Power Tool Machine Learning Control for Adaptive Motor Response
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Solution Overview
Problem
Conventional power tools rely on hard-coded thresholds for operation adjustments, which are inflexible and unable to adapt to changing conditions or complex scenarios, such as kickback detection, limiting their effectiveness in dynamic applications.
Innovation Solution
The integration of a machine learning controller that uses sensor data to generate outputs for adjusting tool operations, such as identifying fasteners, operating modes, torque values, and detecting obstacles, and can retrain based on feedback, allowing for adaptive and context-aware control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hard-coded thresholds are used for operation adjustments, then device complexity is reduced and ease of manufacture is improved, but adaptability to changing conditions and complex scenarios deteriorates
Solution Approach 1:
The patent implements a machine learning controller that dynamically adjusts operational parameters based on real-time sensor data and learned patterns, replacing static hard-coded thresholds with adaptive decision-making algorithms that evolve through training on operational data
Solution Approach 2:
The system changes the control parameters from fixed threshold values to dynamic, data-driven parameters generated by the machine learning model, allowing the controller to adapt its behavior based on learned relationships between sensor inputs and optimal operational states
2Measurement precision
If machine learning controller is integrated, then adaptability and detection capabilities are improved, but device complexity and manufacturing difficulty increase
Solution Approach 1:
The machine learning controller serves multiple functions simultaneously: it detects operational conditions, classifies fastener types, predicts tool state, and generates control commands, replacing multiple specialized sensors and controllers with a single multi-functional intelligent system
Solution Approach 2:
The system uses sensor data to create virtual representations of physical conditions (such as identifying fastener types and tool states) without requiring physical copies or additional hardware sensors, processing information through software-based machine learning models
3Productivity
If machine learning controller retraining based on feedback is implemented, then productivity and operational efficiency are improved, but loss of time for training and computational resources increase
Solution Approach 1:
The system implements continuous feedback loops where operational outcomes are monitored and used to retrain the machine learning model, allowing the controller to learn from actual performance and improve its predictions and control decisions over time
Solution Approach 2:
The machine learning controller is pre-trained on comprehensive datasets before deployment, establishing a baseline level of performance that allows the system to function effectively while continuing to improve through incremental retraining on new operational data
Data Source
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AI summary
A power tool includes a housing and a sensor, a machine learning controller, a motor, and an electronic controller supported by the housing. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool. The machine learning controller includes a first processor and a first memory and is coupled to the sensor. The machine learning controller further includes a machine learning control program configured to receive the sensor data, process the sensor data using the machine learning control program, and generate an output based on the sensor data using the machine learning control program. The electronic controller includes a second processor and a second memory and is coupled to the motor and to the machine learning controller. The electronic controller is configured to receive the output from the machine learning controller and control the motor based on the output.