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.
| Original language | English |
|---|---|
| Title of host publication | Fourteenth International Conference on Machine Vision, ICMV 2021 |
| Editors | Wolfgang Osten, Dmitry Nikolaev, Jianhong Zhou |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510650442 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 14th International Conference on Machine Vision, ICMV 2021 - Rome, Italy Duration: 8 Nov 2021 → 12 Nov 2021 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 12084 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Conference
| Conference | 14th International Conference on Machine Vision, ICMV 2021 |
|---|---|
| Country/Territory | Italy |
| City | Rome |
| Period | 8/11/21 → 12/11/21 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Damage detection
- Illumination source
- Image processing
- Milling tools
- Predictive maintenance
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
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