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  • dang133
  • dang133
Today 23:56
Paid Consulting Opportunity was created by dang133

Paid Consulting Opportunity

Category: QtPyVCP

Hi everyone,

My team is using ProbeBasic / Linux CNC on a 5-axis CNC milling machine we are developing.  We've got most everything up, but don't have the INI file for the machine working properly.  To be specific, we're getting a lot of out of bounds and tool change errors.  Many (most) of the example NGC files that come with LinuxCNC don't work.  We fix one thing just to run into another blocker.  We could use some paid help.

If you're interested, email This email address is being protected from spambots. You need JavaScript enabled to view it. with the following:
  • Your name / email / phone
  • Resume (highlighting LinuxCNC work)
  • Your general coding / management style
  • What time zone you're in
  • Your desired rate

We'll get back you super soon.
Thanks so much!
  • rodw
  • rodw's Avatar
Today 23:42
Replied by rodw on topic Probe Z offset

Probe Z offset

Category: QtPyVCP

yeh, pretty sure its the same as qrdragon. Define your probe as a tool and record the height as you would any other tool, I used T99 for the probe.
  • rodw
  • rodw's Avatar
Today 23:32

Industrielle Linux CNC 5achs Fräsmaschine

Category: General LinuxCNC Questions

In my opinion, Ethercat is a great solution for small machines that can use integrated drives.
I have mills where the individual axes are connected to the rest of the machine via a one patch cord and one power cord, and things like homing limit switches  or other sensors are connected locally to additional drive inputs.
Such a machine is simple to wire and, in my opinion, more reliable.
However, if all the control is to be located in one cabinet and long and multi-core cables are to be run, Ethercat loses its advantages for now. It's just expensive and difficult to set up, and what's best about it becomes secondary...

Nonsense. ethercat drives whole factories. The advantages of ethercat remain regardless of the size of the machine. Ethercat should be a more efficient protocol than mesa hm2 so it should have more efficient bandwidth utilisation. Linuxcnc won't care if you use mesa or ethercat

 
  • RLA
  • RLA
Today 22:56 - Today 23:00
Replied by RLA on topic 5 axis tool offset

5 axis tool offset

Category: CAD CAM

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[/img]
  • RLA
  • RLA
Today 21:57
Replied by RLA on topic 5 axis tool offset

5 axis tool offset

Category: CAD CAM

I am not sure if tcp will help here..see attached pic...maybe not clear but difference in green and yellow rectangle show difference between tool center and reusuting cut of a profile cutter where cutting edge is offset from center of tool..seems like b axis would need more z height to give correct cut on both sides of stock.
 
  • Marlie013
  • Marlie013
Today 21:48
Replied by Marlie013 on topic Anilam 1100 and Mesa 7i97t

Anilam 1100 and Mesa 7i97t

Category: Milling Machines

I hope these help! The wires pretty much go straight down from the REV A board to the PCB board. I appreciate you taking the time to help me!


 
  • dbtayl
  • dbtayl
Today 21:19
Replied by dbtayl on topic Probe Z offset

Probe Z offset

Category: QtPyVCP

I don't know if Probe Basic does it differently, but the probe I use with the Axis UI is configured as a tool in the tool table. The length offset then gets applied like every other tool
  • tommylight
  • tommylight's Avatar
Today 20:35
Replied by tommylight on topic Anilam 1100 and Mesa 7i97t

Anilam 1100 and Mesa 7i97t

Category: Milling Machines

I believe the drive enables are wired, it's the light blue wire hidden under the grey wires in the back.

Oh yeah, i could not see it as it is obfuscated by the rest of the wiring.
More pictures, following the wiring from one board to the other, and the other if they diverge, etc.
Also the back side of the board, if possible, would help.
  • Marlie013
  • Marlie013
Today 20:09
Replied by Marlie013 on topic Anilam 1100 and Mesa 7i97t

Anilam 1100 and Mesa 7i97t

Category: Milling Machines

I believe the drive enables are wired, it's the light blue wire hidden under the grey wires in the back.
I have no schematics, and am unsure where to look.
I'm trying to figure out the wiring off the original power distribution board, which is the last picture.
  • Estley
  • Estley
Today 19:59
Probe Z offset was created by Estley

Probe Z offset

Category: QtPyVCP

Apologies for the use of improper terminology, newbie here. So, I have a PrintNC machine with Probe Basic. For the most part the setup has been intuitive, and most of the questions i've had have already been answered here. The one thing I'm a bit confused about is probing, I have a tp06 probe, and I've been using it to find corners, bosses, bores etc. So one of the settings is the tip diameter of the probe, which is perfectly clear to me as to why it's needed. The one thing I cant figure out is, isn't the height of the probe also needed? In my mind, i can bring the spindle (no tool) down until it touches the spoilboard, and record the Z height at the point (Z0). Then I cant put the probe in the spindle, and lower it until the spindle touches the spoil board and record that Z (Z1). From there the probe offset is Z0-Z1 (or the other way around), My question is, where do i configure that probe offset? is it a variable in the .var file?
  • tommylight
  • tommylight's Avatar
Today 19:58

img2m67 - Image to M67 Analog Out (PWM) for laser raster

Category: CAD CAM

Seems interesting, but i have no clue when i'll have some time to test.
I have scratchers i build and use dmap2gcode for it modified to output M67 values, works perfectly fine for my needs, and also works for an UV laser i have collecting dust in the shop (makes to much smoke, unhealthy), so i am interested to see how much the results would improve.
My scratchers:
forum.linuxcnc.org/show-your-stuff/46752...ver-etcher-scratcher
forum.linuxcnc.org/show-your-stuff/37784...-or-any-hard-surface
  • tommylight
  • tommylight's Avatar
Today 19:50
Replied by tommylight on topic Anilam 1100 and Mesa 7i97t

Anilam 1100 and Mesa 7i97t

Category: Milling Machines

First, do not power on the drives, if i ma seeing properly, the drive enables are not wired on the Mesa, so again, do not power on the drives.
Do you have any schematics of the machine?
If not, it will require a bit of reverse engineering to figure out.
About wiring and testing, see here:
forum.linuxcnc.org/10-advanced-configura...to-example-mesa-7i77
  • rdtsc
  • rdtsc's Avatar
Today 18:36

img2m67 - Image to M67 Analog Out (PWM) for laser raster

Category: CAD CAM

Good day.  I've created a PyQt6 app to convert an image file into rasterized M67 gcode output.  It renders .svg files to 16-bit depth; check it out: github.com/mj1911/img2m67

 
  • tuxcnc
  • tuxcnc
Today 18:00

Kommunikation mit Mesa 7i92 sehr instabil, Woran kann es liegen?

Category: Deutsch

Try apt install r8168-dkms
You have to enable  non-free tree in sources-list.
As I wrote above, sometimes it not works with some kernel versions, but can work with the previous or next version (it means, for example, 6.12.xx not works, but 6.12.yy works).
  • muddiver
  • muddiver
Today 17:37

Kommunikation mit Mesa 7i92 sehr instabil, Woran kann es liegen?

Category: Deutsch

Which driver should i go for?

The command "lspci -nnk | grep -iA3 net " 
tells me i have a "RTL8111/8168/8211/8411" , i read something about changing the driver to the 8168, but i'm unsure..

Other thing, if it is a bit thing to get it running, what do you think about these small computers for LinuxCNC?
They are not too expensive..
Lenovo tiny M92? Which CPU would you recommend at Least?
I learned the hard way to get a INtel-Networkchip, so i will definitly have an eye on this!
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