Dr. Sunil Kancharana Profile

Dr. Sunil Kancharana

at Technische Hochschule Ingolstadt

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Proceedings Article | 5 March 2022 Paper

Image-based damage detection on TiN-coated milling tools by using a multi-light scattering illumination technique

Mühenad Bilal, Sunil Kancharana, Christian Mayer, Markus Bregulla, Adam Ziębiński, Rafal Cupek

Proceedings Volume 12084, 120840O (2022) https://doi.org/10.1117/12.2623140

KEYWORDS: Damage detection, RGB color model, Image segmentation, Optical microscopes, Image processing, 3D modeling, Inspection, Detection and tracking algorithms, Tin, Scattering

Read Abstract +

It is crucial for early detection of damage or wears on tools, to perform any manufacturing operation on a work-piece with good quality and precision. If tool cutting edges are worn or damaged, they can be reground instead of opting for a new tool. This saves a lot of machining costs for the company. Traditionally the damage detection is done by manual inspection with the help of an optical microscope and the damage locations are reground using a CNC machine. However, damage detection on coated milling tool using an optical microscope is time taking process and quite challenging due to factors such as non-homogeneous illumination intensity, a huge amount of reflections captured by the camera system, and different damage formations. Therefore, a novel approach has been proposed in this paper where automatic image-based damage detection of optical critical components such as TiN (Titanium Nitride) coated milling tools is done by using a new lighting source. The illumination source where a Cylindrical Shaped Enclosure (CSE) with 14 multi-spectral Light Emitting Diodes (LED) distributed uniformly around its circumference to enhance multi-light scattering allows to capture high-quality images with high resolution, good contrast, and low noise which helps in improving damage detection tasks. To date, the current work is the first of its kind, where an optical critical object is inserted in a cylindrical-shaped illumination source to capture high-quality images for damage detection. In the end, the proposed method is compared with the traditional approach to compare the damage detection capability. In this paper, Image-based damage detection on TiN-coated Milling tools by using a multi-light scattering illumination technique is proposed. This paper is experimentally oriented work and presents a practical solution to a given problem. With the proposed lightning system, the image processing algorithm can better localize the damage.

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