Multidimensional Discretization??? Event-Codification ????????? ????????? ????????? ?????? ?????? ??????

In the literature, various stochastic anomaly detection methods, such as limit checking and PCAbased approaches, have been applied to weld defect detection. However, it is still a challenge to identify meaningful defect patterns from very limited sensor signals of laser welding, characterized by intermittent, discontinuous, very short, and non-stationary random signals. In order to effectively analyze the physical characteristics of laser weld signals: plasma intensity, weld pool temperature, and back reflection, we first transform the raw data of laser weld signals into the form of event logs. This is done by multidimensional discretization and event-codification, after which the event logs are decoded to extract weld defect patterns by Naïve Bayes classifier. The performance of the proposed method is examined in comparison with the commercial solution of PRECITEC???s LWM TM and the most recent PCA-based detection method. The results show higher performance of the proposed method in terms of sensitivity (1.00) and specificity (0.98).clos

Welding of deep-drawing steels with a protective layer by means of an oscillating laser beam

Práce se zabývá výzkumem vlivu procesních parametrů svařování laserem s rozmítáním svazku na vlastnosti svarového spoje pozinkované hlubokotažné oceli. V první části je vytvořen přehled o technologii laserového svařování, vysvětlena problematika svařování ocelí s ochrannou Zn vrstvou a popsána metodika zkoušení svarových spojů. Druhá část se věnuje návrhu, průběhu a vyhodnocení experimentu, při kterém bylo zhotoveno sedm zkušebních svarů oceli s označením DC04+ZE v podobě přeplátovaných spojů tenkých plechů za využití různých parametrů rozmítání. Ze svarů byla odebrána řada vzorků, na kterých je provedena metalografická zkouška pro vyhodnocení makrostruktury a tahová zkouška pro posouzení mechanických vlastností spoje.This thesis deals with the topic of procedural parameters influence on the properties of welded joint in zinc-coated deep-drawing steels in laser welding with wobbling. First part of the thesis serves as an overview of laser welding technology, welding of steel with protective zinc layer and weld quality testing. Second part is dedicated to a proposal, process flow and evaluation of an experiment. Seven trial lap welding joints of DC04+ZE steel have been welded using different welding parameters. Specimens from these trial welds were used for metallurgical testing by macro-etch examination and for tensile strength break testing to get mechanical properties of welded joints.

Detec??o de descontinuidades no processo de soldagem por eletrodo revestido por meio de intelig?ncia computacional.

Programa de P?s-Gradua??o em Engenharia de Materiais. Departamento de Engenharia Metal?rgica, Escola de Minas, Universidade Federal de Ouro Preto.Prop?e-se neste trabalho uma nova metodologia para a detec??o de descontinuidades no cord?o de solda aplicado em processos de soldagem por eletrodos revestidos (SMAW). Para a execu??o dos experimentos e otimiza??o de par?metros do processo, foi desenvolvida uma esta??o de soldagem robotizada. O sistema de detec??o baseia-se em dois sensores ? um microfone e um cristal piezoel?trico ? que adquirem as emiss?es ac?sticas geradas durante a soldagem. Os vetores de caracter?sticas extra?dos do conjunto de dados dos sensores s?o usados para construir os modelos dos classificadores. As abordagens baseadas nos classificadores de Rede Neural Artificial (RNA) e de M?quina de Vetor de Suporte (SVM) s?o capazes de identificar com alta acur?cia as tr?s classes propostas de cord?es de solda: cord?o de solda normal, e descontinuidades de cratera e de perfura??o. Os resultados experimentais ilustram a acur?cia do sistema, superior a 83% para cada classe. Uma nova estrutura de m?quinas de suporte hier?rquico (HSVM) ? proposta para viabilizar o uso deste sistema em ambientes industriais. Esta abordagem apresentou 96,6% de acur?cia global. Este sistema pode ser aplicado nas ind?strias metal-mec?nicas.One proposes in this work a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. A robotized welding station was developed for the execution of the experiments and optimization of process parameters. The detection system is based on two sensors ? a microphone and piezoelectric ? that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system?s accuracy, greater than 83% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. This system can be applied in the metal-mechanical industries

Methods for Quality Monitoring in Ultrasonic Welding of Carbon Fiber Reinforced Polymer Composites

Carbon fiber reinforced composites have been increasingly used in various industrial sectors, especially in the automotive industry. Ultrasonic welding is considered as an effective approach to joining such composites. Reliable weld quality classification and prediction methods are needed to ensure quality and reduce manufacturing costs. However, existing methods have two weaknesses. The first one is that the majority of the existing methods are based on signal feature data extracted from the original experimental time-series data. Feature-based models may not take full advantage of the information contained in the large amounts of time-series data available, even though the models are simple and easy to program. On the other hand, when using experimental time-series data to conduct weld quality monitoring, the data size may be insufficient for training neural network-based methods for quality monitoring or classification. Therefore, a method is needed to augment experimental data while preserving the statistical characteristics of the experimental data. To find reliable quality monitoring models in various situations, this dissertation proposes two neural network models that are respectively applied to feature-based data and full time-series-based data and compares their performances. The dissertation first investigates the relationship between weld energy and joint performance in ultrasonic welding of carbon fiber reinforced polymer (CFRP) sheets through weld experiments. The weld quality classes for training quality monitoring algorithms are determined from welded joint lap-shear strength and the microstructure of the weld zone. These pre-defined weld quality classes are the output criteria for weld quality monitoring on feature-based models and time-series-based models. For feature- based weld quality monitoring, a simple and efficient feature selection method is first developed to screen the most significant features for classification from multiple weld quality classes. A Bayesian regularized neural network (BRNN) is then demonstrated to be more accurate and robust when classifying weld quality classes in ultrasonic composite welding when using feature-based data as the input than the previously proposed methods of support vector machine (SVM), k-nearest neighbors (kNN), and linear discriminant analysis (LDA). To address the limited size of experimental data, a Multivariate Monte Carlo (MMC) simulation with copulas approach is proposed to reasonably generate large amounts of time-series process signals for ultrasonic composite welding. With both experimental data and a large quantity of simulated data, a deep convolutional neural network (CNN) is applied to weld quality classification. The CNN model is found to be more accurate and robust, not only under small training data set sizes, but also under large training data set sizes when compared with previously researched classification methods applied in ultrasonic welding. In conclusion, neural network-based models could achieve high accuracy using feature signals and the full time-series process signals.Ph.D.Manufacturing EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/168232/1/Dissertation_Lei Sun.pd