""" Processing of Elastic Modulus, Shear Modulus, Poisson's Ratio from tensile frame testing. Refer to Standards: ASTM D3039 and D3518 Running script: 1. Script has been verified to work with the following versions: python==3.9.25 matplotlib==3.9.4 npTDMS==1.10.0 numpy==2.0.2 pandas==2.3.3 PyYAML==6.0.3 scipy==1.13.1 Without versions, libraries can be installed as: `python3 -m pip install numpy nptdms matplotlib pandas scipy pyyaml` 2. Download and extract the tar file of data to be in the same folder as the present script. `tar -xzvf static_coupon_data.tar.gz` 3. Material specifications can be looked up via the panel number included with the outputs. For tests conducted at 0 and 90 degrees, the elastic modulus and Poisson's ratio are of interest. Tests at 45 degrees are generally for the shear modulus. 4. *.csv files for different cases are saved in an `outputs` folder at the same location as this script. Data Preparation Notes: 1. Text file notes are included in some coupon folders about repeat tests and/or potential damage to coupons before tensile testing. """ import os import numpy as np from nptdms import TdmsFile import glob import matplotlib.pyplot as plt import pandas as pd from scipy.signal import butter, filtfilt ############################################################################### ######## Inputs ######## ############################################################################### show_plots = False default_start_threshold = 1000 # start strain (ue) default_end_threshold = 3000 # End strain (ue) default_max_samples = int(1e30) # maximum number of samples to include lbf_to_N = 4.4482216 # N/lbf filter_strain = True high_freq = 50 # for filter fs = 1000 # Hz transverse_sign = -1 # measurement is positive in compression, so flip sign filedir = os.path.dirname(os.path.realpath(__file__)) # get path to this file data_folder = os.path.join(filedir, 'static_coupon_data') case_dict = { 'Epoxy' : { 'UD0' : {}, 'UD45' : {}, 'UD90' : {'start_threshold' : 500, 'end_threshold' : 1000}, 'BX0_90' : {}, 'BX45' : {}, 'TX0' : {'max_samples' : int(1e5)}, 'TX45' : {'max_samples' : int(1e5)}, 'TX90' : {'max_samples' : int(1e5), 'end_threshold' : 2000}, }, 'Elium' : { 'UD0' : {}, 'UD45' : {}, 'UD90' : {}, 'BX0' : {}, 'BX45' : {}, }, 'PECAN' : { 'UD0' : {}, 'UD45' : {}, 'UD90' : {}, 'BX0' : {}, 'BX45' : {}, }, 'CFRP' : { '3mm_Epoxy' : {}, }, } ############################################################################### ###### Verify Data is Located Correctly ###### ############################################################################### if not os.path.exists(data_folder): raise Exception('Data folder not found. Please download and extract ' 'static_coupon_data.tar.gz to the same folder as this ' 'script.') ############################################################################### ######## Coupon Sizing ######## ############################################################################### uni_dict = { '0deg' : { 'number' : [25, 26, 27, 28, 29], 'width' : [14.9, 14.855, 14.835, 14.87, 14.8425], 'thickness' : [2.2725, 2.385, 2.2775, 2.325, 2.355], }, 'p45deg' : { 'number' : [30, 31, 32, 33, 34], 'width' : [14.8775, 14.8675, 14.8975, 14.8625, 14.845], 'thickness' : [2.3225, 2.3475, 2.3075, 2.3475, 2.34], }, '90deg' : { 'number' : [35, 36, 37, 38, 39], 'width' : [14.865, 14.8475, 14.93, 14.86, 14.8625], 'thickness' : [2.3775, 2.3625, 2.5025, 2.33, 2.4175], }, } bx_dict = { '0deg' : { 'number' : [25, 26, 27, 28, 29], 'width' : [14.7525, 14.835, 14.8275, 14.805, 14.8475], 'thickness' : [1.775, 1.7725, 1.8325, 1.815, 1.8475], }, 'p45deg' : { 'number' : [30, 31, 32, 33, 34], 'width' : [14.8325, 14.83, 14.8425, 14.8275, 14.8375], 'thickness' : [1.84, 1.8475, 1.8425, 1.84, 1.8575], }, } tx_dict = { '0deg' : { 'number' : [1, 2, 3, 4, 5], 'width' : [14.8025, 14.8125, 14.805, 14.795, 14.77], 'thickness' : [1.705, 1.7425, 