SPC Methods for Detecting Simple Sawing Defects Using Real-Time Laser Range Sensor Data

Effective statistical process control (SPC) procedures can greatly enhance product value and yield in the lumber industry, ensuring accuracy and minimum waste. To this end, many mills are implementing automated real-time SPC with non-contact laser range sensors (LRS). These systems have, thus far, had only limited success because of frequent false alarms and have led to tolerances being set excessively wide and real problems being missed. Current SPC algorithms are based on manual sampling methods and, consequently, are not appropriate for the volume of data generated by real-time systems. The objective of this research was to establish a system for real-time LRS size control data for automated lumber manufacturing. An SPC system was developed that incorporated multi-sensor data, and new SPC charts were developed that went beyond traditional size control methods, simultaneously monitoring multiple surfaces and specifically targeting common sawing defects. In this paper, eleven candidate control charts were evaluated. Traditional X-bar and range charts are suggested, which were explicitly developed to take into account the components of variance in the model. Applying these methods will lead to process improvements for sawmills using automated quality control systems, so that machines producing defective material can be identified and prompt repairs made