Concept selection of the automated inspection and maintenance test unit for the EU DEMO using a novel fuzzy-based decision support tool

Bongioví, G.; Grazioso, Stanislao; Jiménez, Samuel Olegario Iñiguez

doi:10.1016/j.fusengdes.2019.111324

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…It is fused on the classification results of a single CNN, which is more suitable for the surface defect classification of automobile pipe joints. However, the requirements of decision-level fusion for preprocessing are high [10]. Appropriate preprocessing methods can avoid wasting the energy of feature extraction on filtering interference information and can underline the features of the defects themselves, helping classification and fusion.…”

Section: Introductionmentioning

confidence: 99%

Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network

Yang

Zhang

et al. 2022

IEEE Access

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The surface defect detection of automobile pipe joints based on computer vision faces technical challenges. The tiny-sized and smooth surfaces with processing textures will undermine the defect detection accuracy. In order to solve this problem, a new method was proposed, which combines wavelet decomposition and reconstruction with the canny operator to detect defects, and then uses the multi-channel fusion convolutional neural network to identify the types of defects. Firstly, illumination compensation technology is used to obtain a more uniform gray distribution of the original image. Then, the wavelet decomposition and reconstruction are used to remove noises and processing textures. Furthermore, the defect regions are segmented using the canny operator and hole filling from the image. Finally, the multichannel fusion convolutional neural network of decision-level is used to identify the surface defect types. This method provides an idea for the surface defects detection of automobile pipe joints with serious interference, such as smooth surface, random noises, and processing textures. The experimental results reveal that the method can effectively eliminate the influence of uneven illumination, random noises, and processing textures and achieve high defect classification accuracy.INDEX TERMS Automobile pipe joint, surface defect detection, wavelet decomposition and reconstruction, multi-channel fusion convolutional neural network.

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Section: Introductionmentioning

confidence: 99%

Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network

Yang

Zhang

et al. 2022

IEEE Access

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Decision support in engineering design: the ELIGERE open source software platform

Grazioso,

Caporaso,

Di Gironimo

2023

Int J Interact Des Manuf

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In engineering design, the selection of the optimal design solution represents a critical phase for the development of successful products. In this paper, we present ELIGERE, an open source decision support system targeted at engineering design applications. It allows to rank multiple design solutions with respect to different evaluation criteria according to the evaluations provided by a group of experts. ELIGERE is composed by three main modules: (1) a distributed web application, for generation and participation to the decision making session; (2) a mathematical engine, based on the fuzzy analytical hierarchy process, to quantify the results of the decision making session according to the evaluation of the experts; (3) a relational database, to collect and store data. The most important contribution of this paper is introducing a practical and effective software tool that facilitates decision-making analysis based on the fuzzy analytical hierarchy process, thereby allowing better-informed choices on concept selection, as it has been designed with a specific focus on the engineering field. In this paper we describe the key concepts of ELIGERE and its modalities of use in several real use cases. Finally, we compare ELIGERE with the widely used general purpose decision support software based on the fuzzy analytical hierarchy process.

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Progresses in the structural assessment of the central region of a HELIAS 5-B breeding blanket half sector

Bongioví

Häußler

2020

Fusion Engineering and Design

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Concept selection of the automated inspection and maintenance test unit for the EU DEMO using a novel fuzzy-based decision support tool

Cited by 6 publications

References 27 publications

Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network

Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network

Decision support in engineering design: the ELIGERE open source software platform

Progresses in the structural assessment of the central region of a HELIAS 5-B breeding blanket half sector

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