Object Structural Status Detection via Stimulus Response
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Solution Overview
Problem
Existing methods are inefficient and labor-intensive for determining the structural status of objects, particularly when internal issues like mold, fungus, or pests are not visible from the exterior, making it difficult to identify healthy or unhealthy produce or structural components without manual inspection.
Innovation Solution
An object scanning system that uses a stimulus agent, such as a puff of air, and sensors to capture reactions, combined with a machine learning model to predict the structural status based on how the object responds to the stimulus, allowing for non-invasive assessment of internal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to determine structural status, then inspection accuracy can be maintained, but inspection time and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated system that uses stimulus agents (acoustic, optical, or mechanical) and sensors to detect structural status. The stimulus source applies controlled stimuli to the object, and sensors capture the responses, which are then analyzed by a machine learning model to determine structural integrity, eliminating the need for manual inspection while maintaining accuracy.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the sensor data and the structural status determination. The machine learning model processes the complex sensor responses and patterns, translating them into accurate structural status predictions, thereby enabling automated detection without requiring manual expert analysis.
2Productivity
If external appearance inspection is used, then inspection speed is maintained, but internal structural issues remain undetected
Solution Approach 1:
The patent replaces visual external inspection with a stimulus-response system that can penetrate and assess internal structures. The stimulus source generates waves or signals that interact with the internal structure of the object, and sensors detect the responses, enabling the system to identify internal issues such as mold, fungus, or structural defects while maintaining high inspection speed through automation.
Solution Approach 2:
The patent utilizes mechanical vibration or wave propagation as a stimulus agent that interacts with the internal structure of the object. By analyzing how the object responds to these vibrations or waves, the system can detect internal structural issues that are not visible from the exterior, thereby improving detection precision without sacrificing inspection speed.
3Measurement precision
If comprehensive internal inspection methods are used, then detection accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent employs a machine learning model that serves multiple functions: it processes data from various sensor types, adapts to different object types, and identifies multiple kinds of internal issues (mold, fungus, structural defects, etc.). This universal approach allows the system to maintain high detection accuracy across diverse applications without requiring separate specialized systems for each inspection task.
Solution Approach 2:
The machine learning model acts as an intelligent intermediary that simplifies the overall system complexity. Instead of requiring complex hardware for each type of detection, the machine learning model processes diverse sensor inputs and handles the complexity of pattern recognition and classification, thereby reducing hardware complexity while maintaining high detection accuracy.
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
Enables rapid and accurate prediction of structural statuses, reducing the time and effort required for inspection and detecting internal issues that may not be apparent from the exterior, thereby improving quality control and preventing damage or spoilage.
Implementation Method 1
the stimulus source is configured to output a stimulus agent towards the object
Data Source
AI summary
Systems and techniques are disclosed for predicting the structural status of an object. An object model, such as a machine learning model, can be trained on sample sensor data indicating vibrations, movements, and/or other reactions of objects with known desired and undesired structural statuses to a stimulus agent, such as a puff of air. A scanning device can output a corresponding stimulus agent towards an object, capture sensor data indicating the reaction of the object to the stimulus agent, and provide the sensor data to the trained object model. Based on the sensor data indicating how the object reacted to the stimulus agent, the object model can predict whether the object has a desired structural status or an undesired structural status.