Thermal Cycling Data For Benchmark Paper

This dataset supports the development and benchmarking of machine learning models for predicting the reliability and characteristic life of electronic components subjected to varying environmental and mechanical stressors. The data comprises numerically encoded features representing material properties (e.g., solder alloy and surface finish), test conditions (e.g., temperature, acceleration level), and the corresponding reliability outcomes (e.g., characteristic life). All categorical variables have been preprocessed and converted to numerical form to ensure compatibility with data-driven modeling frameworks.The primary goal of this dataset is to facilitate research in predictive reliability modeling, enabling the exploration of data-driven approaches as an alternative or complement to physics-based and empirical models. It is particularly well-suited for supervised learning applications, including regression and classification, and may also be used for feature selection, model interpretability studies, and benchmarking ensemble or deep learning architectures. This dataset contributes to the growing field of reliability-aware artificial intelligence by offering a clean, structured, and ready-to-use platform for reproducible experimentation.