The key part of the Autofac, the part that kept it from being built before, is the AI that runs it.
That’s what’s doing the work here.
We can’t automate machining because an AI that can control a robot arm to do typical machinist things (EG:changing cutting tool inserts, removing stringy steel-wool-like tangles of chips, etc.) doesn’t exist or is not deployed.
If you have a robot arm + software solution that can do that it would massively drop operational costs which would lead to exponential growth.
The core problem is that currently we need the humans there.
To give concrete examples, a previous company where I worked had been trying to fully automate production for more than a decade. They had robotic machining cells with machine tools, parts cleaners and coordinate measuring machines to measure finished parts. Normal production was mostly automated in the sense that hands off production runs of 6+ hours were common, though particular cells might be needy and require frequent attention.
What humans had to do:
Changing cutting tools isn’t automated. An operator goes in with a screwdriver and box of carbide inserts 1-2x per shift.
The operators do a 30-60 minute setup to get part dimensions on size after changing inserts.
Rough machining can zero tools outside the machine since their tolerances are larger but someone is sitting there with a T-handle wrench so the 5-10 inserts on an indexable end mill have fresh cutting edges.
Intermittent problems operators handled:
A part isn’t perfectly clean when measuring. Bad measurement leads to bad adjustment and 1-2 parts are scrap
chips are too stringy and tangle up, clear the tangle every 15 mins
chips are getting between a part and fixture and messing up alignment, clean intermittently and pray.
That’s ignoring stupider stuff like:
Our measurement data processing software just ate the data for a production run so we have to stop production and remeasure 100 parts.
someone accidentally deleted some files so (same)
We don’t have the CAM done for this part scheduled to be produced so … find something else we can run or sit idle.
The employee running a CAM software workflow didn’t double-check the tool-paths and there’s a collision.
And that’s before you get to maintenance issues and (arguably) design defects in the machines themselves leading to frequent breakdowns.
The vision was that a completely automated system would respond to customer orders and schedule parts to be produced with AGVs carrying parts between operations. In practice the AGVs sta there for 10+ years because even the simple things proved nearly impossible to automate completely.
Conclusion
Despite all the problems, the automated cells were more productive than manual production (robots are consistent and don’t need breaks) and the company was making a lot of money. Not great automation is much much better than using manual operators.
It’s hard to grasp how much everything just barely works until you’ve spent a year somewhere that is trying to automate. Barely works is the standard in industry AFAIK so humans are still highly necessary.
It is, in theory, possible to automate almost everything and to build reliable machines and automation. The problem is O-ring theory of economic development. If tomorrow median IQ jumps +2SD automation would rapidly start to just work as actually good solutions are put in place. As is, organizations have to fight against institutional knowledge loss (employees leaving) just to maintain competence.
Yes, absolutely! A fine description of the current state of the art. I upvoted your post by 6 points (didn’t know I could do that!).
I’m imagining doing everything the machinist has to do with a mobile pair of robot arms. I can imagine a robot doing everything you listed in your first list of problems. Your “stupider stuff” is all software problems, so will be fixed once, centrally, and for good on the Autofac. The developers can debug their software as it fails, which is not a luxury machinists enjoy.
Call a problem that requires human input a “tough” problem. We can feed the solutions to any tough problems back into the model, using fine-tuning or putting it in the prompt. So ideally, any tough problem will have to be solved once. Or a small number of times, if the VLM is bad at generalizing. The longer we run the Autofacs, the more tough problems we hit, resolve, and never see again. With an exponentially increasing number of Autofacs, we might have to solve an exponentially increasing number of tough problems. This is infeasible and will destroy the scheme. We have to hope that the tough problems per hour per Autofac drops faster than the number of Autofacs increases. It’s a hope and only a hope—I can’t prove it’s the case.
What’s your feeling about the distribution of tough problems?
TLDR:autofac requires solving “make (almost) arbitrary metal parts” problem but that won’t close the loop. Hard problem is building automated/robust re-implementation of some of the economy requiring engineering effort not trial and error. Bottleneck is that including for autofac. Need STEM AI (Engineering AI mostly). Once that happens, economy gets taken over and grows rapidly as things start to actually work.
better CAD/CAM (this is still mostly unsolved (CF:white collar CAD/CAM workers))
AI for tricky robotics stuff
estimate:100-1000 engineer years of labor from 99th percentile engineers.
Median engineers are counterproductive as demonstrated by current automation efforts not working.
EG:we had two robots develop “nerve damage” type repetitive strain injury because badly routed wires flexed too much. If designers aren’t careful/mindful of all design details things won’t be reliable.
This extends to sub-components.
