Simulated Dataset for Edge-Based Defect Prediction in Robotic Welding

This dataset is a simulated collection of 300 samples representing real-time robotic welding process parameters. It includes arc voltage, welding current, welding speed, wire feed speed, gas flow rate, torch angle, and base metal temperature. Each entry is labeled with a binary defect tag (0 = no defect, 1 = defect) based on parameter thresholds that reflect known causes of welding quality issues. The dataset was generated using Python for research in edge computing applications, machine learning model development, and defect prediction in smart manufacturing environments. Note: This is a synthetic dataset created for academic use only.本数据集为模拟构建的300组样本集合，覆盖实时机器人焊接工艺参数，具体包含电弧电压、焊接电流、焊接速度、送丝速度、气体流量、焊枪角度及母材温度。 每条样本均基于反映已知焊接质量问题成因的参数阈值，标注二元缺陷标签（0表示无缺陷，1表示存在缺陷）。 本数据集通过Python生成，旨在支撑边缘计算应用、机器学习模型研发以及智能制造场景下的缺陷预测相关研究。 注：本数据集为仅用于学术用途的合成数据集。