Power Tool ML Control for Precise Fastener Seating
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Solution Overview
Problem
Existing power tools rely on hard-coded thresholds for controlling fastener seating, which cannot adapt to changing conditions or applications, and may not effectively detect and respond to complex conditions like kickback.
Innovation Solution
A power tool system that incorporates a machine learning controller, which processes sensor data to generate outputs indicating seating values associated with fastening operations, allowing for adaptive control of the motor based on real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hard-coded thresholds are used for controlling fastener seating, then the control logic is simple and easy to implement, but the system cannot adapt to changing conditions or applications
Solution Approach 1:
The patent implements a machine learning model that dynamically adjusts control parameters based on real-time sensor data and historical operational patterns. The system transitions from static hard-coded thresholds to dynamic adaptive control, where the controller continuously learns and adjusts to varying fastening conditions, material properties, and tool performance characteristics.
Solution Approach 2:
The machine learning controller performs self-learning and self-adjustment by processing sensor data and automatically optimizing control parameters without requiring external reprogramming. The system serves itself by continuously improving its control strategy through accumulated operational data, enabling adaptability while maintaining relatively simple hardware architecture.
2Measurement precision
If hard-coded thresholds are used for detecting fastener seating, then the detection logic is straightforward, but the precision and reliability of seating detection is insufficient
Solution Approach 1:
The patent implements a closed-loop feedback system where sensor data from torque, speed, and position sensors continuously feeds back to the machine learning controller. The controller compares actual measurements against learned patterns and thresholds, adjusting control signals to achieve precise seating detection. This feedback mechanism enables high detection precision by continuously refining the detection criteria based on actual operational data.
Solution Approach 2:
The patent replaces simple mechanical threshold-based detection with an intelligent system that uses machine learning algorithms to analyze sensor data patterns. The system substitutes straightforward comparison logic with sophisticated pattern recognition and prediction algorithms, significantly improving seating detection precision while managing complexity through software-based solutions.
3Reliability
If machine learning control is implemented, then the system can adapt to varying conditions and improve seating precision, but the device complexity increases
Solution Approach 1:
The patent designs the machine learning controller to perform multiple functions: it processes sensor data, predicts fastener seating status, adjusts motor control parameters, and learns from operational patterns. By consolidating these diverse functions into a single intelligent controller, the system achieves high reliability across varying conditions while managing overall device complexity through functional integration rather than proliferation of separate components.
Data Source
AI summary
A power tool is provided including a housing a motor supported by the housing, a sensor supported by the housing, and an electronic controller. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool. The electronic controller includes an electronic processor, and a memory. The memory includes a machine learning control program for execution by the electronic processor. The electronic processor is configured to receive the sensor data, and process the sensor data, using the machine learning control program. The electronic processor is further configured to generate, using the machine learning control program, an output based on the sensor data, the output indicating a seating value associated with a fastening operation of the power tool. The electronic processor is further configured to control the motor based on the generated output.