A Moonraker plug-in for real-time compensation of frame thermal expansion

Overview

Frame Expansion Compensation

A Moonraker plug-in for real-time compensation of frame thermal expansion.

Installation

Credit to protoloft, from whom I plagarized in near entirety the install.sh script -> Z Auto Calibration


Clone this repo into you home directory. For example:

cd /home/pi
git clone https://github.com/alchemyEngine/klipper_frame_expansion_comp

Copy the frame_expansion_compensation.py module to the Klippy extras folder:

cp /home/pi/klipper_frame_expansion_comp/frame_expansion_compensation.py /home/pi/klipper/klippy/extras/

[Optional] Configure Moonraker Updates

Run the install shell script:

bash /home/pi/klipper_frame_expansion_comp/install.sh

Configure the update manager. Add the following section to moonraker.conf:

[update_manager client frame_expansion]
type: git_repo
path: /home/pi/klipper_frame_expansion_comp
primary_branch: main
origin: https://github.com/alchemyEngine/klipper_frame_expansion_comp.git
install_script: install.sh

Configuration

[frame_expansion_compensation]
#temp_coeff:
#   The temperature coefficient of expansion, in mm/K. For example, a
#   temp_coeff of 0.01 mm/K will move the Z axis downwards by 0.01 mm for every
#   Kelvin/degree celcius that the frame temperature increases. Defaults to 0.0,
#   no offset.
temp_sensor:
#   Temperature sensor to use for frame temp measurement. Use full config
#   section name without quoutes. E.g. temperature_sensor frame
#smooth_time:
#   Smoothing window applied to the temp_sensor, in seconds. Can reduce motor
#   noise from excessive small corrections in response to sensor noise. The
#   default is 2.0 seconds.
#max_comp_z:
#   Disables compensation above this Z height [mm]. The last computed correction
#   will remain applied until the toolhead moves below the specified Z position
#   again. The default is 0.0mm (always on).
#max_z_offset:
#   Maximum absolute compensation that can be applied to the Z axis [mm]. The
#   default is 99999999.0mm (unlimited).
z_stepper:
#   The Z stepper motor linked with the Z endstop, as written in printer.cfg.
#   Used for triggering reference temperature measurement. Usually 'stepper_z'
#   unless otherwise defined.

G-Code Commands

The following commands are available when the frame_expansion_compensation config section is enabled:

  • SET_FRAME_COMP ENABLE=[<0:1>]: enable or disable frame expansion compensation. When disabled, the last computed compensation value will remain applied until next homing.
  • QUERY_FRAME_COMP: report current state and key parameters of the frame expansion compensation.

Overview

TODO

Comments
  • QUERY_FRAME_COMP in klipper implementation...

    QUERY_FRAME_COMP in klipper implementation...

    The new klipper documentation doesn't say anything about a query function.... will it still work? If not any reason I shouldn't just stay with the plugin?

    opened by PhilBaz 7
  • stepper_z for multiple Z steppers.

    stepper_z for multiple Z steppers.

    Im on a 24. Voron with 4 Z stepper motors stepper_z - stepper_z3. defined as bellow.

    Is config, z_stepper: stepper_z , still correct?

    The frame compensation appears as if its functioning. Doesn't throw an error, and the query looks as it should. But i dont think it is functioning. I cranked up the temp_coeff: 0.03 producing -0.12mm on a 23min first layer. and it appeared to have no effect. I previously used a manual correction of -0.06mm to correct going into the second layer.

    So I'm at a bit of a loss. I suspect something is not working correctly.

    Im also using 'virtual gantry backers' and have created a corresponding issue there as well. I would appreciate any thoughts or input.

    https://github.com/Deutherius/VGB/issues/3

    printer.cfg

    [frame_expansion_compensation] temp_coeff: 0.03 ##0.0009 temp_sensor: temperature_sensor ToolHP max_z_offset: 0.12 z_stepper: stepper_z

    [stepper_z] ## Z0 Stepper - Front Left ## In Z-MOT Position step_pin: PD14 dir_pin: PD13 enable_pin: !PD15 rotation_distance: 40 gear_ratio: 80:16 microsteps: 16

    position_max: 330 ##<<<<<<<<<

    endstop_pin: ^PA0

    position_min: -5 homing_speed: 32 second_homing_speed: 3 homing_retract_dist: 3

    [tmc2209 stepper_z] uart_pin: PD10 interpolate: True run_current: 0.8 hold_current: 0.8 sense_resistor: 0.110 stealthchop_threshold: 0

