Optical Non-Destructive Surface Inspection and Automatic Classification of Cast Iron Automotive Part

Borwankar, Raunak

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Optical Non-Destructive Surface Inspection and Automatic Classification of Cast Iron Automotive Part

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Over the past decade, research into computer vision has proliferated with the goal to incorporate artificial intelligence into a wide range of applications. These applications can be as sophisticated as intelligent assistants in smartphones and self-driving cars or as mundane as text and face recognition. While most of these applications are software based, they represent unique challenges when it comes to industrial implementation. This thesis concentrates on an optical non-destructive testing (NDT) and automatic classification methodology using customized image processing techniques. In contrast to conventional spatial analyses, which are highly susceptible to noise and human perception, our proposed transform domain approach provides a high degree of robustness and flexibility in feature selection and hence a better classification efficiency. Our presented algorithm classifies the Part-Under-Test (PUT) into two bins of either acceptable or faulty using transform domain techniques in conjunction with a classifier. Because the classification is critically dependent on the features extracted from these images, a sophisticated scalable database was created. This thesis applies transform domain techniques such as Discrete Wavelet Transform (DWT) and Rotated Wavelet Transform (RWT) for feature extraction and then classifies the PUT based on those features. Although, this approach achieves promising classification efficiency, it does not meet industrial standards. It was concluded that in order to achieve those standards, the effect of emissivity fluctuations of the PUT should be negated. The research was then extended to apply an image acquisition algorithm in the form of shape from polarization. The approach exploits the partially linearly polarization of reflected light from the PUT surface. It was observed that this method could not only detect if the PUT is faulty or fault free, but also highlight the locations of the flaws.

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