Closing the loop needs much more than just “make (almost) arbitrary metal parts”. “build a steel mill and wire drawing equipment”, is just the start. There are too many vitamins needed representing unimplemented processes
raw materials production (rock --> metal) and associated equipment
wire
Those in turn imply other things like:
refractory materials for furnaces
corrosion resistant coatings (nickel?, chromium?)
non-traditional machining (try making a wire drawing die with a milling machine/lathe)
ECM/EDM is unavoidable for many things
Things just snowball from there.
Efficiency improvements like carbide+coatings for cutting tools are also economically justified.
All of this is possible to design/build into an even bigger self-reproducing automated system but requires more engineer-hours put into a truly enormous git repo.
STEM AI development (“E” emphasis) is the enabler.
Addendum: simplifying the machine tools and robots
Simplifications can be made to cut down on vitamin cost of machine tools. Hydraulics really helps IMO:
servohydraulics for most motion (EG:linear machine tool axes, robots) to cut down on motor sizes and simplify manufacturing
efficiency is worse, but saves enormously on power electronics and manufacturing complexity.
Similar principles to hydraulic power steering used in cars.
Boston Dynamics ATLAS Robot uses rotary equivalent for joints (I think).
hydrostatic bearings for rotary/linear motion in machine tools.
No hardened metal parts like in ball/roller bearings
Feeding fluid without flexible hoses across linear axes via structure similar to a double ended hydraulic cylinder. Just need two sliding rod seals and a hollow rod. Hole in the middle of the center rod lets fluid into the space between the rod seals.
Both bearings and motion can run off a single shared high pressure oil supply. Treat it like electricity/compressed-air and use one big pump for lots of machines/robots.
End result: machine tools with big spindle motors and small control motors for all axes. Robots use rotary equivalent. Massive reduction in per-axis power electronics, no ballscrews, no robot joint gears.
For Linear/rotary position encoders, calibrated capacitive encoders (same as used in digital calipers) are simple and needs just PCB manufacturing. Optical barcode based systems are also attractive but require an optical mouse worth of electronics/optics per axis, and maybe glass optics too.
That’s what’s doing the work here.
We can’t automate machining because an AI that can control a robot arm to do typical machinist things (EG:changing cutting tool inserts, removing stringy steel-wool-like tangles of chips, etc.) doesn’t exist or is not deployed.
If you have a robot arm + software solution that can do that it would massively drop operational costs which would lead to exponential growth.
The core problem is that currently we need the humans there.
To give concrete examples, a previous company where I worked had been trying to fully automate production for more than a decade. They had robotic machining cells with machine tools, parts cleaners and coordinate measuring machines to measure finished parts. Normal production was mostly automated in the sense that hands off production runs of 6+ hours were common, though particular cells might be needy and require frequent attention.
What humans had to do:
Changing cutting tools isn’t automated. An operator goes in with a screwdriver and box of carbide inserts 1-2x per shift.
The operators do a 30-60 minute setup to get part dimensions on size after changing inserts.
Rough machining can zero tools outside the machine since their tolerances are larger but someone is sitting there with a T-handle wrench so the 5-10 inserts on an indexable end mill have fresh cutting edges.
Intermittent problems operators handled:
A part isn’t perfectly clean when measuring. Bad measurement leads to bad adjustment and 1-2 parts are scrap
chips are too stringy and tangle up, clear the tangle every 15 mins
chips are getting between a part and fixture and messing up alignment, clean intermittently and pray.
That’s ignoring stupider stuff like:
Our measurement data processing software just ate the data for a production run so we have to stop production and remeasure 100 parts.
someone accidentally deleted some files so (same)
We don’t have the CAM done for this part scheduled to be produced so … find something else we can run or sit idle.
The employee running a CAM software workflow didn’t double-check the tool-paths and there’s a collision.
And that’s before you get to maintenance issues and (arguably) design defects in the machines themselves leading to frequent breakdowns.
The vision was that a completely automated system would respond to customer orders and schedule parts to be produced with AGVs carrying parts between operations. In practice the AGVs sta there for 10+ years because even the simple things proved nearly impossible to automate completely.
Conclusion
Despite all the problems, the automated cells were more productive than manual production (robots are consistent and don’t need breaks) and the company was making a lot of money. Not great automation is much much better than using manual operators.
It’s hard to grasp how much everything just barely works until you’ve spent a year somewhere that is trying to automate. Barely works is the standard in industry AFAIK so humans are still highly necessary.
It is, in theory, possible to automate almost everything and to build reliable machines and automation. The problem is O-ring theory of economic development. If tomorrow median IQ jumps +2SD automation would rapidly start to just work as actually good solutions are put in place. As is, organizations have to fight against institutional knowledge loss (employees leaving) just to maintain competence.
