Power Tool Machine Learning Control for Adaptive Kickback Detection
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Solution Overview
Problem
Existing power tools rely on hard-coded thresholds that cannot adapt to changing conditions or applications, failing to effectively detect and respond to complex situations such as kickback, limiting their operational efficiency.
Innovation Solution
The integration of a machine learning controller that uses sensor data to adjust operational parameters and thresholds based on previous tool usage data, allowing the power tool to adapt to specific applications and modes, and providing real-time feedback for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hard-coded thresholds are used for controlling power tool operation, then device complexity is reduced and ease of manufacture is improved, but adaptability to changing conditions and applications deteriorates
Solution Approach 1:
The patent implements dynamic thresholds that automatically adjust based on real-time sensor data and machine learning algorithms. The system transitions from static hard-coded values to dynamic adaptive thresholds that change according to operational conditions, work material characteristics, and tool state, thereby resolving the contradiction between ease of manufacture and adaptability.
Solution Approach 2:
The system changes the parameter of thresholds from fixed constant values to variable values that are continuously updated based on sensor inputs and machine learning predictions. This parameter transformation enables the system to adapt to different applications and conditions while maintaining a relatively simple overall architecture.
2Adaptability or versatility
If machine learning controller is integrated to enable adaptive operation, then adaptability and detection capability are improved, but device complexity increases
Solution Approach 1:
The patent segments the control system into distinct functional modules: sensor data acquisition module, machine learning processing module, threshold determination module, and execution module. This segmentation allows the complex machine learning functionality to be integrated in a structured manner, managing complexity through modular design while maintaining adaptability.
Solution Approach 2:
The system introduces an intermediary layer between raw sensor data and control decisions - the machine learning model that processes sensor inputs and generates adaptive thresholds. This intermediary handles the complexity of pattern recognition and adaptation, shielding the rest of the system from complex algorithms while enabling sophisticated behavior.
3Measurement precision
If real-time sensor data processing is implemented, then detection precision and response capability are improved, but use of energy and computational load increase
Solution Approach 1:
The system implements partial processing by focusing computational resources on the most critical sensor data and features relevant to threshold determination. Rather than processing all sensor data at full resolution, the machine learning model processes only the essential features needed for accurate detection, reducing energy consumption while maintaining detection precision.
Data Source
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.