Fastening Tool System with Camera for Automatic Torque Adjustment
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Solution Overview
Problem
In vehicle manufacturing, workers face inefficiencies and increased maintenance costs due to the need to manage and switch between various fastening tools with different torque values, leading to potential human errors and compromised product quality.
Innovation Solution
A fastening tool system equipped with a camera, distance measurement sensor, and machine learning algorithms for image classification, which automatically recognizes the fastening portion and adjusts the torque value accordingly, reducing the need for multiple tools and manual torque settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workers use multiple fastening tools with different torque values, then appropriate torque can be applied to each fastening portion, but working efficiency deteriorates and maintenance costs increase
Solution Approach 1:
The patent applies universality by enabling a single fastening tool to perform multiple functions through automatic torque value adjustment. The tool can adapt to different fastening portions by recognizing them via image processing and automatically selecting appropriate torque values from stored data, eliminating the need for multiple specialized tools while maintaining reliable torque application for each fastening type
Solution Approach 2:
The patent replaces the manual mechanical system of tool selection and torque adjustment with an automated optical and control system. Image processing technology captures and analyzes fastening portion characteristics, while a controller automatically adjusts torque values based on recognized features, substituting worker knowledge and manual operations with automated detection and control mechanisms
2Reliability
If workers manually change torque values, then appropriate torque can be set for each fastening portion, but human errors occur affecting product quality
Solution Approach 1:
The patent implements feedback by using image processing to detect fastening portion characteristics and automatically adjusting torque values based on this detected information. The system continuously monitors the fastening portion through imaging, compares it with stored reference data, and adjusts torque accordingly, creating a closed-loop control system that eliminates manual error while maintaining precise torque application
Solution Approach 2:
The fastening tool performs self-service by automatically identifying the fastening portion type through image processing and autonomously selecting the appropriate torque value without worker intervention. The tool uses its own imaging and processing capabilities to determine and apply the correct torque, making the system self-sufficient and error-proof regarding torque selection
3Adaptability or versatility
If workers replace fastening tools frequently, then appropriate tools can be used for each task, but device complexity and management difficulty increase
Solution Approach 1:
The patent applies universality by transforming a single fastening tool into a multi-functional device capable of handling various fastening portions. By integrating image processing and automatic torque adjustment capabilities, one tool replaces multiple specialized tools, reducing management complexity while maintaining the adaptability to match appropriate torque settings for each fastening application
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 enhances working efficiency by automating torque adjustments and reduces maintenance costs by simplifying tool management, while ensuring accurate fastening and improved product quality.
Implementation Method 1
when a distance with the fastening portion measured through a distance measurement sensor is within a predetermined distance
Data Source
AI summary
A control method of a fastening tool for fastening a component part includes photographing a fastening portion of the component part through a camera portion mounted on the fastening tool. The control method includes pre-processing that rotates an input image, which has been photographed by the camera portion, to match with representative model image. The control method includes estimating the fastening portion through an inference process through a convolutional neural network (CNN) algorithm-based image classification work on a video input image of a same fastening portion finished with the pre-processing work. The control method also includes setting a torque value that is matched with a recognized fastening portion when a probability value of the fastening portion in the inference process exceeds a predetermined reference ratio.