To Örebro University

Online weld joint inspection by non-destructive testing is necessary for modern joining industries. Nondestructive testing gained popularity through its dominance in examinations and reliability in confirming the part’s excellence. Joining dissimilar metals is preferable in industries due to reduction in the mass of components and less cost of manufacturing using the safety and structural requirements in various applications ranging from automotive to railway and naval trades. The weld joint imperfection examination plays a significant role in the manufacturing industry. A setup of Gas Tungsten Arc Welding (GTAW) has been proposed for joining stainless steel grades of 316L, 310 and 410 thick sheets of 150 × 60 × 3 mm using variable process parameters. An autonomous technique known as Computer Aided Graphical User Interface (CAGUI) has been proposed for online detection and classification of multiform weld joint imperfections precisely comprising of crack, undercut, gas pores, porosity, tungsten inclusion, wormholes, lack of penetration, and non-defects in radiographic images using Support Vector Machine (SVM) and Recurrent Neural Network (RNN) developed using a MATLAB workbench. The support vector machine classifier has classified the weld images by finding the best hyperplane that separates all the weld joint images into defects and non-defect classes. SVM has classified the weld joint defects and non-defect images and confirmed their accuracy performance as 97.50% using the confusion matrix. It confirmed the lack of penetration defects are erroneous for gas pores. A RNN classifier handles the nonlinear weld joint images along with the parallel processing of information and flexibility in system. The feedforward neural network classified weld joint defects and non-defect and confirmed their accuracy performance as 98.75% using a confusion matrix. The confusion matrix confirmed that the lack of penetration defects is erroneous for undercut. The proposed CAGUI improved the computation period without disturbing the correctness of features selection. 

In this paper we show how relational representations of design structure matrices (DSM), on the one hand, enables to describe domain dependencies and connections as relational composition, and, on the other hand, invites to using a variety of algebraic structures for the sets of qualifications attached with non-binary matrices. Particularly, we use fuzzy sets of higher types to model qualifications in many-valued DSMs where compositional techniques allow for extending the use of fuzzy sets of higher types also in the setting of multidomain matrices (MDM). We further show how clustered domains can be embedded as modelled within powersets of domains, thus providing a further justification for adopting the relational view of DSMs, particularly as the qualification space needs to support folding and unfolding across hierarchies in clustered domains. Our case study is drawn from scenarios involving maintenance of equipment in mineral mining.

Automated inspection in lithium-ion battery manufacturing has emerged as a significant enabler of quality assurance, process optimization, and operational efficiency. Defect detection at several stages from electrode manufacturing to cell packaging has a direct impact on battery safety, performance, and durability. Traditional machine vision approaches were early solutions for detecting electrode flaws, but recent improvements in deep learning, hybrid image processing, and AI assisted inspection have considerably improved accuracy, resilience, and real-time capabilities. This study proposes a comprehensive automated inspection framework that incorporates 3D structured-light profiling, high-speed 2D line-scan imaging, and edge-computing-enabled analytics throughout the production process. The system controls coating uniformity, calendering integrity, geometric precision, electrode alignment, and weld quality, all while ensuring comprehensive digital traceability via MES integration. Vision-guided robotic handling improves assembly accuracy, throughput, and process reliability. By merging multidimensional sensing modalities with AI-driven analysis, the proposed framework assures high-throughput, defect-free battery manufacture while lowering waste, boosting sustainability, and promoting Industry 5.0 digitalization.

In this paper, we open up more possibilities to define higher types of fuzzy sets in a mixed way. In doing so, we show that there are essentially two alternative definitions for fuzzy sets of higher types, one of which is widely adopted in the literature, and one where the unit interval, or subsets thereof, is used to represent membership values. The other alternative definition opens up new perspectives for the use of fuzzy sets of higher types, and it promotes the use of other algebraic structures of sets of membership values, where quantales are seen as particularly useful, also in applications. The paper also underlines the importance of making distinctions between “computing with fuzzy” and “fuzzy computing” and understanding the difference between “logic with fuzzy” and “fuzzy logic”.