Profile Charts for Monitoring Lumber Manufacturing Using Laser Range Sensor Data

Abstract

Real-time technologies using noncontact laser range sensors (LRS) have recently been introduced to improve statistical process control (SPC) programs in automated lumber mills by greatly increasing the volume of data available for SPC. However, present SPC procedures based on sampling theory developed for manual data collection do not fully utilize data from these systems. A new system of control charts is introduced here that simultaneously monitors multiple lumber surfaces and specifically targets three common sawing defects (taper, snipe/flare, and snake). Nontraditional control charts are suggested based on the decomposition of LRS measurements into trend, waviness, and roughness. The proposed charts can be used to monitor the slope parameter of a multiple linear regression model and the peak-to-peak waviness of observations from each board. Applying these methods should lead to process improvements in sawmills by better detecting common sawing problems and identifying the causes.

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