TECH FOCUS: LINE SCAN IMAGING Tech Focus sponsored by Vieworks Featured product


an optical measuring system for SGL Carbon to monitor micro-defects and early detection of potential, negative process influences, thereby avoiding disruptions such as the formation of wrappers or misaligned filaments. Te system also provides data and knowledge that can be applied to improve stability in carbon fibre production. ‘Te automatic detection


of very small fibre defects in carbon fibre production is still not completely resolved,’ said Kristina Klatt, SGL’s head of carbon fibre development. ‘Te uniform colouring of fibres and fibre defects make it


‘Te automatic detection of very small fibre defects in carbon fibre production is still not completely resolved’


particularly difficult for optics and software to achieve good results.’ Previous testing solutions


have been limited, for example, to inspecting fabrics that have a contrast in the material thanks to the knitting threads, which makes deviations more easily recognisable. Fraunhofer IGCV’s approach


– using Chromasens’ Allpixa Wave line scan camera’s true-colour RGB sensor and a Chromasens Corona II LED


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line scan light – was designed to achieve continuous surface monitoring, all done online during operation. Te Allpixa Wave camera was combined with an adapted neural network, resulting in higher flexibility in image processing. Both upstream and downstream processes, such as spinning of polyacrylonitrile (PAN) fibres or fibre spreading, can now be monitored with minimal transfer or configuration. Te pre-trained network


also adapts to new conditions, and learns to find the errors in different structures. Te neural network recognises whether a component is a good or bad part, with hit rates of more than 90 per cent. However, this success is


only realistic for patterns that appear particularly heterogeneous, noted Andreas Margraf, project manager of sub-project Opima 2.0, which considers online process monitoring at Fraunhofer IGCV. ‘In the case of fibres, there are very different errors that are also subject to varying environmental conditions. Te researchers and SGL Carbon were interested in how often, and where, the defects occur in the individual sections.’ Researchers discovered


this can be achieved with an expansion of the previous neural networks, the pixel- based segmentation or semantic segmentation. Each individual pixel of an image is classified and given a corresponding label as either good (not defective) or bad


VL Series - high-performance and cost-effective line scan cameras


Vieworks’ VL Series, first launched in 2014, has been reintroduced in 2020 to meet customers’ needs for more powerful features, but still at an affordable price. The two models, VL-


8K and VL-16K, each presents high resolution of 8k and 16k; fast speed up to 80kHz and 40kHz respectively. The series offers various features,


(defective). Anomalies on the fibre carpet can be identified by classifying larger groups of pixels or image areas as bad. With the help of this technology, defective fibres on the surface can be reliably identified in the images recorded by the Chromasens camera. Its 15k pixel resolution captures micro-defects on the fibre carpet via a quadlinear CMOS line sensor with line frequency up to 69kHz. ‘Te detail the system offers online could only be exceeded in the laboratory under a microscope,’ said Margraf.


such as compact size, exposure control, pre- emphasis function, and diverse image modes to upgrade your machine vision inspection systems. The cameras are


applicable for flat panel display inspection, printed circuit board inspection and high-performance document scanning. See the new VL Series at vision.vieworks.com.


Carbon fibres are around


5 to 10µm in diameter. Te material gets its strength by being twisted tightly together like yarn. It is five times stronger than steel and twice as stiff, yet much lighter in weight, making it ideal for aviation parts. Outside of aviation, carbon fibre is making inroads into the automotive industry. It is believed that carbon fibre composites could reduce passenger car weight by 50 per cent, which would improve fuel efficiency by nearly 35 per cent without compromising


g OCTOBER/NOVEMBER 2020 IMAGING AND MACHINE VISION EUROPE 29


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