1.8175, 1.725, 1.7475], }, 'm45deg' : { 'number' : [16, 17, 18, 19, 20], 'width' : [14.8075, 14.7875, 14.7775, 14.82, 14.7975], 'thickness' : [1.8425, 1.765, 1.8, 1.8125, 1.815], }, '90deg' : { 'number' : [6, 7, 8, 9, 10], 'width' : [14.77, 14.7825, 14.805, 14.825, 14.7675], 'thickness' : [1.8125, 1.78, 1.8075, 1.7825, 1.805], }, } carbon_dict = { '0deg' : { 'number' : [7, 8, 9, 10, 11, 12], 'width' : [14.84, 14.8925, 14.855, 14.8575, 14.83, 14.8775], 'thickness' : [2.98, 3.0175, 2.9825, 2.98, 2.9775, 2.98], }, 'load_frame' : '250 kN' } ud_pecan0_dict = { '0deg' : { 'number' : [6, 7, 8, 9, 10], 'width' : [14.8625, 14.8175, 14.875, 15.0125, 14.785], 'thickness' : [2.2775, 2.2875, 2.2825, 2.1975, 2.27], }, 'load_frame' : '12 kN' } ud_pecan90_dict = { '90deg' : { 'number' : [6, 7, 8, 9, 10], 'width' : [14.87, 14.8875, 14.865, 14.8825, 14.9325], 'thickness' : [2.365, 2.355, 2.34, 2.4325, 2.34], }, 'load_frame' : '12 kN' } ud_pecan45_dict = { 'p45deg' : { 'number' : [6, 7, 8, 9, 10], 'width' : [14.8275, 14.815, 14.83, 14.8025, 14.82], 'thickness' : [2.2525, 2.25, 2.225, 2.2375, 2.25], 'adjust_max' : { '7' : 56000 #56000 is good here } }, 'load_frame' : '12 kN' } bx_pecan0_dict = { '0deg' : { 'number' : [6, 7, 8, 9, 10], 'width' : [14.8325, 14.77, 14.79, 14.805, 14.7875], 'thickness' : [1.8, 1.8325, 1.8575, 1.835, 1.855], }, 'load_frame' : '12 kN' } bx_pecan45_dict = { '45deg' : { 'number' : [7, 8, 9, 10, 11, 12], 'width' : [14.8375, 14.775, 14.8225, 14.7875, 14.81, 14.7925], 'thickness' : [1.8575, 1.89, 1.9, 1.8525, 1.8525, 1.8725], }, 'load_frame' : '12 kN' } epoxy_ud_dict = { '0deg' : { 'number' : [13, 14, 15, 16, 17, 18], 'width' : [14.7925, 14.825, 14.7775, 14.8, 14.785, 14.755], 'thickness' : [2.2575, 2.25, 2.2275, 2.325, 2.24, 2.225], }, '90deg' : { 'number' : [11, 12, 13, 14, 15, 16], 'width' : [14.8825, 14.815, 14.8875, 14.8175, 14.86, 14.875], 'thickness' : [2.245, 2.2525, 2.245, 2.26, 2.245, 2.2825], }, 'load_frame' : '12 kN' } epoxy_ud_pm45_dict = { 'pm45deg' : { 'number' : [1,2,3,4,5,6], 'width' : [14.82, 14.8225, 14.8525, 14.8325, 14.855, 14.8075], 'thickness' : [2.5475, 2.5225, 2.535, 2.5125, 2.5075, 2.495], }, 'load_frame' : '12 kN' } epoxy_bx_dict = { '0deg' : { 'number' : [4, 6, 8], 'width' : [15.5775, 15.5375, 15.5375], 'thickness' : [1.8275, 1.81, 1.84], }, '90deg' : { 'number' : [4, 6, 8], 'width' : [15.46, 15.54, 15.4925], 'thickness' : [1.8225, 1.8375, 1.8125], }, 'p45deg' : { 'number' : [4, 6, 8], 'width' : [15.2575, 15.56, 15.5425], 'thickness' : [1.745, 1.8425, 1.8575], }, 'm45deg' : { 'number' : [4, 6, 8], 'width' : [15.51, 15.475, 15.4875], 'thickness' : [1.82, 1.7875, 1.79], }, 'load_frame' : '12 kN' } coupon_sizes = { '2025003' : epoxy_bx_dict, '2025004' : epoxy_ud_dict, '2025008' : uni_dict, '2025009' : bx_dict, '2025011' : tx_dict, 'PCF001' : carbon_dict, '2025018' : ud_pecan90_dict, '2025019' : ud_pecan0_dict, '2025020' : ud_pecan45_dict, '2025021' : bx_pecan0_dict, '2025023' : bx_pecan45_dict, '2025036' : epoxy_ud_pm45_dict, } def process_data_set(folder_name, start_threshold, end_threshold, max_samples): ########################################################################### ######## Parse Files ######## ########################################################################### filenames = sorted(glob.glob(f"{folder_name}/*.tdms")) parsed_files = [None] * len(filenames) # Iterate over each filename for ind,filepath in enumerate(filenames): curr_file = os.path.basename(filepath) dash_components = curr_file.split('-') under_components = dash_components[-1].split('_') parsed_files[ind] = { 'filename': curr_file, 'panel': dash_components[1], # panel number 'angle': dash_components[2], # coupon cut angle 'number': int(under_components[0]), # coupon number 'date' : '-'.join(under_components[1:4]), 'time' : ':'.join(under_components[4:7]), } parsed_freq = float(under_components[-1][:-7]) assert