Metal Arc Welding

Predictive Quality Arc Welding Dataset The dataset comprises various current and voltage time series. Both currents and voltages are synchronously sampled at a frequency 100 kHz, with a maximum permissible error of 0.5%. Preprocessed Data Column Name Description ------------ ------------------------------------------------------------- labels Quality label (0: bad weld quality | 1: good weld quality | -1: no label) exp_ids ID of the experiment run V_000 Voltage at the beginning of the cycle (t_0) ... Voltage from (t_1) to (t_198) V_199 Voltage at the end of the cycle I_000 Current at the beginning of the cycle (t_0) ... Current from (t_1) to (t_198) I_199 Current at the end of the cycle Code Sample Reading the Data import numpy as np import pandas as pd def convert_to_np(data: pd.DataFrame) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """ Convert DataFrame to numpy arrays, separating labels, experiment IDs, and features. Args: data (pd.DataFrame): Input DataFrame containing 'labels', 'exp_ids', and feature columns. Returns: tuple: A tuple containing: - labels (np.ndarray): Array of labels - exp_ids (np.ndarray): Array of experiment IDs - data (np.ndarray): Combined array of current and voltage features """ logging.info(f"Converting data to numpy array") labels, exp_ids = data["labels"].values, data["exp_ids"].values data = data.drop(columns=["labels", "exp_ids"]) cols_v = data.columns[data.columns.str.startswith("V")] cols_i = data.columns[data.columns.str.startswith("I")] current_data = data[cols_i].values voltage_data = data[cols_v].values data = np.stack([current_data, voltage_data], axis=2) return labels, exp_ids, data data_path = "" data = pd.read_csv(data_path) labels, exp_ids, data = convert_to_np(data)