3D Pole Scanning for Automated ANSI Classing
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Solution Overview
Problem
Existing pole classification methods in the lumber industry are unsafe, time-consuming, and prone to errors due to manual handling, leading to misinterpretation of standards and potential rejection of poles.
Innovation Solution
An automated system utilizing 3D scanners, programmable logic controllers, and computing devices to accurately measure and classify poles based on ANSI standards, minimizing manual interaction and ensuring precise pole classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to classify poles, then operators can directly assess pole quality by touch, but the process becomes unsafe and time-consuming with high error rates
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical measurement system. 3D scanners and cameras capture pole geometry and surface features, while computer vision algorithms analyze the data to classify poles. This substitution eliminates direct human contact with poles, improving safety while maintaining or enhancing measurement precision through digital measurement capabilities.
Solution Approach 2:
The system creates digital copies of poles through 3D scanning and imaging. These digital models serve as virtual replicas that can be analyzed without physical handling. The copying process captures all necessary geometric and surface information, allowing classification to be performed on digital representations rather than physical objects.
2Productivity
If manual classification is performed, then operators can make real-time decisions, but the process is slow and subject to human error and misinterpretation of standards
Solution Approach 1:
The patent replaces human cognitive processing with automated computer vision and machine learning algorithms. These systems consistently apply classification standards without fatigue or misinterpretation, ensuring reliable and repeatable results. The automated decision-making process maintains real-time classification capability while eliminating human error sources.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results are continuously validated against established standards. The automated system can adjust its analysis based on feedback from previous classifications and standard updates, ensuring consistent application of criteria. This feedback loop maintains high reliability while operating at automated speeds.
3Reliability
If automated systems are implemented, then safety and accuracy improve, but system complexity and initial setup requirements increase
Solution Approach 1:
The patent employs multi-functional equipment that performs multiple operations within the classification system. For example, 3D scanners simultaneously capture geometric dimensions and surface features, while a single computing platform handles image processing, data analysis, and classification decision-making. This universality reduces the number of separate components needed, simplifying the overall system despite its advanced capabilities.
Solution Approach 2:
The automated classification system is designed to be self-sufficient in its operation. Once configured, it autonomously performs scanning, analysis, and classification without requiring constant human intervention or complex operational procedures. The system self-calibrates and maintains its classification standards, reducing the operational complexity burden despite the sophisticated technology employed.
Data Source
AI summary
A pole classing system may include an array of three-dimensional (3D) scanners, programmable logic controller equipment, and a computing device. The computing device may control the array of the 3D scanners to generate images of a pole from different directions and positions, generate a first pole dataset comprising dimensions and features of the pole, determine a class for the pole based on the dimensions of the pole and a first set of pole standard parameters, generate a second pole dataset by selecting a partial first pole dataset, and transmit the second pole dataset to the programmable logic controller equipment. The programmable logic controller equipment may process, based on a second set of pole standard parameters, the second pole dataset, the images and the features of the pole to thereby optimize the determined class of the pole and generate an updated class of the pole.