An MCDM framework for the classification of features from ultrasonic images of plastic pipe welds

H Yusuf1, 2, G Panoutsos2 and C Nageswaran1
1TWI Ltd, UK
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2The University of Sheffield, UK
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Multi-criteria decision-making (MCDM) is a set of computational and mathematical techniques for assessing a set of choices, based on often several conflicting criteria such as various costs and benefits. MCDM can be used to aid the decision-making process and attain a better understanding of the problem. MCDM as a discipline covers a wide range of topics, including mathematics, psychology and economics. The literature shows that a great deal was done in the effort of improving MCDM for various everyday decision-making applications, such as selecting the best supplier or deciding which software package to install. However, not a great deal of literature demonstrates MCDM being used for classification applications such as: defect recognition or medical diagnosis.

One of the applications for defect recognition is plastic pipe welds. In the industry, BF welds are inspected using phased array ultrasonic testing (PAUT) where a non-destructive testing (NDT) operator will collect the data and an NDT analyst would check the data for signs of defects. The process is time-consuming and can be fairly subjective. To reduce the time required for analysing the data, a model will be developed that will be able to classify the features as either defective or healthy. The model will use an MCDM technique that is data-driven and based on expert knowledge. The classification model will use extracted features from ultrasonic images of BF welds. After training the model using a training dataset, a testing dataset will be used to assess its performance.