NDLI: Intelligent systems for statistical process control in steel (abstract only)

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Intelligent systems for statistical process control in steel (abstract only)

Content Provider	ACM Digital Library
Author	Kamal, Mustafa A.
Abstract	In today's mini steel mills quality steel produced is largely controlled by scrap selection, melting and refining. Casting of various shapes are based on scientific measurements of the prevailing operating conditions. Measurement of these operating conditions provide reliable heat data for correlation with resulting product quality. Many steel melt shops have developed empirical models to allow the heat approvers to predict the yield and ultimate tensile strengths, in standard statistical package to fit equations to data. Such systems have serious prediction difficulties. A large portion of prediction failure is due to two major defects in the system that builds the empirical model. First, programs developed for such use assume that the steel yield strength and ultimate tensile strength depends linearly on the amount of chemical elements present in the billet and second, many production engineers have little knowledge in the analysis of multivariate data, so that a meaningful inference from a fairly complicated analysis becomes quite difficult. A careful analysis by a professional statistician does not, however, resolve the problem either since the mechanical properties of steel depend on the heat chemistry chosen and hence building of such prediction model also requires “domain knowledge”. Such building process are not available in conventional statistical package.Research is being conducted for the development of an intelligent system that would perform multifactor data analysis, using conventional statistical package, and built empirical production model on the basis of the analysis performed and domain critical information about the chemical element provided by the production engineer (or chemist). The domain knowledge may include, but not limited to, the chemical and metallurgical properties of different elements and interaction among themselves as pertains to the production; the temperature, pressure and other environmental conditions to which the billets are exposed to; the rate of cooling and the shape of the finished bar; the engineers expectation of the presence or absence of certain elements in the model.Our system, when completed, would build one or more linear/nonlinear empirical models and predict yield, elongation and ultimate tensile strength of steel bars of different shapes in advance of rolling, so that if a particular heat chemistry is incapable of meeting the strength requirement in the finished bar, the heat approver can take appropriate action and reapply the billet in question to another product. We would be looking at the finished product of circular, angular and flat shapes. The predictability of such system would greatly reduce the uncertainties that the heat approver encounters in selecting the heat chemistry for a specified final product.
File Format	PDF
ISBN	0897912187
DOI	10.1145/322917.322975
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1987-02-01
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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