DOI: 10.5061/dryad.1vhhmgr1f
title: Data for: Melt electrowriting enabled 3D liquid crystal elastomer structures for cross-scale actuators and temperature field sensors

description: Liquid crystal elastomers have garnered significant attention due to their
 remarkable capability to undergo reversible strains and shape
 transformations under various stimuli. Early studies on LCE primarily
 focused on limited shape changes of macrostructures or quasi-3D
 microstructures. However, fabricating complex cross-scale LCE-based 3D
 structures still remains challenging. Here, we report a compatible method,
 the Melt-Electrohydrodynamic (Melt-EHD) 3D printing, to create LCE-based
 microfiber actuators and various 3D actuators across micrometer to
 centimeter scales, showcasing their actuation to thermal airflow stimulus.
 By controlling printing parameters, microfiber actuators with different
 diameters (5 μm~70 μm), and tunable properties including actuation strain
 (10%~55%), actuation stress (0~0.6 MPa), and large work density (~160J/kg)
 have been demonstrated. Under dynamic thermal airflow stimulus at 15 Hz,
 the microfiber actuators lift weights over 3500 times heavier than
 themselves. These 3D structures were obtained by depositing LCE
 microfibers along pre-programmed paths, including various
 gradient-responsive elementary structural units, 1 mm-sized microgripper,
 and various large area 3D lattice structures. In addition, by integrating
 a Deep Learning model, we have demonstrated, for the first time, large
 area (≥ centimeter scale), real-time (24 Hz sampling frequency),
 high-precision (~95%) LCE grid based spatial temperature field
 sensors with a spatial resolution of only 4 mm.
description: The dataset was collected by a large area (≥ centimeter scale),
 real-time (24 Hz sampling frequency), high-precision (~95%) spatial
 temperature field (STF) sensor with a spatial resolution of 4 mm. it has
 been peocessed by python programs.
description: # Dataset of Melt electrowriting enabled 3D liquid crystal elastomer
 structures for cross-scale actuators and temperature field
 sensors Here, we
 report a compatible method, the Melt-Electrohydrodynamic (Melt-EHD) 3D
 printing, to create LCE-based microfiber actuators and various 3D
 actuators across micrometer to centimeter scales, showcasing their
 actuation to thermal airflow stimulus. By controlling printing parameters,
 microfiber actuators with different diameters (5 μm~70 μm), and tunable
 properties including actuation strain (10%~55%), actuation stress (0-0.6
 MPa), and large work density (~160J/kg) have been demonstrated. Under
 dynamic thermal airflow stimulus at 15 Hz, the microfiber actuators lift
 weights over 3500 times heavier than themselves. These 3D structures were
 obtained by depositing LCE microfibers along pre-programmed paths,
 including various gradient-responsive elementary structural units, 1
 mm-sized microgripper, and various large area 3D lattice structures. In
 addition, by integrating a Deep Learning model, we have demonstrated, for
 the first time, large area (≥ centimeter scale), real-time (24 Hz sampling
 frequency), high-precision (~95%) LCE grid based spatial temperature field
 sensors with a spatial resolution of only 4 mm. ## Description of the data
 and file structure ## File structure and contents -dataset: Datasets used
 for deep learning models train and test. -dataset/preprocess: Python file
 that preprocesses the acquired thermal images. -dataset/test: Preprocessed
 dataset for deep learning model test. -dataset/train: Preprocessed dataset
 for deep learning model train. -misc: Base models layer and utils for
 building deep learning models. -model: Results of our trained model.
 -result: The result obtained by using the trained model to predict.
 -AI_model.py: Our deep learning model, for training and prediction.
 -Res50.py: The structure of the deep learning model being invoked.
 -Usage.md: How to use python files and datasets. ### How can you use this
 dataset and process your own We have provided the processed data set in
 dataset/trian and dataset/test, and you can use them directly. And you can
 also use following steps to process your own data set. 1. extract_data
 folder to create a new [number] folder as the current working condition of
 the main folder (mainDir), a new csv folder to save the next line of csv
 file (such as E:\t_map\extract_data\1\csv) 2. Extract video images. Modify
 VIDEO_PATH and mainDir in dataset/preprocess/config.yaml. Switch to
 dataset/preprocess/ and type: python 1extract_image_data.py 3. Extract the
 corner points. Modify the cornerPath and alignTxtPath in config.yaml.
 python 2get_corner.py Take the four sharp edges on the frame in order [top
 left, top right, bottom left, bottom right]. 4. Time alignment. python
 3registerTansform.py Input to help distinguish aligned temperature chart
 numbers and find the best blend chart numbers. 5. Extract the csv file for
 making the data set from the thermal imager exe data and put it into the
 csv folder 6. Start labeling. Modify the x in config.yaml in the pre_data
 folder. python 4createDataset.py 1\)Draw the polygon surrounding box,
 surround the 14 lines in the middle, left click to add a point, right
 click to delete the point, enter to close the polygon to complete the
 surrounding. 2\) Follow the prompts to check points. 3\) Follow the
 prompts to check numbers. 7. You can check the data. python
 5checkDataset.py 8. Divide the data set into a training set and a test
 set. python ../../datasetDivide.py (You need to change the folder_list and
 target_folder in datasetDivide.py to yourself.) Training the model: python
 AI_model.py --train True --batch_size 128 --dataset_dir /path_to_dataset
 --model_dir /where_to_save_model Evaluating the model: python AI_model.py
 --train False --load_model_path /where_to_load_model --result_dir
 /where_to_save_results All units are pixels.