Machine Learning Power Tool Control for Application Identification
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Solution Overview
Problem
Existing power tools lack advanced control mechanisms that can accurately identify and adapt to different applications, fasteners, and materials, leading to inefficient operation and potential damage due to misuse.
Innovation Solution
Integration of a machine learning controller within power tools to process sensor data, including motor speed, current, and motion characteristics, to determine the type of application, fastener, and material being worked on, enabling adaptive control and optimized operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional control mechanisms are used in power tools, then the device complexity is low, but the measurement precision of application identification and adaptability are insufficient
Solution Approach 1:
The patent replaces traditional mechanical control systems with a machine learning-based electronic control system. Sensors collect operational data (motor current, speed, acceleration), which is processed by a machine learning model to identify applications and materials, enabling precise adaptation without mechanical adjustments.
Solution Approach 2:
The machine learning model dynamically changes control parameters (motor power, speed, torque) based on identified applications and materials. The system continuously monitors operational parameters and adjusts them in real-time to optimize performance for each specific task.
2Adaptability or versatility
If machine learning control is implemented, then the adaptability and productivity are improved, but the device complexity and energy consumption increase
Solution Approach 1:
The machine learning control system provides universal adaptability across multiple applications (drilling, driving, cutting, sanding) and material types (wood, metal, plastic, masonry). A single unified control system replaces the need for multiple specialized controls, enabling one tool to perform many functions intelligently.
Solution Approach 2:
The machine learning model enables the power tool to self-identify the application and material, then automatically adjust its operational parameters without user intervention. The system serves itself by making real-time decisions based on sensor data, eliminating the need for manual mode selection or adjustment.
3Productivity
If machine learning control is implemented, then the productivity is improved, but the use of energy increases
Solution Approach 1:
The control system dynamically adjusts motor power delivery based on real-time application identification and material detection. The machine learning model optimizes power consumption by delivering only the necessary energy for each specific task, avoiding both under-powering and over-powering scenarios that waste energy.
Data Source
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AI summary
A power tool (500) includes a housing and a sensor (530), a machine learning controller (540), a motor (505), and an electronic controller (550) supported by the housing. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool. The electronic controller includes an electronic processor (550) and a memory. The memory includes a machine learning control program. The electronic controller is configured to receive the sensor data. The sensor data includes a motor speed of the motor, a motor current of the motor, and a motion characteristic of the power tool. The electronic controller is configured to process the sensor data using the machine learning control program and generate an output based on the sensor data. The output can include an identified type of application that is being performed by the power tool.