A pattern directed inference approach to hardwood log breakdown decision automation
Abstract
The hardwood log breakdown decision process in the presence of internal defect detection is modelled in a knowledge-based environment geared towards eventual sawmill automation. Information on log and internal defect profiles, now accessible through non-invasive detection methods like CAT or NMR scanning, were collected from the breakdown of yellow poplar logs. A graphics sawing simulator for hardwood logs, based on solid modelling concepts, was developed and used to analyze the log-to-lumber production process in the presence of internal defect information. Knowledge gained, from the analysis and from current sawmill practice, was formalized in a logic-based, pattern-directed inference model to enable automatic specification of log breakdown instructions from the extracted defect configuration pattern of a given log.
Degree
Ph.D.
Advisors
Tanchoco, Purdue University.
Subject Area
Industrial engineering|Artificial intelligence
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