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)