A pattern directed inference approach to hardwood log breakdown decision automation

Luis Gallardo Occena, Purdue University

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

Major Professor: Jose M. A. Tanchoco, Purdue University.

Subject Area

Engineering, Industrial|Artificial Intelligence

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