Impact Tool Torque Setting by Fastener Visual Identification
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Solution Overview
Problem
Existing power tools lack the ability to accurately identify 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 operating parameters like torque based on these characteristics, enabling precise motor control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If power tools use fixed torque settings, then the tool structure remains simple, but torque application is inefficient and may cause damage to fasteners or workpieces
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 torque setting based on the identified fastener type, ensuring precise torque application without requiring complex real-time adjustment mechanisms during the fastening process.
Solution Approach 2:
The system changes the torque parameter dynamically based on the identified fastener characteristics. The controller selects from multiple predefined torque settings corresponding to different fastener types, materials, and lubrication conditions. This allows precise torque application tailored to each fastener while maintaining a manageable set of discrete torque levels rather than continuous adjustment complexity.
2Reliability
If power tools lack fastener identification capability, then the device complexity remains low, but the reliability of torque application deteriorates
Solution Approach 1:
The system replaces manual fastener identification and torque setting with an automated machine learning-based visual identification system. The camera captures images of the fastener, and the machine learning model automatically determines fastener characteristics and recommends appropriate torque settings. This substitution of mechanical/manual operations with automated intelligent systems improves reliability while managing device complexity through software-based solutions.
Solution Approach 2:
The system enables the power tool to automatically identify fastener characteristics and determine appropriate torque settings without requiring user expertise or manual intervention. The machine learning model processes the captured fastener image and autonomously selects the optimal torque parameter, making the tool self-sufficient in determining the correct operating conditions for reliable fastener installation.
3Productivity
If power tools use generic torque settings, then the ease of operation remains high, but the productivity deteriorates due to potential rework and damage
Solution Approach 1:
The system performs preliminary identification of fastener characteristics before the fastening operation begins. By capturing an image of the fastener and using machine learning to determine its properties (size, thread pitch, material, lubrication), the system pre-determines the optimal torque setting. This preliminary action prevents the need for rework or damage correction, thereby improving productivity while maintaining ease of operation as the process remains automated and requires minimal user input.
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.