Application of Deep Neural Network in Estimation of the Weld Bead Parameters

Autor:	Keshmiri, Soheil, Zheng, Xin, Chew, Chee Meng, Pang, Chee Khiang
Rok vydání:	2015
Předmět:	Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	We present a deep learning approach to estimation of the bead parameters in welding tasks. Our model is based on a four-hidden-layer neural network architecture. More specifically, the first three hidden layers of this architecture utilize Sigmoid function to produce their respective intermediate outputs. On the other hand, the last hidden layer uses a linear transformation to generate the final output of this architecture. This transforms our deep network architecture from a classifier to a non-linear regression model. We compare the performance of our deep network with a selected number of results in the literature to show a considerable improvement in reducing the errors in estimation of these values. Furthermore, we show its scalability on estimating the weld bead parameters with same level of accuracy on combination of datasets that pertain to different welding techniques. This is a nontrivial result that is counter-intuitive to the general belief in this field of research. Comment: Disapproval of funding organization
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1502.04187 Zobrazit plný text záznamu View this record from Arxiv