Industrial Application of Surface Crack Detection in Sheet Metal Stamping Using Shift-and-Add Speckle Imaging
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Institute of Manufacturing Technologies, Warsaw University of Technology, ul. Narbutta 85, 02-524 Warsaw, Poland
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Robert Cacko   

Institute of Manufacturing Technologies, Warsaw University of Technology, ul. Narbutta 85, 02-524 Warsaw, Poland
Adv. Sci. Technol. Res. J. 2023; 17(5):220-235
The sheet metal surface crack detection during manufacturing is an essential issue because of both the product quality and process productivity. Development of solutions to eliminate defective products during the metal forming process is crucial for the smooth production and for developing an appropriate tool geometry in the initial phase of the process. Currently, the methods of surface crack detection used in the industry are mostly related to visual inspection. These are methods that require operators of industrial facilities considerable attention and effort to capture emerging discontinuities on the sheet metal surface. Also, this situation results increase in the duration of the specific operations of stamping and significantly reduces productivity. Therefore, an industrial application of a non-contact laser technique that simultaneously provides the results of the speckle imaging is presented. The authors demonstrate a specially designed machine vision system along with experimental tools for the stamping operation. Proposed solution uses the phenomenon of speckle pattern that appears in the image of the investigated sheet surface produced by the laser beam emission. In this method, coherent laser light is emitted to the surface, where a speckle pattern is generated due to scatter reflection from the sheet metal surface and then, shift-and-add technique and image processing is applied. The proposed measurement technique consists, initially, of making a sequence of images of the tested object for the moving surface of the sheet. Secondly, the object's displacement quantity in each image is determined, and the position is corrected. The test object in each image is moved to the starting position, and all images are superimposed. It allows to obtain a high-quality image with visible surface defects. Finally, the dynamically changing speckle pattern intensity is evaluated using Gaussian-of-Laplacian edge detection to investigate a surface crack location due to the surface discontinues and light scattering. This process is recommended for machine vision imaging of distant objects, which works well in industrial conditions as well as online analysis. Also, from the speckle size measurement, an experimental procedure is employed to verify the best condition for vision system resolution.
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