn't that a better data source for flash and underfilling?

I'll have to keep that in mind - it's a great idea. The hard part there is that you'll need a per-machine calibration and a lot of data collection. Could be good though!

> Is this supposed to get retrofit, or go on new machines?

Ideally both since it's a separate camera system, although I'd love to try to integrate with the machines themselves.

[acyou]

Thank you.

I have seen machines with visual pressure curve output on the operator screen for each part. I also think some machines have automatic pressure monitoring already built into the machine control, but it's certainly not transformer model based.

I didn't know they were using resin molds, that takes cheap aluminum prototype scale up mold to a whole new level.

Last time I checked, the mold design software itself has the same UI as 1999 AutoCAD.

How many images/angles can you effectively sample and compare on that hardware in a 30 second cycle time? How would you process images from more than one camera? If you have 8 cameras, can the defect recognition software run on 8 threads?

Are injection mold operators mostly located in low labor cost areas? Is any reshoring happening?

[lasermatts]

Injection molding houses are heavily concentrated in LCOL areas -- but it's a massive market, so, so much of modern materials are plastic that there's a lot that's done in the US/Canada/Mexico, in North America, and Germany/Italy/Austria.

For just the automotive industry, there are 120 injection molding contractors in Michigan alone. Onshoring and reshoring are desired for really customer facing parts -- you spend a lot of weight on packaging to mitigate scratches when you produce abroad then assemble domestically.

Staying with automotive, electrification is driving the injection molding industry -- as your weight shifts to "big battery with a shell around it" more of the total components of a vehicle are injected.

Zooming out of automotive, biomedical device packaging is a huge injection molded business that's stayed in the US and is growing.

[Slowki]

Steph here - each image takes about ~250ms on a small single board compute like an Nvidia Orin Nano. On something larger like an RTX 4080 GPU it's less than 100ms. Because we're running big models we can't really just spin out more threads ourselves, we throw them over to the GPU (or deep learning accelerator - depending on the platform) and the driver's internal scheduler decides how to get it done.

In a robotic packaging scenario most of the time is spent by the robot picking up the objects and moving them, so for a 30 second cycle we usually get less than a second to take multiple pictures and make a decision about the part. For a smaller number of images - like 4 - it's pretty easy to handle with cheap hardware like an Orin Nano or Orin NX. If we've got more images (like 8) and a tight time budget (like less than 2 seconds) we'd usually just bump up the hardware, like going to a higher tier of Nvidia's line of Orins or using compute with an RTX 4080 GPU or equivalent in it.