parsed_freq == fs, 'Sample frequency from filename' \ + ' is not what is assumed for filtering.' ########################################################################### ######## Load Data ######## ########################################################################### recorded_data = [None] * len(filenames) processed_results = [None] * len(filenames) for ind,filepath in enumerate(filenames): tdms_file = TdmsFile.read(filepath) # Identify parameters of the tests panel = parsed_files[ind]['panel'] angle = parsed_files[ind]['angle'] coupon_num = parsed_files[ind]['number'] size_ind = np.where(np.array(coupon_sizes[panel][angle]['number']) == coupon_num)[0][0] width = coupon_sizes[panel][angle]['width'][size_ind] * 1e-3 thickness = coupon_sizes[panel][angle]['thickness'][size_ind] * 1e-3 curr_max_samples = max_samples if 'adjust_max' in coupon_sizes[panel][angle].keys(): if '{}'.format(coupon_num) in coupon_sizes[panel][angle]\ ['adjust_max']: curr_max_samples = coupon_sizes[panel][angle]\ ['adjust_max']['{}'.format(coupon_num)] # list of channels # tdms_file.groups()[0].channels() force_sign = 1.0 if 'load_frame' in coupon_sizes[panel].keys(): if coupon_sizes[panel]['load_frame'].upper() == '250 kN'.upper(): strain_key = 'Axial Strain 250 kN' load_key = 'Load 250kN' force_sign = -1.0 elif coupon_sizes[panel]['load_frame'].upper() == '12 kN'.upper(): strain_key = 'Axial Strain 12 kN' load_key = 'Load 12kN' else: raise else: strain_key = 'Axial Strain' load_key = 'Load 12kN' recorded_data[ind] = { 'axial_data' : tdms_file.groups()[0][strain_key]\ [:curr_max_samples], 'axial_units' : tdms_file.groups()[0][strain_key].properties['Units'], 'transverse_data' : transverse_sign * \ tdms_file.groups()[0]['Transverse Strain'][:curr_max_samples], 'transverse_units' : tdms_file.groups()[0]['Transverse Strain'].properties['Units'], 'force_data' : force_sign*tdms_file.groups()[0][load_key]\ [:curr_max_samples], 'force_units' : tdms_file.groups()[0][load_key].properties['Units'], } ###################### # Filter data to eliminate noise if filter_strain: high = high_freq / (0.5 * fs) filter_order = 3 b, a = butter(filter_order, high, btype='lowpass') recorded_data[ind]['axial_data'] \ = filtfilt(b, a, recorded_data[ind]['axial_data']) recorded_data[ind]['transverse_data'] \ = filtfilt(b, a, recorded_data[ind]['transverse_data']) recorded_data[ind]['force_data'] \ = filtfilt(b, a, recorded_data[ind]['force_data']) ###################### # Find the Index with Max Strain max_ind = np.argmax(recorded_data[ind]['axial_data']) start_ind = np.where(recorded_data[ind]['axial_data'] \ > start_threshold)[0][0] # find the last index (before the peak strain) where the ending # threshold is met end_ind = np.where(recorded_data[ind]['axial_data'][:max_ind] \ < end_threshold)[0][-1] ###################### # Linear regression results axial_curr = recorded_data[ind]['axial_data'][start_ind:end_ind] force_curr = recorded_data[ind]['force_data'][start_ind:end_ind] trans_curr = recorded_data[ind]['transverse_data'][start_ind:end_ind] force_fit,ss_res,_,_ = np.linalg.lstsq( np.vstack((np.ones_like(axial_curr), axial_curr)).T, force_curr, rcond=None) ss_tot = ((force_curr - force_curr.mean())**2).sum() rsq_force = 1 - ss_res / ss_tot trans_fit,ss_res,_,_ = np.linalg.lstsq( np.vstack((np.ones_like(axial_curr), axial_curr)).T, trans_curr, rcond=None) ss_tot = ((trans_curr - trans_curr.mean())**2).sum() rsq_trans = 1 - ss_res / ss_tot ###################### # Shear modulus fit shear_stress = force_curr * lbf_to_N / 2 / width / thickness shear_strain = axial_curr/1e6 - (trans_curr / (width*1e3)) shear_fit,ss_res,_,_ = np.linalg.lstsq( np.vstack((np.ones_like(shear_strain), shear_strain)).T, shear_stress, rcond=None) ss_tot = ((shear_stress - shear_stress.mean())**2).sum() rsq_shear = 1 - ss_res / ss_tot ###################### # Format results processed_results[ind] = { 