Predictive Modeling for Casting Surface Defects Using CNN-Random Forest Fusion

In the abstract, The article examines a classification model developed to find and classify surface flaws in casting processes, with an emphasis on five unique defect classes: strike, scar, scab, drop, or penetration. The efficacy of the model is thoroughly tested using accuracy, recall, or F1-Score metrics, and support values representing the number of examples in each class. The support proportion highlights the relative prevalence of each class in the sample. The model achieves impressive precision, and recall, with F1-Score values ranging from 97.74% to 98.22% across the various fault classes. The support values vary between 1335 to 1405, indicating the diversity in instances in each class. A precision of 99% demonstrates the model's general ability to make correct predictions. The macro-average, weighted-average, or micro-average metrics provide a comprehensive view of the model's performance, demonstrating its uniformity in predicting defects across categories. The investigation continues by emphasizing the model's durability and possible uses in real-world circumstances, demonstrating its dependability in detecting casting surface defects. The suggested model's high accuracy, as well as recall values, indicate that it can transform inspection processes in the metallurgical industry, resulting in improved product safety, effectiveness, and quality.