To solve the current problem of poor weld formation due to groove width variation in swing arc narrow gap welding, an infrared passive visual sensing detection approach was developed in this work to measure groove width under intense welding interferences. This approach, called global pattern recognition, includes self-adaptive positioning of the ROI window, equal division thresholding and in situ dynamic clustering algorithms. Accordingly, the self-adaptive positioning method filters several of the nearest values of the arc’s highest point of the vertical coordinate and groove’s same-side edge position to determine the origin coordinates of the ROI window; the equal division thresholding algorithm then divides and processes the ROI window image to extract the groove edge and forms a raw data distribution of groove width in the data window. The in situ dynamic clustering algorithm dynamically classifies the preprocessed data in situ and finally detects the value of the groove width from the remaining true data. Experimental results show that the equal division thresholding algorithm can effectively reduce the influences of arc light and welding fume on the extraction of the groove edge. The in situ dynamic clustering algorithm can avoid disturbances from simulated welding spatters with diameters less than 2.19 mm, thus realizing the high-precision detection of the actual groove width and demonstrating stronger environmental adaptability of the proposed global pattern recognition approach.

To enhance the synchronous detection of the horizontal and vertical positions of the torch in swing arc narrow gap welding, a torch pose detection (TPD) method is proposed. This approach utilizes passive visual sensing to capture images of the arc on the groove sidewall, using advanced image processing methods to extract and fit the arc contour. The coordinates of the arc contour center point and the highest point are determined through the arc contour fitting line. The torch center position is calculated from the average horizontal coordinates of the arc contour centers in adjacent welding images, while the height position is determined from the vertical coordinate of the arc’s highest point. Experimental validation in both variable and constant groove welding conditions demonstrated the TPD method’s accuracy within 0.32 mm for detecting the torch center position. This method eliminates the need to construct the wire centerline, which was a requirement in previous approaches, thereby reducing the impact of wire straightness on detection accuracy. The proposed TPD method successfully achieves simultaneous detection of the torch center and height positions, laying the foundation for intelligent detection and adaptive control in swing arc narrow gap welding.

This study systematically explores the innovative applications and mechanisms of artificial intelligence (AI) in interdisciplinary collaborative education to reconstruct interdisciplinary learning systems. The research focuses on three core dimensions: Intelligent Personalized Learning: AI dynamically optimizes interdisciplinary learning paths based on learning analytic and adaptive algorithms, improving knowledge integration efficiency. Collaborative Learning Platforms: Utilizing multi-agent matching and knowledge graph technologies, AI facilitates efficient knowledge construction in interdisciplinary teams. Multidimensional Dynamic Assessment: Combining cognitive analytics, behavioral analytics, and machine learning algorithms, AI enables precise learning evaluation and adaptive feedback. The findings demonstrate that AI technologies significantly enhance students' interdisciplinary problem-solving abilities and team collaboration efficacy, while effectively bridging traditional disciplinary divides. This study provides a theoretical framework and practical pathway for transforming interdisciplinary education in the AI era, offering critical insights for cultivating interdisciplinary innovators.

Amid rapid technological advancements, the wave of artificial intelligence (AI) is sweeping across various fields, with its applications in education becoming increasingly extensive and profound. Industry-specialized universities, as key institutions for cultivating professionals in specific sectors, place great emphasis on fostering students’ innovative and practical abilities. This study focuses on universities specializing in naval architecture and ocean engineering, exploring the pathways for AI to empower the cultivation of students’ innovation and practical skills. It provides a detailed analysis of the current applications and future trends of AI in naval architecture and ocean engineering, highlighting its multidimensional significance in enhancing students’ innovative and practical capabilities. Furthermore, the study proposes comprehensive and actionable cultivation strategies, covering curriculum design, practical platforms, and teaching methods. The findings aim to offer operational and forward-looking insights for industry-specialized universities to leverage AI in boosting students’ innovation and practical competencies.