Wood Fastener Removal Using X-Ray Mapping and Robotic Extraction
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Solution Overview
Problem
Current material recycling technologies face challenges in autonomously and efficiently removing embedded fasteners from wood products, particularly in distinguishing and extracting threaded and non-threaded fasteners, which hampers the effective recycling of wood materials.
Innovation Solution
A system comprising an X-ray scan module, optical sensors, and a fastener extractor module that uses computer vision and robotic mechanisms to detect and remove fasteners by creating a virtual model of the wood workpiece, identifying fastener types, and executing precise extraction schedules for both threaded and non-threaded fasteners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional recycling methods are used, then processing speed is maintained, but fastener removal efficiency deteriorates due to inability to autonomously distinguish and extract different fastener types
Solution Approach 1:
The patent replaces manual inspection and mechanical fastener removal with an automated system using X-ray sensors, optical sensors, and computer vision algorithms to detect fasteners, followed by robotic extraction mechanisms. This substitution enables autonomous operation, improving both productivity and automation extent simultaneously.
Solution Approach 2:
The system creates a virtual model of the wood workpiece based on sensor data, which serves as a digital copy representing the physical object's fastener locations and types. This virtual model allows the control system to plan and execute extraction operations without direct physical interaction during the detection phase, enhancing automation capability.
2Loss of time
If manual fastener removal is used, then equipment complexity is low, but processing time increases and recycling quality deteriorates
Solution Approach 1:
The system segments the fastener removal process into distinct phases: detection (X-ray and optical scanning), identification (computer vision classification of fastener types), virtual model creation, and extraction (robotic execution). This segmentation allows each subsystem to be optimized independently, reducing overall processing time while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary detection and classification of fasteners using non-contact sensors before the actual extraction operation. By creating a virtual model and identifying all fasteners in advance, the system prepares extraction paths and parameters beforehand, significantly reducing actual processing time during the removal phase.
3Measurement precision
If automated detection systems are implemented, then fastener identification accuracy improves, but system complexity and cost increase
Solution Approach 1:
The patent merges multiple detection technologies (X-ray scanning and optical sensing) into a unified detection system. The X-ray sensors penetrate the wood to detect embedded fasteners, while optical sensors capture surface features. By combining these methods, the system achieves high detection accuracy for both threaded and non-threaded fasteners while sharing common processing infrastructure.
Solution Approach 2:
The virtual model serves as an intermediary between the sensor data and the extraction execution. It consolidates information from multiple sensor sources, processes fastener identification results, and provides a unified representation for the control system, simplifying the overall system architecture while maintaining high detection precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables efficient and autonomous removal of fasteners, improving the recyclability of wood products by accurately detecting and extracting various types of fasteners, thereby enhancing the quality and quantity of recycled materials.
Implementation Method 1
accessing a first set of X-ray scans captured by an X-ray sensor facing the scan volume occupied by the recycled wood workpiece
Data Source
AI summary
A method includes: receiving a recycled wood workpiece populated with a set of metal fasteners; accessing an internal imaging scan; detecting the set of metal fasteners embedded in the recycled wood workpiece based on internal features detected in the internal imaging scan; for each metal fastener in the set of metal fasteners, extracting an initial position and an initial orientation of the metal fastener from the internal imaging scan; generating a virtual model of the recycled wood workpiece based on the internal imaging scan; accessing an image captured by an optical sensor; detecting a first metal fastener in the recycled wood workpiece; deriving a first position and a first orientation of the first metal fastener; and, in response to identifying the first metal fastener analogous to an initial metal fastener in the virtual model, isolating the first metal fastener in the virtual model and generating a fastener removal schedule.