Experiments and shape prediction of plasma deposit layer using artificial neural network  

作　　者：徐继彭 林柳兰 胡庆夕 方明伦 

机构地区：[1]Rapid Manufacturing Engineering Center, Shanghai University, Shanghai 200444, P.R. China

出　　处：《Journal of Shanghai University(English Edition)》2006年第5期443-448,共6页上海大学学报（英文版）

基　　金：Project supported by National Natural Science Foundation of China ( Grant No .50075032) , and National High-Technology Research and Development Program of China ( Grant No .2001AA421150)

摘　　要：Plasma surfacing is an important enabling technology in high-performance coating applications. Recently, it is applied to rapid prototyping/tooling to reduce development time and manufacturing cost for the development of new products. However, this technology is in its infancy, it is essential to understand clearly how process variables relate to deposit microstructure and properties for plasma deposition manufacturing process control. In this paper, layer appearance of single surfacing under different parametem such as plasma current, voltage, powder feedrate and travel speed is studied. Back-propagation neural networks are used to associate the depositing process variables with the features of the deposit layer shape. These networks can be effectively implemented to estimate the layer shape. The results Indicate that neural networks can yield fairly accurate results and can be used as a practical tool in plasma deposition manufacturing process.Plasma surfacing is an important enabling technology in high-performance coating applications. Recently, it is applied to rapid prototyping/tooling to reduce development time and manufacturing cost for the development of new products. However, this technology is in its infancy, it is essential to understand clearly how process variables relate to deposit microstructure and properties for plasma deposition manufacturing process control. In this paper, layer appearance of single surfacing under different parametem such as plasma current, voltage, powder feedrate and travel speed is studied. Back-propagation neural networks are used to associate the depositing process variables with the features of the deposit layer shape. These networks can be effectively implemented to estimate the layer shape. The results Indicate that neural networks can yield fairly accurate results and can be used as a practical tool in plasma deposition manufacturing process.

关 键 词：plasma deposition manufacturing （PDM） artificial neural network （ANN） deposit layer back-propagation. 

分 类 号：O53[理学—等离子体物理]