Mr. Luis Garcia-Barth Profile

Luis Garcia-Barth

at Hochschule Aalen - Technik und Wirtschaft

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Proceedings Article | 8 March 2023 Presentation + Paper

Accuracy evaluation of a new 3D photogrammetric position measurement system for 6D printing

Luis Garcia, Uwe Bielke, Rainer Boerret

Proceedings Volume 12428, 124280D (2023) https://doi.org/10.1117/12.2649172

KEYWORDS: Head, Tunable filters, Light sources, Optical filters

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Additive manufacturing has a great potential for modifying optics in a way that new functions, for example individual light distributions, could be realized by modifying the optical surface. The functionalization of the curved surface by multijet modeling is the goal of the 6D printing project (6D AF) at the Center of Optical Technology, Aalen University. To combine the coordinate systems of the printing unit, the printing table and the metrology unit a new photogrammetric position measurement system is developed and presented. First results of the accuracy in three axes are show and the improvement in motion is demonstrated.

Proceedings Article | 8 July 2020 Paper

Machine learning model for robot polishing cell

M. Schneckenburger, L. Garcia-Barth, R. Börret

Proceedings Volume 11478, 114780J (2020) https://doi.org/10.1117/12.2564633

KEYWORDS: Polishing, Sensors, Machine learning, Surface finishing, Glasses, Neurons, Neural networks, Data modeling

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The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. With increasing optic sizes, the stability of the polishing process becomes more and more important. If not empirically known, the optical surface must be measured after each polishing step. One approach is to mount sensors on the polishing head in order to measure process relevant quantities. On the basis of these data, Machine Learning algorithms can be applied for surface value prediction. The aim of this work is the stepwise development of an artificial neural network (ANN) in order to improve the accuracy of the models' prediction. The ANN is developed in the Python programming language using the Keras deep learning library. Beginning with simple network architecture and common training parameters. The model will then be optimized step-by-step through the implementation of different methods and Hyperparameter optimization (HPO). Data, which is generated by the sensor-integrated glass polishing head, is used to train the ANN-model. A representative part of these data is held back before, in order to validate the models' prediction. The so-called dataset contains measured values from multiple polishing runs, preceded by a design of experiment. After the model is trained on the dataset, it is able to predict the result of not yet performed polishing runs, with given polishing parameters. Concrete, the ANN is used to predict the resulting glass-surface quality, which includes the surface roughness and the shape accuracy, calculated by the material removal over time. The prediction by artificial neural networks reduces the polishing iterations and thus the production time.

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