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Investigation into Adaptive Control of a Slip-Cast, Reaction-Bonded Silicon-Nitride Process via Adaptive Learning Network Modeling

机译：自适应学习网络建模研究滑移，反应键合硅氮化物过程的自适应控制

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A program was conducted to model the modulus of rupture (MOR) strength using Adaptive Learning Networks (ALN's) for aircraft engine components produced by a slip-cast, reaction-bonded, silicon-nitride production process. The primary objectives of the work were to identify key process variables and to predict optimum values for those variables as a guide for further experimentation. Nonlinear models have been synthesized that predict MOR with an average error of about 4 ksi over a range from 18.6 to 47.8. The manufacturing and analysis work done to date has demonstrated the feasibility of modeling the slip-cast RBSN process with the Adaptive Learning Network methodology and is viewed as the first iteration in the optimization procedure which is ultimately aimed at finding those manufacturing conditions which will produce the strongest, most consistent material strengths. (Author)

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Cleveland, D.; Garafola, P. M.; Decina, B. A.; Mucciardi, A. N.;
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年度 1979
页码 p.1-97
总页数 97
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Silicon nitrides; Processing; Models; Synthesis(Chemistry); Slip casting; Adaptive control systems; Optimization; Data bases; Particle size; Distribution; Engine components; Nonlinear systems; Manufacturing; Rupture; Strength(Mechanics); Variables; Aircraft engine;

机译：氮化硅;加工;模型;合成（化学）;滑动铸造;自适应控制系统;优化;数据库;粒度;分布;发动机部件;非线性系统;制造;破裂;强度（力学）;变量;飞机发动机;

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Investigation into Adaptive Control of a Slip-Cast, Reaction-Bonded Silicon-Nitride Process via Adaptive Learning Network Modeling

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