Yes, absolutely! A fine description of the current state of the art. I upvoted your post by 6 points (didn’t know I could do that!).
I’m imagining doing everything the machinist has to do with a mobile pair of robot arms. I can imagine a robot doing everything you listed in your first list of problems. Your “stupider stuff” is all software problems, so will be fixed once, centrally, and for good on the Autofac. The developers can debug their software as it fails, which is not a luxury machinists enjoy.
Call a problem that requires human input a “tough” problem. We can feed the solutions to any tough problems back into the model, using fine-tuning or putting it in the prompt. So ideally, any tough problem will have to be solved once. Or a small number of times, if the VLM is bad at generalizing. The longer we run the Autofacs, the more tough problems we hit, resolve, and never see again. With an exponentially increasing number of Autofacs, we might have to solve an exponentially increasing number of tough problems. This is infeasible and will destroy the scheme. We have to hope that the tough problems per hour per Autofac drops faster than the number of Autofacs increases. It’s a hope and only a hope—I can’t prove it’s the case.
What’s your feeling about the distribution of tough problems?
TLDR:autofac requires solving “make (almost) arbitrary metal parts” problem but that won’t close the loop. Hard problem is building automated/robust re-implementation of some of the economy requiring engineering effort not trial and error. Bottleneck is that including for autofac. Need STEM AI (Engineering AI mostly). Once that happens, economy gets taken over and grows rapidly as things start to actually work.
To expand on that:
“make (almost) arbitrary metal parts”
can generate a lot of economic value
requires essentially giant github repo of:
hardware designs:machine tools, robots, electronics
software:for machines/electronics, non-ai automation
better CAD/CAM (this is still mostly unsolved (CF:white collar CAD/CAM workers))
AI for tricky robotics stuff
estimate:100-1000 engineer years of labor from 99th percentile engineers.
Median engineers are counterproductive as demonstrated by current automation efforts not working.
EG:we had two robots develop “nerve damage” type repetitive strain injury because badly routed wires flexed too much. If designers aren’t careful/mindful of all design details things won’t be reliable.
This extends to sub-components.
Closing the loop needs much more than just “make (almost) arbitrary metal parts”. “build a steel mill and wire drawing equipment”, is just the start. There are too many vitamins needed representing unimplemented processes
A minimalist industrial core needs things like:
PCB/electronics manufacturing (including components)
IC manufacturing is its own mess
a lot of chemistry for plastics/lubricants
raw materials production (rock --> metal) and associated equipment
wire
Those in turn imply other things like:
refractory materials for furnaces
corrosion resistant coatings (nickel?, chromium?)
non-traditional machining (try making a wire drawing die with a milling machine/lathe)
ECM/EDM is unavoidable for many things
Things just snowball from there.
Efficiency improvements like carbide+coatings for cutting tools are also economically justified.
All of this is possible to design/build into an even bigger self-reproducing automated system but requires more engineer-hours put into a truly enormous git repo.
STEM AI development (“E” emphasis) is the enabler.
Addendum: simplifying the machine tools and robots
Simplifications can be made to cut down on vitamin cost of machine tools. Hydraulics really helps IMO:
servohydraulics for most motion (EG:linear machine tool axes, robots) to cut down on motor sizes and simplify manufacturing
efficiency is worse, but saves enormously on power electronics and manufacturing complexity.
Similar principles to hydraulic power steering used in cars.
Boston Dynamics ATLAS Robot uses rotary equivalent for joints (I think).
https://www.researchgate.net/figure/Comparison-of-the-Hy-Mo-actuator-with-traditional-hydraulic-actuator-52_fig5_346755993
hydrostatic bearings for rotary/linear motion in machine tools.
No hardened metal parts like in ball/roller bearings
Feeding fluid without flexible hoses across linear axes via structure similar to a double ended hydraulic cylinder. Just need two sliding rod seals and a hollow rod. Hole in the middle of the center rod lets fluid into the space between the rod seals.
Both bearings and motion can run off a single shared high pressure oil supply. Treat it like electricity/compressed-air and use one big pump for lots of machines/robots.
End result: machine tools with big spindle motors and small control motors for all axes. Robots use rotary equivalent. Massive reduction in per-axis power electronics, no ballscrews, no robot joint gears.
For Linear/rotary position encoders, calibrated capacitive encoders (same as used in digital calipers) are simple and needs just PCB manufacturing. Optical barcode based systems are also attractive but require an optical mouse worth of electronics/optics per axis, and maybe glass optics too.