Comprehensive Computational Framework for Intelligent Steel Surface Defect Management and Prediction: A Synergy of Front End Interactivity, Back-End Robustness, and Predictive Modeling

This research has constructed an intelligent management and prediction system for steel surface defects based on the integration of deep learning and machine learning. The system adopts a microservice architecture and innovatively integrates advanced frameworks such as Vue.js, Spring Boot, and Django to achieve an end-to-end intelligent solution from defect detection to prediction analysis. In the context of the intelligent transformation of the industry, the existing steel surface defect management systems have technical bottlenecks such as insufficient detection accuracy, poor real-time performance, and high maintenance costs. Traditional systems generally have problems like high architecture coupling, low modularization degree, and limited system scalability, making it difficult to meet the requirements of flexibility and precision in intelligent manufacturing. Specifically, the system integration architecture is imperfect, it is hard to balance real-time detection and accuracy optimization, the integration degree of intelligent algorithms is insufficient, the front-end and back-end architectures are deeply coupled, and the collaborative processing ability for multi-source heterogeneous data is limited. In response to the above problems, this research proposes a technical framework based on “deep decoupling and collaborative optimization of the front-end and back-end”. Through an innovative data flow orchestration strategy, the efficient integration of deep learning detection algorithms and machine learning prediction models is achieved. At the algorithm level, an adaptive multi-model integrated learning framework has been designed, integrating algorithms such as LSTM, BP neural network, Adaboost, and SVM to build an accurate recognition mechanism for complex nonlinear defect patterns. Meanwhile, based on the improved YOLOv5 algorithm, remarkable breakthroughs have been achieved in the real-time performance and accuracy of defect detection. Experimental verification shows that the system realizes the efficient integration of deep learning detection and machine learning prediction, verifying the effectiveness and practical value of the intelligent management and prediction system for steel surface defects. This research not only provides a systematic solution for the digital transformation of the steel industry but also offers a technical paradigm for the intelligent upgrading of the manufacturing industry, which has important theoretical value and practical significance for promoting the implementation of the Industry 4.0 strategy.