Log And Cant Re-Optimization With 3D Split Detection
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Solution Overview
Problem
Existing log cutting technologies face challenges in optimizing cuts due to log shifts or incorrect rotations, leading to improper cutting and reduced value from logs with splits, as existing methods struggle to accurately detect splits on debarked logs with rough surfaces and do not account for defects revealed during processing.
Innovation Solution
A method utilizing a scanner optimizer system with geometric and vision sensors to generate 3D models of logs and cants, re-optimize cut solutions based on real-time data, and detect splits, allowing for adjustments in cutting strategies to minimize waste and maximize wood recovery, involving multiple scan zones and computer systems to process data and adjust cutting parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the log is scanned and an optimized cut solution is calculated assuming a particular position, then cutting efficiency is improved, but if the log shifts or is not turned to the correct angle, manufacturing precision deteriorates
Solution Approach 1:
The system performs preliminary scanning and optimization calculations before cutting, establishing the optimal cut solution in advance. The scanner captures 3D geometric data of the log, and the optimization algorithm calculates the best cut pattern before the actual cutting operation begins, allowing the system to prepare cutting instructions while the log is still in position.
Solution Approach 2:
The system implements feedback by using geometric sensors to detect the actual position and orientation of the log, then comparing this with the assumed position used in optimization. The system adjusts the cut solution based on detected deviations, ensuring that even if the log shifts or is incorrectly positioned, the cutting accuracy is maintained through real-time corrections.
2Loss of substance
If the log is rotated to place the split at a predetermined angle to minimize impact, then value recovery is improved, but if the log is not rotated to the correct angle, productivity deteriorates
Solution Approach 1:
The system performs preliminary detection of splits and defects using geometric sensors and vision systems before the cutting operation. The optimization algorithm identifies the optimal rotation angle that minimizes the impact of splits on board quality and value, then pre-calculates the adjusted cut solution before the log is rotated and cut.
Solution Approach 2:
The system dynamically changes the rotation angle parameter of the log based on detected split characteristics. The optimization algorithm evaluates different rotation angles and selects the one that maximizes wood value recovery by positioning splits in less critical locations. This parameter adjustment is integrated into the overall cutting process without requiring separate manual intervention.
3Measurement precision
If multiple sensors and scan zones are used to detect splits and generate 3D models, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges multiple sensor types (geometric sensors, vision cameras) and multiple scan zones into a unified measurement system. The data from different sensors and scan zones are integrated to create a comprehensive 3D model of the log, including surface geometry and detected splits. This combined approach improves measurement precision while managing system complexity through integrated data processing.
Solution Approach 2:
The system transitions from 2D surface imaging to 3D geometric modeling by using geometric sensors that capture depth and spatial information. The multiple scan zones are combined to create a complete 3D representation of the log, allowing for more accurate split detection and volume calculations. This dimensional enhancement improves measurement precision without requiring proportionally more complex hardware.
4Manufacturing precision
If the cut solution is modified based on detected defects, then manufacturing precision is improved, but processing time increases
Solution Approach 1:
The system performs defect detection and cut solution modification in advance, before the actual cutting operation begins. The geometric sensors and vision systems scan the log, detect splits and defects, and the optimization algorithm recalculates the cut solution to account for these defects. This preliminary adjustment ensures that the cutting process itself proceeds efficiently without interruptions or rework.
Solution Approach 2:
The system maintains continuous operation by integrating defect detection and cut solution modification into the overall processing flow. The scanning, detection, and optimization occur while the log is being positioned or between processing steps, rather than requiring separate stopping and restarting. This continuous approach minimizes processing time while still achieving precise, defect-aware cut solutions.
Data Source
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AI summary
Embodiments provide methods, apparatuses, and systems for cutting wood workpieces, such as logs and cants, into desired products. In various embodiments, after a log is chipped into a cant, the cant may be scanned and re-optimized based on the new scan data and information about the source log, such as simulated orientation parameters, a 3D model, and/or potential cut solutions. In other embodiments, data from multiple sensor types may be used in combination to detect splits in logs, cants, or both. Optionally, re-optimization and split detection techniques may be used in combination to improve wood volume recovery, value, and/or throughput speed.