Power Tool Fastener Recognition for Accurate Torque Control
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Solution Overview
Problem
Existing power tools lack the ability to accurately determine the characteristics of fasteners, such as size, thread pitch, material, and lubrication, leading to inefficient torque application and potential damage to fasteners or workpieces.
Innovation Solution
The power tool incorporates a controller that uses a camera to capture visual indications of fasteners, applies machine-learning models to identify their characteristics, and sets motor operating parameters based on these characteristics, such as K-Factor, to optimize torque delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a fixed torque setting is used in existing power tools, then the tool structure remains simple, but the torque application is inaccurate and may cause fastener damage
Solution Approach 1:
The system performs preliminary identification of fastener characteristics (size, thread pitch, material, lubrication) using machine learning models before torque application. This allows the controller to pre-determine the appropriate K-Factor and torque settings based on the identified fastener type, ensuring accurate torque application without requiring complex real-time adjustments during operation
Solution Approach 2:
The patent replaces manual torque setting mechanisms with an automated optical detection and machine learning-based control system. The camera captures visual indications of fasteners, and machine learning models analyze these images to identify fastener characteristics, automatically determining optimal torque settings without manual intervention or complex mechanical adjustment mechanisms
2Productivity
If manual fastener identification is required, then the control system remains simple, but the operation efficiency decreases and time is lost
Solution Approach 1:
The power tool performs self-identification of fastener characteristics through its integrated camera and machine learning models. The system automatically captures visual indications of the fastener, analyzes the image to determine fastener type and properties, and autonomously adjusts torque settings without requiring operator intervention or manual lookup of fastener specifications
Solution Approach 2:
The patent introduces a camera as an intermediary device to capture visual indications of fasteners, which are then processed by machine learning models to identify fastener characteristics. This intermediary optical detection system bridges the gap between the operator and the fastener, enabling automatic identification and torque setting adjustment without direct manual measurement or inspection
3Reliability
If generic torque settings are applied to all fasteners, then the control system remains simple, but fastener damage occurs and reliability decreases
Solution Approach 1:
The system applies local quality by determining specific torque settings tailored to each identified fastener type rather than using a universal torque value. The machine learning model analyzes visual characteristics of the specific fastener present and adjusts the K-Factor and torque parameters accordingly, ensuring each fastener receives the precise torque it requires based on its material, size, thread pitch, and lubrication conditions
Solution Approach 2:
The patent dynamically changes control parameters (K-Factor and torque settings) based on the identified fastener characteristics. The machine learning model determines the appropriate K-Factor value corresponding to the detected fastener type, and the controller adjusts the motor torque output accordingly, transforming the control system from static generic settings to dynamic adaptive parameter adjustment
Data Source
AI summary
Systems and methods for determining tool parameters based on characteristics of a driven fastener. Power tools described herein include a motor and an impact mechanism coupled to the motor. The impact mechanism includes a hammer driven by the motor and an anvil configured to receive an impact from the hammer and drive a fastener. The power tool includes a controller connected to the motor. The controller is configured to receive a visual indication of the fastener and identify a type of the fastener based on the visual indication. The controller is configured to determine a K-Factor of the fastener based on the type of the fastener, set an operating parameter for driving the motor based on the K-Factor, and drive the motor according to the operating parameter.