Traditional welding quality inspection methods for pipelines and pressure vessels are time-consuming, labor-intensive, and suffer from false and missed inspection problems. With the development of smart manufacturing, there is a need for fast and accurate in-situ inspection of welding quality. Therefore, detection models with higher accuracy and lower computational complexity are required for technical support. Based on that, an in-situ weld surface defect recognition method is proposed in this paper based on an improved lightweight MobileNetV2 algorithm. It builds a defect classification model with MobileNetV2 as the backbone of the network, embeds a Convolutional Block Attention Module (CBAM) to refine the image feature information, and reduces the network width factor to cut down the number of model parameters and computational complexity. The experimental results show that the proposed weld surface defect recognition method has advantages in both recognition accuracy and computational efficiency. In summary, the method in this paper overcomes the limitations of traditional methods and achieves the goal of reducing labor intensity, saving time, and improving accuracy. It meets the actual needs of in-situ weld surface defect recognition for pipelines, pressure vessels, and other industrial complex products.

Trustworthy product quality data forms the foundation of digital and distributed manufacturing, yet current centralized product quality management (PQM) systems remain vulnerable to data manipulation, traceability breaks, single points of failure, and related adverse effects. To clarify how blockchain can address these weaknesses, this paper presents a systematic review of blockchained product quality management (BPQM). Firstly, the paper groups the architectures and models related to BPQM and proposes an ISA 95-aligned reference framework that secures a real-time quality data exchange. Secondly, seven key BPQM enablers are analyzed, including (1) visual intelligence-based quality inspection, (2) cyber–physical twinning and parallel control of manufacturing systems, (3) blockchained agent modeling and secure data sharing, (4) multi-level blockchain mapping, (5) smart contract-based decentralized system configuration and operation, (6) artificial intelligence-based decentralized BPQM applications, and (7) traceability of process coordination and control. Thirdly, through analysis of social barriers and technological challenges, four research directions are identified, namely, (1) optimal granularity of data in system configuration; (2) smart contracts for self-organizing intelligence; (3) balancing system security, cost, and performance; and (4) interoperability and integration with legacy systems. It is expected that this paper lays a solid foundation for the practical use of blockchain in PQM engineering.