Dissertation Title: Statistical Procedures for the Development of Real-Time Statistical Process Control (SPC) Systems in Lumber Manufacturing
Abstract: High raw material costs and reduced allowable forest harvest levels have created challenges for the Canadian lumber industry. Sawlogs typically comprise 75% of all the costs in a sawmill and insufficient log availability is a widespread problem. Thus, maximum product value and yield from every log processed is an urgent priority.
Effective statistical process control (SPC) procedures can greatly enhance product value and yield, ensuring accuracy and minimum waste. However, present procedures are manual in nature. The time and effort required means that only small data samples are collected at infrequent intervals, seriously limiting quality control effectiveness. Attempts to implement automated SPC with non-contact laser range sensors (LRS) have thus far had only limited success. Such systems have given frequent false alarms, prompting tolerances to be set excessively wide. Thus, real problems are often missed for extended periods.
The objective of this research was to establish a system for collecting and processing real-time LRS size control data for automated lumber manufacturing. An SPC system was developed that incorporated multi-sensor data filtering procedures, a model with complex structure, and new control charting procedures. The LRS data were first filtered for measurement errors using techniques from image processing. Non-sawing defects were then removed from the data using a sheet-of-light profiling system and defect recognition algorithm. Defect-free filtered data were modeled in a multi-stage process, which explicitly considered multiple sources of variation and a complex correlative structure. New SPC charts were developed that went beyond traditional size control methods, simultaneously monitoring multiple surfaces and specifically targeting common sawing defects.
Nineteen candidate control charts were evaluated. For some sawing defects (e.g., machine positioning errors and wedge), traditional X-bar and range charts are suggested. These charts were explicitly developed to take into account the components of variance in the model. For other sawing defects (e.g., taper, snipe, flare, and snake), control charts are suggested that are non-traditional. The charts that target these defects were based on the decomposition of LRS measurements into trend, waviness, and roughness.
Applying these methods will lead to process improvements in sawmills, so that machines producing defective material can be identified, allowing prompt repairs to be made.
Co-Supervisor: Dr. Thomas Maness
Current Position: Assistant Professor, School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, USA