Precision machining plays a vital role in modern, efficient, and sustainable manufacturing. Monitoring and controlling the clamping forces can influence positioning accuracy, workpiece deformation, thus improving the production outcome. The clamping force must be adapted to the cutting forces, workpiece geometry, and material properties to improve accuracy and control workpiece deformation during machining. The best devices available have a repeatability of ±1 µm, however, with limited precision and repeatability when re-clamping the workpiece. This paper presents the newly developed high-precision adaptable clamping system for controlled high-precision positioning and repositioning of a workpiece in the x-y plane with visual pattern recognition, adjustment, and controlled clamping forces. The clamping system is based on a pneumatic clamping chuck with controlled air pressure on a very accurate CNC machine. FEA calculations of thin-walled workpieces are used for designing associated jaws to ensure workpiece holding, limited forces and limited deformations. Once the workpiece has been removed and re-clamped with the defined forces, the vision device identifies the new workpiece position. Force and position data are collected and analyzed for calculating the repositioning movement in the x, y, and theta axes. The difference between the measured position after re-clamping and the reference position is calculated using a specially developed algorithm, yielding the motion commands to the x, y and theta axis. Using the vision system made it possible to identify an accuracy of ±1 µm and a repeatability of ±0.5 µm.
This article describes, how color textures can be reliably detected and classified in the production process independent of external parameters such as brightness, object positions (translation), angulars (rotation), object distances (scaling) or curved surfaces (rotation + scaling). The methods described here are also suitable for reliably classifying at least 18 color textures even if they differ only slightly from each other optically. The online classification of color textures is a classic task in the wood, furniture and textile industry. For example, un- wanted defects or partial soiling on moving webs can be reliably detected regard- less of fluctuations in brightness and/or shadows during process operation. Algo- rithms has been developed for teach-in with RGB-HSI-transform, set fewer seg- ments on the color textures of each class with e.g. 24x24 Pixel, use suitable transformations {HSI}, e.g. 2D-FFT for formation characteristic 2D spectral mountains in these segments, extraction of statistical features and setting up the individual classifiers. Algorithms has been developed for identification & classification in process op- eration with extraction of statistical characteristics and methods of robust classi- fication. The implementation of the methods, the triggering of the color cameras, the processing of the color information including the output of the results to the process control is done with the data analysis program Xeidana®.
Perfectly coated surfaces are an essential quality feature in the automotive and consumer goods industries. They are the result of an optimized, controlled coating process. Because entire assemblies could be rejected if Out-of-Specification (OOS) parts are installed, this has a severe economic impact. This paper presents a novel, line-integrated multi-camera system with intelligent algorithms for anomaly detection on small KTL-coated aluminum parts. The system also aims to automatize the previously used human inspection to a sophisticated and automated vision system that efficiently detects defects and anomalies on coated parts.
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