    [stepper_z1] ## Z1 Stepper - Rear Left ## In E1-MOT Position step_pin: PE6 dir_pin: !PC13 enable_pin: !PE5 rotation_distance: 40 gear_ratio: 80:16 microsteps: 16

    [tmc2209 stepper_z1] uart_pin: PC14 interpolate: True run_current: 0.8 hold_current: 0.8 sense_resistor: 0.110 stealthchop_threshold: 0

    [stepper_z2] ## Z2 Stepper - Rear Right ## In E2-MOT Position step_pin: PE2 dir_pin: PE4 enable_pin: !PE3 rotation_distance: 40 gear_ratio: 80:16 microsteps: 16

    [tmc2209 stepper_z2] uart_pin: PC15 interpolate: true run_current: 0.8 hold_current: 0.8 sense_resistor: 0.110 stealthchop_threshold: 0

    [stepper_z3] ## Z3 Stepper - Front Right ## In E3-MOT Position step_pin: PD12 dir_pin: !PC4 enable_pin: !PE8 rotation_distance: 40 gear_ratio: 80:16 microsteps: 16

    [tmc2209 stepper_z3] uart_pin: PA15 interpolate: true run_current: 0.8 hold_current: 0.8 sense_resistor: 0.110 stealthchop_threshold: 0

    opened by PhilBaz 2
  • questions regarding temp_sensor & z_stepper configurations

    questions regarding temp_sensor & z_stepper configurations

    Hi,

    My chamber temp sensor was already defined in [temperature_fan] section as the chamber fan was controlled by this thermsitor, I cannot use it to define in a [temperature_sensor] section otherwise an error would be raised. How can I deal with this issue? Any work around?

    Also, how to configure the z_stepper for voron2.4 since there're 4 z steppers?

    Thanks.

    opened by dukeduck1984 1
  • Updated install.sh to no longer use dummy service

    Updated install.sh to no longer use dummy service

    The dummy service should no longer be needed for use with Moonraker. Updated the install.sh file to continue following the pattern used by Z Auto Calibration. In addition, updated the README since copying the file into Klipper isn't needed since the install.sh file will just create a link.

    opened by randellhodges 0
  • Problem with process_frame_expansion

    Problem with process_frame_expansion

    Hello, I have a problem with the process_frame_expansion.py script. If I run the measure_thermal_behavior.py and the process_meshes.py all sound good but when I run the process_frame_expansion.py script I have this error:

    [email protected]:~/measure_thermal_behavior $ python3 process_frame_expansion.py thermal_quant_mark988#5325_2022-05-29_23-12-26.json Analyzing file: thermal_quant_mark988#5325_2022-05-29_23-12-26 sys:1: RankWarning: Polyfit may be poorly conditioned

    And it doesn't create the temp_coeff_fitting.png

    I am attaching the edited measure_thermal_behavior.py the out.txt and the thermal_quant fil

    Thank you for your help

    Marco

    measure_thermal_behavior.zip e

    opened by panik988 0
  • measure_thermal_behavior : Anything to be gained by adding klicky z_calibration between meshes?

    measure_thermal_behavior : Anything to be gained by adding klicky z_calibration between meshes?

    I have a klicky probe.

    My brain is telling me it would be nice to have the z-calibration routine/data added into the measure_thermal_behavior script.

    But I cant actually figure out what it would be useful for. the z-calibration does drift with temperature and time, over squishing after long periods of heated chamber.

    Is there anything to be gained here?

    https://github.com/protoloft/klipper_z_calibration

    opened by PhilBaz 0
  • Need methodology for different active lengths

    Need methodology for different active lengths

    I'm trying to apply this to an i3 bedslinger style frame, where the gantry is supported by twin stainless steel leadscrews, and inside an enclosure. The deviation from expected Z position is going to be dependent on the thermal growth of the length of leadscrew that is supporting the gantry. When the nozzle is at z=0 there's about 50 mm of active leadscrew, so if the chamber was heated from 20C to 40C the leadscrews would grow thermally 0.0000173 mm/mm/C x 50mm x (40C-20C) = 0.017mm. But when the nozzle gets up to z=100mm there would be 100+50 = 150mm of leadscrew active, so the total growth would be 0.0000173 x 150mm x 20c = 0.052mm. So the compensation needs to know the active length of the support element, which may change from layer to layer as it does in the case of the i3. I don't think what you currently have set up here takes that in to account.

    feature request 
    opened by cmgreyhounds 1
Releases(v0.0.2)
  • v0.0.2(Aug 3, 2022)

    What's Changed

    • Updated install.sh to no longer use dummy service by @randellhodges in https://github.com/alchemyEngine/klipper_frame_expansion_comp/pull/4

    Re-run install.sh after updating and make any necessary changes to your Moonraker config (see README/Configuration).

