An expert system approach to fracture mechanics

Menandro, Fernando César Meira; Moyer, E. T.; Liebowitz, Harold

doi:10.1016/0013-7944(94)e0049-m

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…En primer instancia se evaluó la factibilidad para el desarrollo del SE en AF empleando para esto el procedimiento propuesto por Beckman (1991). Con base en el resultado positivo de esta evaluación, se estructuró la forma en que se debería desarrollar el sistema (Cerrud, 2002;Jacobo et a.l, 2003) , 1995a, 1995b, 1997a, 1997cKazaz, 2003;Komai et al, 1995;Lee, 2001;Liao et al, 1999aLiao et al, , 1999bLiao, 2000Liao, , 2004Menandro et al, 1995;Roberge et al, 1996). Asimismo, se consideraron los resultados emanados por Shu-Hsien (2005), el cual revisó las metodologías para el desarrollo de SE´s aplicadas en el período 1995-2004.…”

Section: Metodología De Desarrollo Del Seafemunclassified

SEAFEM: Herramienta computacional para la determinación de causas de falla en elementos mecánicos metálicos

Jacobo-Armendáriz¹,

Ortiz-Prado²,

Schouwenaars-Franssens³

2007

IIT

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experto híbrido (SEAFEM) para análisis de falla de este tipo de elementos (engranes, ejes, resortes, cojinetes, entre otros). Esta herramienta de tipo colega, permite por un lado, encaminar al usuario a la determinación de las causas de falla, y a su vez, capacitar personal a través de su uso. El programa funciona de forma interactiva, plantea preguntas que tendrán que ser contestadas por el usuario y lo encamina hacia la solución del problema. Lo novedoso de este sistema, es el empleo de dos métodos de razonamiento (basado en reglas y basado en casos), lo cual representa una ventaja respecto de otros anteriormente desarrollados, ya que permite al usuario utilizar y ejercitar sus procesos de razonamiento deductivo y el manejo de información estructurada en bases de datos. Éste es un proceso similar al que realiza un experto, ya que establece un proceso metodológico en la solución del problema y utiliza el conocimiento de otros casos que ha resuelto o que se reportan en la literatura. En este artículo, se presenta la metodología de desarrollo del SEAFEM, su estructura de conocimiento, funcionamiento y la aplicación en la solución de problemas de análisis de falla en ejes y barras de torsión. Descriptores: Análisis de falla, metalurgia mecánica, sistemas expertos, inteligencia arti fi cial, razonamiento basado en casos, razonamiento basado en reglas.

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Section: Metodología De Desarrollo Del Seafemunclassified

SEAFEM: Herramienta computacional para la determinación de causas de falla en elementos mecánicos metálicos

Jacobo-Armendáriz¹,

Ortiz-Prado²,

Schouwenaars-Franssens³

2007

IIT

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“…The early trial of the artificial intelligence (AI) approach to solve the two-dimensional (2D) fracture mechanism was developed by expert system method. [5][6][7] However, this pioneered research had a limited knowledge domain and was developed for specific problems. 8 Also, fractography with the scanning electron microscope (SEM) has been proved to be an effective method for failure analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The early trial of the artificial intelligence (AI) approach to solve the two‐dimensional (2D) fracture mechanism was developed by expert system method 5–7 . However, this pioneered research had a limited knowledge domain and was developed for specific problems 8 .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence‐assisted fatigue fracture recognition based on morphing and fully convolutional networks

Lyu

Yang

Liang

et al. 2022

Fatigue Fract Eng Mat Struct

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Fatigue fracture is one of the most common metallic component failure cases in manufacturing industries. The observation on fractography can provide direct evidence for failure analysis. In this study, an image semantic segmentation method based on fully convolutional networks (FCNs) was proposed to figure out the boundary between fatigue crack propagation and fast fracture regions from optical microscope (OM) fractography images. Furthermore, a novel morphing‐based data augmentation method was adopted to enable few‐shot learning of sample images. The proposed framework can successfully segment two categories, namely, the crack propagation and fast fracture regions, thus differentiating the boundary of two regions in one image. This artificial intelligence (AI)‐assisted fatigue analysis architecture can complete the failure analysis procedure in 0.5 s and prove the feasibility of fatigue failure analysis. The segmentation accuracy of self‐developed network achieves 95.4% for the fatigue crack propagation region, as well as 97.2% for the fast fracture region. Not only for semantic segmentation DNN, we also prove that our novel data augmentation method can applied at the instance segmentation DNN, such as mask regional convolutional neural network (mask R‐CNN), one state‐of‐the‐art deep learning network for instance segmentation, to achieve similar accuracy.

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“…For determining the fracture characteristic of materials and structures, knowledge-based system could be very useful, as shown by H.Liebowitz [1]. Several expert systems (ES) related to fracture mechanics were developed [2,3,4], where the usefulness of an AI approach to some deterministic problems were discussed and some ES approach to fracture mechanics problems were devised.…”

Section: Introductionmentioning

confidence: 99%

Web-Based Expert System in Fatigue and Fracture Analysis

Ren

Zhu

et al. 2005

KEM

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An expert system in fatigue and fracture analysis based on Web is developed in this paper. The system consists of six functional parts: a general inference engine, some knowledge bases, a context, an interpretation engine, a bases administration system and the interface (includes browser, Web-server, etc.). It can simulate a human expert to make analysis and design scheme for some typical structural components: pressure vessels, huge rotation constructions, welded structures, etc. It is an open system which may be broadened and perfected to cover a wider range of application through the modification and enlargement of the knowledge and data bases on Web.

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An expert system approach to fracture mechanics

Cited by 6 publications

References 4 publications

SEAFEM: Herramienta computacional para la determinación de causas de falla en elementos mecánicos metálicos

SEAFEM: Herramienta computacional para la determinación de causas de falla en elementos mecánicos metálicos

Artificial intelligence‐assisted fatigue fracture recognition based on morphing and fully convolutional networks

Web-Based Expert System in Fatigue and Fracture Analysis

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