| import numpy as np |
| import pandas as pd |
|
|
| np.random.seed(42) |
| epsilon = 1e-8 |
|
|
| class Dataset: |
| def __init__(self, mat_name='FRP'): |
| filename = './Data/240_simulations_DS.xlsx' |
| self.df = pd.read_excel(filename, sheet_name=mat_name, header=1) |
| |
| self.input_columns = [ |
| 'Gate Location', |
| 'Matrix', |
| 'Fiber Type', |
| 'Fiber wt%(Volume Fractions)', |
| |
| 'Packing Pressure (MPa)', |
| 'Packing Time (s)', |
| 'Mold Temperature (°C)', |
| 'Injection Speed (cm^3/s)' |
| ] |
| self.output_columns = ['Alpha_angle_deg(ABS)', 'Beta_angle_deg(ABS)', 'Gamma_angle_deg(ABS)'] |
|
|
| self.material_map = {'PA6': 0, 'PP': 1} |
| self.fiber_map = {'CF': 0, 'GF': 1} |
| self.combination_map = {'PA6-CF-10': 0, 'PA6-CF-15': 1, 'PA6-CF-30': 2, 'PA6-CF-40': 3, |
| 'PA6-GF-15': 4, 'PA6-GF-30': 5, 'PA6-GF-40': 6, 'PA6-GF-50': 7, |
| 'PP-CF-10': 8, 'PP-CF-15': 9, 'PP-CF-30': 10, 'PP-CF-40': 11, |
| 'PP-GF-15': 12, 'PP-GF-30': 13, 'PP-GF-40': 14, 'PP-GF-50': 15} |
| self.df['Matrix'] = self.df['Matrix'].map(self.material_map) |
| self.df['Fiber Type'] = self.df['Fiber Type'].map(self.fiber_map) |
| self.df['Fiber-Matrix Combination'] = self.df['Fiber-Matrix Combination'].map(self.combination_map) |
| self.df['Fiber wt%(Volume Fractions)'] = self.df['Fiber wt%(Volume Fractions)'] / 100.0 |
|
|
| self.input_mean = self.df[self.input_columns].mean().to_numpy(dtype=np.float32) |
| self.input_std = self.df[self.input_columns].std().to_numpy(dtype=np.float32) + epsilon |
| self.output_mean = self.df[self.output_columns].mean().to_numpy(dtype=np.float32) |
| self.output_std = self.df[self.output_columns].std().to_numpy(dtype=np.float32) + epsilon |
|
|
|
|
| def get_input(self, normalize=False): |
| data = self.df[self.input_columns].to_numpy(dtype=np.float32) |
| if normalize: |
| data = self.normalize_input(data) |
| return data |
| |
|
|
| def get_output(self, normalize=False): |
| data = self.df[self.output_columns].to_numpy(dtype=np.float32) |
| if normalize: |
| data = self.normalize_output(data) |
| return data |
|
|
| def __str__(self): |
| return str(self.df.head()) |
| |
| def normalize_input(self, input_data): |
| return (input_data - self.input_mean) / self.input_std |
| |
| def normalize_output(self, output_data): |
| return (output_data - self.output_mean) / self.output_std |
| |
| def denormalize_input(self, normalized_input): |
| return normalized_input * self.input_std + self.input_mean |
| |
| def denormalize_output(self, normalized_output): |
| return normalized_output * self.output_std + self.output_mean |
|
|
| if __name__ == "__main__": |
| dataset = Dataset() |
| |
| |
| input_data = dataset.get_input(normalize=False) |
| output_data = dataset.get_output(normalize=False) |
| |
| print("Input shape:", input_data.shape) |
| print("Output shape:", output_data.shape) |
