Log and Cant Re-Optimization Using 3D Split Detection
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Solution Overview
Problem
Existing log cutting methods fail to account for shifts or misalignments of logs during processing, leading to improper cuts and reduced value due to undetected splits, especially in debarked logs with rough surfaces.
Innovation Solution
Implement a scanner optimizer system with geometric and vision sensors to create 3D models of logs and cants, allowing for real-time re-optimization of cutting solutions and split detection, adjusting for positional errors and defects.
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 the solution becomes invalid if the log shifts or is not turned to the correct angle
Solution Approach 1:
The system performs preliminary scanning and optimization calculations before cutting, establishing the optimal cut solution in advance. This allows the cutting process to proceed efficiently while having a pre-determined plan that accounts for the log's initial position, thereby maintaining both productivity and reliability.
Solution Approach 2:
The system uses feedback from the scanning process to verify log position and adjust or re-calculate the cut solution if the log has shifted. This closed-loop approach ensures that the cut solution remains accurate and reliable even when positional changes occur during handling.
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 the process becomes more complex and time-consuming
Solution Approach 1:
The system detects splits and determines optimal rotation angles in advance through scanning and analysis, before the log reaches the cutting position. This preliminary planning allows the log to be rotated to the predetermined angle that minimizes split impact, maximizing wood value recovery without adding complex real-time adjustments during cutting.
Solution Approach 2:
The system creates a digital model or representation of the log including split locations and characteristics, allowing virtual simulation of different rotation angles and cut solutions. This digital copy enables optimization of log positioning to minimize split impact without requiring physical trial-and-error, reducing complexity while improving value recovery.
3Device complexity
If images of debarked logs are used to detect splits, then the process is simple, but split detection is difficult due to rough outer surfaces and lack of depth information
Solution Approach 1:
The system transitions from 2D surface imaging to 3D scanning, capturing depth information and the full geometry of the log including split characteristics. This dimensional enhancement allows accurate detection of splits on rough debarked surfaces by analyzing three-dimensional surface variations rather than relying solely on two-dimensional image patterns.
Solution Approach 2:
The system replaces simple optical imaging with advanced scanning technology that captures three-dimensional geometric data. This substitution enables precise measurement of split depth and orientation by analyzing surface topology in three dimensions, overcoming the limitations of rough surfaces that confuse 2D imaging systems.
Data Source
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.