Abstract
As artificial intelligence and especially machine learning gained a lot of attention during the last few years, methods and models have been improving and are becoming easily applicable. This possibility was used to develop a quality prediction system using supervised machine learning methods in form of time series classification models to predict ovality in radial-axial ring rolling. Different preprocessing steps and model implementations have been used to improve quality prediction. A semi-supervised approach is used to improve the prediction and analyze, to what extend it can improve current research in machine learning for quality prediciton. Moreover, first research steps are taken towards a synthetic data generation within the radial-axial ring rolling domain using generative adversarial networks.
Highlights
The presented research at hand is building on earlier studies of the authors
The approach of synthetic data generation using a Conditional GAN (CGAN) architecture already led to partially useful results, which have been evaluated by both human process experts as well as by TSTR and TRST metrics
Within the present use case of Radial-Axial Ring Rolling (RARR), it can be stated that for a generation of synthethic data, the CGAN architecture performed significantly better than the Auxiliary Classifier Gan (ACGAN) architecture.The hoped-for stabilization of the training process, which was expected from the ACGAN architecture in contrast to the CGAN architecture, did not occur in the case of the RARR
Summary
The presented research at hand is building on earlier studies of the authors These studies introduced a quality prediction approach in the domain of Radial-Axial Ring Rolling (RARR) with regard to form errors and especially ovality [1]. This approach was enhanced by a domain specific preprocessing approach [2] and an evaluation on the best performing model for a Time Series Classification (TSC) task was performed [3]. The laser unit is costly and requires constant maintenance it is not running all the time, but unlabeled data is produced automatically and is acquired by the authors as well This unlabeled data will be used within the semi-supervised approach to improve classification accuracies on the baseline TSC approach.
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