'panel' : panel, 'angle' : angle, 'coupon_number' : coupon_num, # 'axial_lbf/ue' : force_fit[1], # 'trans_mm/ue' : trans_fit[1], 'elastic_mod_Pa' : force_fit[1] * lbf_to_N * 1e6 / width \ / thickness, 'rsq_elastic_mod' : rsq_force[0], 'poissons' : -1*(trans_fit[1] / (width*1e3)) * 1e6, 'rsq_poissons' : rsq_trans[0], 'shear_mod_at_45deg_Pa' : shear_fit[1], 'rsq_shear' : rsq_shear[0] } ###################### if show_plots: plt.plot(recorded_data[ind]['axial_data'], recorded_data[ind]['force_data']) plt.xlabel(recorded_data[ind]['axial_units']) plt.ylabel(recorded_data[ind]['force_units']) plt.title(os.path.basename(filepath)) plt.show() plt.plot(recorded_data[ind]['axial_data'], recorded_data[ind]['transverse_data']) plt.xlabel(recorded_data[ind]['axial_units']) plt.ylabel(recorded_data[ind]['transverse_units']) plt.title(os.path.basename(filepath)) plt.show() plt.plot(recorded_data[ind]['axial_data']) bounds = [recorded_data[ind]['axial_data'].min(), recorded_data[ind]['axial_data'].max()] plt.plot([start_ind, start_ind], bounds) plt.plot([end_ind, end_ind], bounds) plt.plot([0, len(recorded_data[ind]['axial_data'])], [start_threshold, start_threshold]) plt.plot([0, len(recorded_data[ind]['axial_data'])], [end_threshold, end_threshold]) plt.ylabel(recorded_data[ind]['axial_units']) plt.title(os.path.basename(filepath)) plt.show() plt.plot(axial_curr, force_curr) plt.xlabel(recorded_data[ind]['axial_units']) plt.ylabel('Force ' + recorded_data[ind]['force_units']) plt.title('Fitted Data ' + os.path.basename(filepath)) plt.show() plt.plot(axial_curr, trans_curr) plt.xlabel(recorded_data[ind]['axial_units']) plt.ylabel('Transverse ' + recorded_data[ind]['transverse_units']) plt.title('Fitted Data ' + os.path.basename(filepath)) plt.show() plt.plot(shear_strain*1e6, shear_stress/1e6) plt.xlabel('Shear strain [ue]') plt.ylabel('Shear Stress [MPa]') plt.show() # axial_curr = recorded_data[ind]['axial_data'][start_ind:end_ind] # force_curr = recorded_data[ind]['force_data'][start_ind:end_ind] # trans_curr = recorded_data[ind]['transverse_data'][start_ind:end_ind] ########################################################################### ######## Output CSV of Data ######## ########################################################################### output_cols = ['panel', 'angle', 'coupon_number', 'elastic_mod_Pa', 'rsq_elastic_mod', 'poissons', 'rsq_poissons', 'shear_mod_at_45deg_Pa', 'rsq_shear'] # output_csv = './pandas_csv_out.csv' df = pd.DataFrame(processed_results, columns=output_cols) df.to_csv(output_fname, index=False) print('{} Coupons Reported'.format(df['elastic_mod_Pa'].shape[0])) print('Avg. Elastic Modulus: {:.4f} GPa'.format( df['elastic_mod_Pa'].mean()/1e9)) print('Avg. Poisson Ratio: {:.4f}'.format(df['poissons'].mean())) print('Shear Modulus for 45 degree orientation: {:.4f} GPa\n'.format( df['shear_mod_at_45deg_Pa'].mean()/1e9)) ############################################################################### ######## Loop through and actually process all data files ######## ############################################################################### # Make the output folder os.makedirs(os.path.join(filedir, 'outputs'), exist_ok=True) # Process data to output folder for top_folder in case_dict.keys(): for second_folder in case_dict[top_folder].keys(): folder_name = os.path.join(data_folder, top_folder, second_folder) output_fname = os.path.join(filedir, 'outputs', './{}_{}.csv'.format(top_folder, second_folder)) curr_dict = case_dict[top_folder][second_folder] start_threshold = curr_dict.get('start_threshold', default_start_threshold) end_threshold = curr_dict.get('end_threshold', default_end_threshold) max_samples = curr_dict.get('max_samples', default_max_samples) print('Processing Data for {} {}'.format(top_folder, second_folder)) process_data_set(folder_name, start_threshold, end_threshold, max_samples)