Smart Tool Image Analysis for Correct Attachment and Force Use
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Solution Overview
Problem
Existing assembly systems face inefficiencies and inaccuracies due to human errors in tool selection and application, leading to incorrect tool usage and improper force application, which can result in suboptimal assembly outcomes.
Innovation Solution
A smart tool system equipped with a neural network that analyzes images from onboard and wearable cameras to identify the tool, attachment, or extension, and determines its position relative to a component, ensuring correct tool usage and force application through image processing and neural network analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sorted tools and attachments are presented to the user in predetermined order or locations, then tool selection efficiency is improved, but human errors such as placing tools in wrong bins or selecting incorrect tools still occur leading to assembly inaccuracies
Solution Approach 1:
The patent replaces the mechanical sorting system with an image recognition and neural network-based identification system. The smart tool uses cameras to capture images of attachments, and a neural network processes these images to automatically identify and verify the correct attachment is selected, eliminating reliance on manual sorting and human visual inspection.
Solution Approach 2:
The system enables self-verification by having the smart tool automatically capture images of the selected attachment, process them through the neural network, and verify correctness without human intervention. The system serves itself by autonomously identifying and validating the attachment selection.
2Device complexity
If manual tool selection and force application are used, then system complexity is reduced, but assembly precision and consistency deteriorate due to human errors in tool selection and force application
Solution Approach 1:
The patent replaces manual mechanical operations with automated image processing and neural network analysis. The smart tool automatically captures images, processes them through computational algorithms, and uses the results to control force application, substituting human cognitive and manual operations with automated electronic systems.
Solution Approach 2:
The system implements feedback by capturing images of the selected attachment, processing them through the neural network to identify the attachment type, and using this information to automatically adjust and control the force application during assembly operations, ensuring precision through closed-loop control.
3Measurement precision
If image processing and neural network analysis are implemented in the smart tool, then tool identification accuracy is improved, but device complexity and processing time increase
Solution Approach 1:
The neural network is pre-trained on a comprehensive dataset of attachment images before deployment in the smart tool. This preliminary training allows the network to perform rapid and accurate identification during actual use, reducing the computational burden and complexity during real-time operations.
Solution Approach 2:
The system uses image copies (photographs) of the physical attachments as the basis for identification. Instead of directly analyzing physical characteristics, the smart tool captures optical images and processes these digital copies through the neural network, simplifying the identification process while maintaining high accuracy.
Data Source
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Figure 3
AI summary
A smart tool (40, 100) includes a body having a working output (44,120). A first controller (121) is disposed within the body and connected to a plurality of sensors. The plurality of sensors includes at least one camera (42, 50,110) having a field of view at least partially capturing the working output (44, 120). A neural network is trained to analyze an image feed from the at least one camera (42, 50, 110) and trained perform at least one of classifying at least one of a working tool (150) and an extension connected to the working output, classifying a component (22) and/or a portion of a component interfaced with the working output, and determining a positioning of the smart tool (100). The neural network is stored in one of the first controller (121) and a processing unit remote from the smart tool.