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Medium Density Fiberboard control parameter optimization system research

Author: TianYanQing
Tutor: XuKaiHong
School: Northeast Forestry University
Course: Detection Technology and Automation
Keywords: Hot pressing process Neural Network Parameter Management Multi-objective optimization
CLC: TS653.6
Type: Master's thesis
Year: 2011
Downloads: 59
Quote: 0
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Abstract


Medium density fiberboard (MDF) production process an important step of the process is the process of hot pressing, hot pressing process parameter selection directly affects the performance of the finished medium density fiberboard and processing costs (energy consumption). MDF current development of new products are based on laboratory tests during the development process parameters, there is the problem of long product development cycle, the process is not reasonable because energy distribution, there is a waste of energy to increase the issue of costs, the production of the finished product performance parameters unsatisfactory product quality problems of instability exists. How to optimize process control of the energy consumption of hot allocation, improve product quality and relevance of the process performance, shorten the development cycle of new products, research and development is an important issue. Thesis by analyzing the characteristics of BP neural network, the algorithm is applied to medium-density Fiberboard selection parameter optimization and prediction, the establishment of a neural network-based simulation model of hot pressing process to achieve the medium-density fiberboard pressing parameters for data mining the trained neural network model integrated into the multi-objective optimization problem, Medium Density Fiberboard making process parameter optimization of multi-objective optimization can be applied directly to hot selected parameters. To achieve the purpose of reducing energy consumption, and the establishment of a hot parameter management system used to manage hot-pressing process parameters and product performance parameters of the matching databases and user management database, to achieve effective control and parameter selection. Medium Density Fiberboard process for the establishment of BP neural network simulation model, from the existing experimental data obtained in BP neural network model of learning samples to analyze the characteristics of the training function, set the hot model training function, and through training to achieve forecast The standard, you can not predict the actual production process parameters used for MDF molding board performance; BP neural network model as a multi-objective optimization algorithm fitness function, the use of multi-objective optimization algorithm can quickly and relatively reasonable to formulate the pressing process, temperature, pressure, time and other parameters, and accompanied by hot pressing information feedback system is able to self-learning, and constantly improve the database, improve their decision-making ability, thereby making the results more reasonable; management system operating parameters the Windows platform, using SQL Server as the database server, development environment using Microsoft Visual C 6.0 (hereinafter referred to as VC), using the MFC framework, and use MATLAB as a simulation tool. Through the establishment of Medium Density Fiberboard parameter management system can quickly find this material in a particular hot parameters (specific pressing temperature, pressing time, pressing pressure) produced under medium density fiberboard finished product properties, such as elastic modulus amount of water swelling, internal bond strength, etc., can also query the corresponding power consumption, to provide the necessary staff consultation, the staff based on this information you can choose the appropriate processing parameters to optimize hot production process. Help to improve productivity, reduce production costs, but also allows the database information management more scientific.

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CLC: > Industrial Technology > Light industry,handicrafts > Wood processing industry,furniture manufacturing industry > Processing > Wood-based panel production > Fiberboard
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