    Source code(tar.gz)
    Source code(zip)
  • v0.0.1(Dec 18, 2021)

An example of time series augmentation methods with Keras

Time Series Augmentation This is a collection of time series data augmentation methods and an example use using Keras. News 2020/04/16: Repository Cre

九州大学 ヒューマンインタフェース研究室 229 Jan 02, 2023
official implementation for the paper "Simplifying Graph Convolutional Networks"

Simplifying Graph Convolutional Networks Updates As pointed out by #23, there was a subtle bug in our preprocessing code for the reddit dataset. After

Tianyi 727 Jan 01, 2023
Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps

Proximal Backpropagation Proximal Backpropagation (ProxProp) is a neural network training algorithm that takes implicit instead of explicit gradient s

Thomas Frerix 40 Dec 17, 2022
[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdhury, Yongxin Yan

Ayan Kumar Bhunia 44 Dec 12, 2022
code for Fast Point Cloud Registration with Optimal Transport

robot This is the repository for the paper "Accurate Point Cloud Registration with Robust Optimal Transport". We are in the process of refactoring the

28 Jan 04, 2023
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.

Subspace Adversarial Training Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However,

15 Sep 02, 2022
Rede Neural Convolucional feita durante o processo seletivo do Laboratório de Inteligência Artificial da FACOM (UFMS)

Primeira_Rede_Neural_Convolucional Rede Neural Convolucional feita durante o processo seletivo do Laboratório de Inteligência Artificial da FACOM (UFM

Roney_Felipe 1 Jan 13, 2022
A distributed deep learning framework that supports flexible parallelization strategies.

FlexFlow FlexFlow is a deep learning framework that accelerates distributed DNN training by automatically searching for efficient parallelization stra

528 Dec 25, 2022
Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment

Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment.

MT Schmitz 2 Feb 11, 2022
CvT2DistilGPT2 is an encoder-to-decoder model that was developed for chest X-ray report generation.

CvT2DistilGPT2 Improving Chest X-Ray Report Generation by Leveraging Warm-Starting This repository houses the implementation of CvT2DistilGPT2 from [1

The Australian e-Health Research Centre 21 Dec 28, 2022
Invert and perturb GAN images for test-time ensembling

GAN Ensembling Project Page | Paper | Bibtex Ensembling with Deep Generative Views. Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhan

Lucy Chai 93 Dec 08, 2022
PyTorch implementation of paper A Fast Knowledge Distillation Framework for Visual Recognition.

FKD: A Fast Knowledge Distillation Framework for Visual Recognition Official PyTorch implementation of paper A Fast Knowledge Distillation Framework f

Zhiqiang Shen 129 Dec 24, 2022
Research code for Arxiv paper "Camera Motion Agnostic 3D Human Pose Estimation"

GMR(Camera Motion Agnostic 3D Human Pose Estimation) This repo provides the source code of our arXiv paper: Seong Hyun Kim, Sunwon Jeong, Sungbum Park

Seong Hyun Kim 1 Feb 07, 2022
PyTorch code for our paper "Attention in Attention Network for Image Super-Resolution"

Under construction... Attention in Attention Network for Image Super-Resolution (A2N) This repository is an PyTorch implementation of the paper "Atten

Haoyu Chen 71 Dec 30, 2022
Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment (ICCV2021)

Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment This is a pytorch project for the paper Seeing Dynamic Scene i

DV Lab 21 Nov 28, 2022
Miscellaneous and lightweight network tools

Network Tools Collection of miscellaneous and lightweight network tools to simplify daily operations, administration, and troubleshooting of networks.

Nicholas Russo 22 Mar 22, 2022
A Python implementation of active inference for Markov Decision Processes

A Python package for simulating Active Inference agents in Markov Decision Process environments. Please see our companion preprint on arxiv for an ove

235 Dec 21, 2022
TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction TSDF++ is a novel multi-object TSDF formulation that can encode mult

ETHZ ASL 130 Dec 29, 2022
Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).

Fisher Induced Sparse uncHanging (FISH) Mask This repo contains the code for Fisher Induced Sparse uncHanging (FISH) Mask training, from "Training Neu

Varun Nair 37 Dec 30, 2022