Journal
APPLIED SOFT COMPUTING
Volume 85, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2019.105683
Keywords
Autoencoder; Generative models; Quality prediction; Time-delayed neural networks; Powder metallurgy
Categories
Funding
- Doctoral college - Cyber-Physical Production Systems project - Technische Universitat Wien, Vienna, Austria
Ask authors/readers for more resources
One of the key challenges in manufacturing processes is improving the accuracy of quality monitoring and prediction. This paper proposes a generative neural network model for automatically predicting work-in-progress product quality. Our approach combines an unsupervised feature-extraction step with a supervised learning method. An autoencoding neural network is trained using raw manufacturing process data to extract rich information from production line recordings. Then, the extracted features are reformed as time-series and are fed into a multi-layer perceptron for predicting product quality. Finally, the outputs are decoded into a forecast quality measure. We evaluate the performance of the generative model on a case study from a powder metallurgy process. Our experimental results suggest that our method can precisely capture the defective products. (C) 2019 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available