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”.

The fault detection system using automated concepts is a crucial aspect of the industrial process. The automated system can contribute efficiently in minimizing equipment downtime therefore improving the production process cost. This paper highlights a novel model based fault detection (FD) approach combined with an interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy system for fault detection in the drilling process. The system uncertainty is considered prevailing during the process, and type-2 fuzzy methodology is utilized to deal with these uncertainties in an effective way. Two theorems are developed; Theorem 1, which proves the stability of the fuzzy modeling, and Theorem 2, which establishes the fault detector algorithm stability. A Lyapunov stabilty analysis is implemented for validating the stability criterion for Theorem 1 and Theorem 2. In order to validate the effective implementation of the complex theoretical approach, a numerical analysis is carried out at the end. The proposed methodology can be implemented in real time to detect faults in the drilling tool maintaining the stability of the proposed fault detection estimator. This is critical for increasing the productivity and quality of the machining process, and it also helps improve the surface finish of the work piece satisfying the customer needs and expectations.

In discrete event simulation (DES), the events are random (aleatory) and typically represented by a probability distribution that fits the real phenomena that is studied. The true distributions of event outcomes, which may be multivariate, are often dependent on the values of covariates and this relationship may be complex. Due to difficulties in representing the influence of covariates within DES models, often only the averaged distribution or expected value of the conditional distribution is used. However, this can reduce modelling accuracy and prevent the model from being used to study the influence of covariates. Distributional random forests (DRF) are a machine learning technique for predicting the multivariate conditional distribution of an outcome from the values of covariates using an ensemble of decision trees. In this paper, the benefits of utilizing DRF in DES are explored through comparison with alternative approaches in a model of a powder coating industrial process.

In discrete event simulation (DES) models, stochastic behaviour is modelled by sampling random variates from probability distributions to determine event outcomes. However, the distribution of outcomes for an event from a real system is often dynamic and dependent on the current system state. This paper proposes the use of artificial neural networks (ANN) in DES models to determine the current distribution of each event outcome, conditional on the current model state or input data, from which random variates can then be sampled. This enables more realistic and accurate modelling of stochastic behaviour. An application is in digital twin models that aim to closely mimic a real system by learning from its past behaviour and utilising current data to predict its future. The benefits of the approach introduced in this paper are demonstrated through a realistic DES model of load-haul-dump vehicle operations in a production area of a sublevel caving mine.

Digital twins (DT) form part of the Industry 4.0 revolution within manufacturing and related industries. A DT is a digital model (DM) of a real system that features continuous and automated synchronisation and feedback of optimisations between the real and digital domains. A core technology for predictive capabilities from DT is discrete event simulation (DES). The modelling cycle for developing and analysing DES models is significantly different compared to DM. A DT specific DES modelling cycle is introduced that is evolved from that of DM. The availability of specialised software tools for DT tailored to these differences would benefit industry.