SPC METHODS FOR DETECTING SIMPLE SAWING DEFECTS USING REAL-TIME LASER RANGE SENSOR DATA
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it