Antonio Giaquinto

A Neurofuzzy Method for the Evaluation of Soldering Global Quality Index

Recently, surface mount technology is extensively used in the production of printed circuit boards due to the high level of miniaturization and to the increase of density in the electronic device integration. In such production process several defects could occur on the final electronic components, compromising its correct working. In this paper a neurofuzzy solution to process information deriving from an automatic optical system is proposed. The designed system provides a global quality index of a solder joint, starting from the assessment of a human inspector. This target is achieved by reproducing the modus operandi of the expert, evaluating the area, the shape and the barycentre position of a solder joint. The proposed architecture is constituted by three supervised neural networks and two fuzzy rule-based modules which automate experts work and provide a refined evaluation of the quality. The considered solution presents some attractive advantages: a complex acquisition system is not needed, equipment costs could be reduced by shifting the assessment of a solder joint on the fuzzy parts. Moreover, intermediate variables used in the method could be employed as control parameters in the production process under analysis.

A Fuzzy Method for Global Quality Index Evaluation of Solder Joints in Surface Mount Technology

In recent years, the requirement of compact devices caused an increasing use of Surface Mount Technology. This technology guarantees the reduction of the size of electronic packages by exploiting solder joint interconnection technology. Nevertheless, parameter variations can occur during the deposition and printing of the soldering paste on a board, compromising its correct working. In this paper, it is proposed a fuzzy architecture for computing an index which provides a quantitative refined assessment about the quality of the soldered interconnections. This task is performed by reproducing the modus operandi of the human experts during their assessments. The proposed architecture consists of three modules connected in series: a feature extraction block and two fuzzy ones. The presented solution keeps the benefits of a neurofuzzy system previously proposed in literature, like the reduction of equipment and computational costs. Moreover, it implies two further advantages: the influence of the human experts in its design is reduced and its implementation is reasonable. Experimental results confirm such advantages, in fact, the architecture approximates the human assessments reliably.

Adaptive particle swarm optimization for CNN associative memories design

In this paper particle swarm optimization is used to implement a synthesis procedure for cellular neural networks autoassociative memories. The use of this optimization technique allows a global search for computing the model parameters that identify designed memories, providing a synthesis procedure that takes into account the robustness of the solution. In particular, the design parameters can be modified during the convergence in order to guarantee minimum recall performances of the network in terms of robustness to noise overlapped to input patterns. Numerical results confirm the good performances of the designed networks when patterns are affected by different kinds of noise.

Angular and axial evaluation of superficial defects on non-accessible pipes by wavelet transform and neural network-based classification

In this paper an effective procedure that allows evaluating the dimensions of corrosive flaws on non-accessible pipes is presented. The method is based on the propagation of ultrasound waves, analyzing the informative content of echoes reflected by defects. The approach exploits the properties of the wavelet transform to represent signals by a reduced form. The coefficients of this representation are selected properly by making use of a filter method followed by a genetic algorithm and, then, they feed a neural network classifier which evaluates the dimensions of defects on the pipe under test. Numerical results show low error rates in the evaluation of both angular and axial extension of each flaw. The main advantage offered by the method consists of analyzing long lines of non-accessible pipes, realizing an automatic evaluation of the dimensions of superficial flaws in pipelines.

Nondestructive evaluation of defects in concrete structures based on finite element simulations of ultrasonic wave propagation

Concrete structures require periodic inspections and quality control to assess their structural integrity. For this task, several methodologies based on the use of different technologies have been previously proposed. In particular, nondestructive evaluations, based on the use of ultrasonic wave propagation, have proved to be attractive due to the possibility to perform reliable assessments of concrete structures. In this paper, an approach exploiting ultrasonic propagation characteristics is proposed. This approach is developed by using data obtained from numerical simulations, whereas proper simulation settings to perform the diagnosis are obtained by preliminary studies. Subsequently, an automatic inspection method to determine the position of defects is developed. The aim of the method consists of determining the position of a defect by the computation of flight times related to signals reflected by anomalies in the structure. Such computation is based on a preliminary classification of defect positions that combines a genetic algorithm for a feature selection and a statistical approach for classification. The performances of the proposed method are evaluated in a specific study case, showing satisfactory numerical results, which show that this approach can be used to identify the position of defects.

