Yousef Nabhan, Sata Amit, Shukla Minal, Jarboui S, Mobarsa Divya
Department of Mechanical Engineering, Marwadi University, Rajkot, India.
Blockchain Project Manager, MGL Group, Rajkot, India.
Sci Rep. 2025 Feb 12;15(1):5300. doi: 10.1038/s41598-025-86777-3.
The quality control of investment casting remains a critical challenge due to defect detection, real-time processing, and data traceability inefficiencies. This study presents an innovative Blockchain-integrated IoT system for advanced inspection of casting defects, combining a ResNet-based deep learning model for defect detection and dimensional measurement with Blockchain technology to ensure data integrity and traceability. The system demonstrated a significant improvement in defect detection accuracy, achieving an F1-score of 0.94, alongside high data integrity (0.99) and traceability (0.98) metrics. Additionally, it processes each casting in an average of 2.3 s, supporting a throughput of 26 castings per minute. By addressing critical challenges in smart manufacturing, this approach enhances operational efficiency, regulatory compliance, and user confidence. While scalability and energy efficiency remain areas for improvement, the proposed method provides a transformative solution for Industry 4.0, fostering transparency and reliability in manufacturing processes.
由于在缺陷检测、实时处理和数据可追溯性方面存在效率低下的问题，熔模铸造的质量控制仍然是一项严峻的挑战。本研究提出了一种创新的区块链集成物联网系统，用于铸件缺陷的高级检测，该系统将基于ResNet的深度学习模型用于缺陷检测和尺寸测量，并结合区块链技术以确保数据完整性和可追溯性。该系统在缺陷检测精度方面有显著提高，F1分数达到0.94，同时具有较高的数据完整性（0.99）和可追溯性（0.98）指标。此外，它平均每2.3秒处理一个铸件，支持每分钟26个铸件的吞吐量。通过解决智能制造中的关键挑战，这种方法提高了运营效率、合规性和用户信心。虽然可扩展性和能源效率仍有待改进，但所提出的方法为工业4.0提供了一种变革性的解决方案，促进了制造过程的透明度和可靠性。
