Multiple‐Output Artificial Neural Network to Estimate Solid Cycle Times in Conical Spouted Beds

2021 ◽  
Vol 44 (3) ◽  
pp. 542-550
Author(s):  
Idoia Estiati ◽  
Aitor Atxutegi ◽  
Haritz Altzibar ◽  
Fabio B. Freire ◽  
Roberto Aguado ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cycle Times ◽  
Spouted Beds ◽  
Artificial Neural

scholarly journals DEVELOPMENT OF AN AUTOMATED ASSEMBLY PROCESS SUPPORTED WITH AN ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 18 (3) ◽  
pp. 28-41 ◽  
Author(s):  
Paul BOBKA ◽  
Jakob HEYN ◽  
Jann-Ole HENNINGSON ◽  
Martin RÖMER ◽  
Thomas ENGBERS ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Financial Risk ◽  
Industrial Robot ◽  
Machine Learning Techniques ◽  
Assembly Process ◽  
Automated Assembly ◽  
Cycle Times ◽  
Advantages And Disadvantages ◽  
Artificial Neural

A central problem in automated assembly is the ramp-up phase. In order to achieve the required tolerances and cycle times, assembly parameters must be determined by extensive manual parameter variations. Therefore, the duration of the ramp-up phase represents a planning uncertainty and a financial risk, especially when high demands are placed on dynamics and precision. To complete this phase as efficiently as possible, comprehensive planning and experienced personnel are necessary. In this paper, we examine the use of machine learning techniques for the ramp-up of an automated assembly process. Specifically we use a deep artificial neural network to learn process parameters for pick-and-place operations of planar objects. We describe how the handling parameters of an industrial robot can be adjusted and optimized automatically by artificial neural networks and examine this approach in laboratory experiments. Furthermore, we test whether an artificial neural network can be used to optimize assembly parameters in process as an adaptive process controller. Finally, we discuss the advantages and disadvantages of the described approach for the determination of optimal assembly parameters in the ramp-up phase and during the utilization phase.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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