An unsupervised multi-swarm clustering technique for image segmentation

Abstract Methods based on Particle Swarm Optimization represent efficient tools to solve a wide class of problems. In particular, they have been successfully applied to data clustering and image processing. In this paper a multi-swarm clustering technique to perform an image segmentation is proposed. The search of the gray levels segmenting the image is carried out by a two-stage procedure. The former is performed by a traditional swarm population, moving in the search space according to a minimum distance criterion. The latter exploits a structure composed by identical swarms that refine the solution of the previous step. The combination of the two swarm approaches allows to tackle the drawbacks of the classical paradigm without making use of a complex implementation. The method is unsupervised, since it identifies the actual number of gray levels to segment the image automatically. Such characteristic is fundamental in the application of image segmentation to real cases, where generally the optimal number of centers is not known a priori and the algorithms are required to face possible environment variations. The conducted experiments show that the proposed technique is able to yield adequate segmentations with a limited computational time, proving to be an interesting tool to face cases in which urgent time constraints have to be satisfied.

Particle swarm optimization-based approach for accurate evaluation of upconversion parameters in Er 3+ -doped fibers

In this Letter, a method for recovering homogeneous upconversion coefficients (HUCs) in Er(3+)-doped glasses and erbium-activated devices is illustrated. It is based on a particle swarm optimization (PSO) approach. The HUCs are calculated on the basis of known values of optical gain evaluated in different pumping conditions. The obtained numerical results proof that the proposed technique provides solutions that are very close to the expected values. Therefore the method constitutes a tool for the design and optimization of efficient rare-earth doped lasers and optical amplifiers. This approach can be considered a feasible and valid alternative method in the field of material science and optical engineering for determining HUCs and avoiding the employment of expensive equipment for the measurement of ion-ion interaction parameters.

PSO-Based Cloning Template Design for CNN Associative Memories

In this brief, a synthesis procedure for cellular neural networks (CNNs) with space-invariant cloning templates is proposed. The design algorithm is based on the use of the evolutionary algorithm of the particle swarm optimization (PSO) with the application to associative memories. The proposed synthesis procedure takes into account requirements in terms of robustness to parametric variations. Numerical results show that the networks also guarantee good performances in terms of correct recall in the presence of noisy patterns.

Non-Destructive Technique for Defect Localization in Concrete Structures Based on Ultrasonic Wave Propagation

Concrete structures require periodic inspections and quality control to assess their structural integrity. For this task several methodologies based on different technologies have been previously proposed. In particular, Non-Destructive Techniques, based on the use of ultrasonic wave propagation, have revealed attractive due to the possibility to perform reliable assessments of concrete structures. In this paper a method exploiting ultrasonic propagation characteristics is proposed. The aim of the method consists of determining the position of a defect by the computation of flight times related to signals reflected by anomalies in the structure. Such computation is based on a preliminary classification of defect positions that combines a genetic algorithm for a feature selection and a statistical approach for classification. The performances of the proposed method are evaluated in a specific case study, showing satisfactory numerical results, which show that this approach can be used to identify the position of small sized defects.

A supervised method for the automatic detection of impulsive noise in naval powerline communications

The use of powerline communications has attracted the attention of researchers increasingly, since this kind of data transmission allows to reduce the number of cables. This advantage is particularly appealing in railway, naval and aeronautical fields. In this paper a method to detect noisy events that occur in a powerline communication system in a naval environment is proposed. To this aim, a model of the powerline used to transmit wideband signals is employed to simulate different possible noisy conditions. The channel is analyzed in the presence of three types of noise which characterize the system. A supervised technique based on the use of principal component analysis and a k-mean classifier is developed to automatically detect the presence and classify the specific noise which occurs during the transmission. Numerical simulations show that the proposed approach enables to classify the occurring of noisy events reliably.