𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Application of neural networks to meltblown process control

✍ Scribed by Qin Sun; Dong Zhang; Bingzhen Chen; Larry C. Wadsworth

Publisher
John Wiley and Sons
Year
1996
Tongue
English
Weight
480 KB
Volume
62
Category
Article
ISSN
0021-8995

No coin nor oath required. For personal study only.

✦ Synopsis

Process modeling is essential for the control of optimization and an on-line prediction is very useful for process monitoring and quality control. Up to now, no satisfactory methods have been found to model an industrial meltblown process since it is of highly dimensional and nonlinear complexity. In this article, back-propagation neural networks (BPNNs) were investigated for modeling the meltblown process and on-line predicting the product specifications such as fiber diameter and web thickness. The feasibility of this application was successfully demonstrated by agreement of the prediction results from the BPNN to the actual measurements of a practical case. The network inputs included extruder temperature, die temperature, melt flow rate, air temperature at die, air pressure at die, and die-tocollector distance (DCD). The output of the fiber diameter was obtained by neural computing. The network training was based on 160 sets of the training samples and the trained network was tested with 70 sets of test samples which were different from the training data. This research is preliminary and of industrial significance and especially valuable for the optimal control of advanced meltblown processes.

πŸ“œ SIMILAR VOLUMES

Run-to-run process control of a plasma e
Run-to-run process control of a plasma etch process with neural network modelling
✍ Jill P. Card; Mark Naimo; William Ziminsky πŸ“‚ Article πŸ“… 1998 πŸ› John Wiley and Sons 🌐 English βš– 250 KB πŸ‘ 1 views

Run-to-run control of a plasma etch process for 8 inch diameter silicon wafers at Digital Semiconductor is determined by maintenance of targeted values of post-etch metrology variables. The post-etch quality variables are extremely sensitive to variation in the etch chamber conditions due to fluctua

Application of neural networks to struct
Application of neural networks to structure–sandalwood odour relationships
✍ Driss Zakarya; Driss Cherqaoui; M'Hamed Esseffar; Didier Villemin; Jean-Michel C πŸ“‚ Article πŸ“… 1997 πŸ› John Wiley and Sons 🌐 English βš– 209 KB πŸ‘ 1 views

Neural networks have proved to be particularly successful in their ability to identify non-linear relationships. This paper shows that a three-layer back-propagation neural network is able to learn the relationship between the sandalwood odour and molecular structures of 85 organic compounds belongi

Application of statistical process contr
Application of statistical process control to competitor benchmarks
✍ Ted M. van Ryn πŸ“‚ Article πŸ“… 2000 πŸ› John Wiley and Sons βš– 132 KB πŸ‘ 2 views

This article outlines the application of statistical process control in the competitive analysis context of corporate Total Quality Management/Statistical Process Control (TQM/SPC). Using attribute measurements of competitor product samples and the descriptive statistics of SPC process analysis, the

On-line adaptive control of non-linear p
On-line adaptive control of non-linear plants using neural networks with application to liquid level control
✍ G. W. Ng; P. A. Cook πŸ“‚ Article πŸ“… 1998 πŸ› John Wiley and Sons 🌐 English βš– 218 KB πŸ‘ 1 views

In this paper, we present two on-line adaptive control algorithms for non-linear plants using neural networks. The architecture used is based on the concept of specialized learning, which was first proposed by Psaltis et al. and suffers from two main problems, namely lack of knowledge of the plant J