Step Bit Progress Detection Using Multi-Sensor ML Control
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Solution Overview
Problem
Power tools face challenges in accurately controlling step bits during drilling, leading to potential over-drilling due to noisy sensor feedback from variations in user operation, workpiece characteristics, and step bit behavior, making it difficult to maintain precise hole sizes and depths.
Innovation Solution
A machine learning controller is integrated into power tools to process sensor data from parameters like current, voltage, and torque, allowing for real-time monitoring and adjustment of step bit progress, enabling precise control and preventing over-drilling by generating output that indicates step bit advancement and confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional sensor feedback control is used for step bit drilling, then the control system is simple, but the measurement precision deteriorates due to noisy sensor feedback from variations in user operation, workpiece characteristics, and step bit behavior
Solution Approach 1:
The patent introduces an intermediary processing layer between the sensor and the control system. Multiple sensors (current sensor, voltage sensor, torque sensor, acceleration sensor) are used as intermediaries to capture different aspects of the drilling process, and their data is combined through a control algorithm to achieve more accurate step bit progress detection than any single sensor could provide alone.
Solution Approach 2:
The patent changes the parameters being monitored from simple sensor readings to a combination of multiple parameters (current, voltage, torque, acceleration) that are processed together. By analyzing changes in these parameters over time and their relationships, the system can detect step bit progress more accurately despite individual parameter noise.
2Measurement precision
If multiple sensors are used to improve measurement precision, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent makes the control system multi-functional by having it perform multiple tasks: monitoring current, voltage, torque, acceleration; detecting step bit progress; determining drilling depth; controlling motor speed; and providing feedback to the user. This consolidates what could be multiple separate systems into one universal control unit, reducing overall device complexity while maintaining high measurement precision.
Solution Approach 2:
The patent merges multiple sensor functions and control functions into a single integrated control system. The current sensor, voltage sensor, torque sensor, and acceleration sensor are combined with the motor controller and user interface into one unified system that processes all inputs and generates coordinated outputs, simplifying the overall device architecture.
3Manufacturing precision
If real-time monitoring and adjustment is implemented, then the manufacturing precision improves, but the loss of time increases due to processing sensor data and generating control outputs
Solution Approach 1:
The patent implements continuous monitoring and control rather than periodic sampling. The sensors continuously feed data to the control system, which continuously adjusts motor parameters. This continuous action eliminates idle time between measurements and adjustments, making the processing time part of the productive drilling process rather than a separate overhead.
Solution Approach 2:
The patent implements a closed-loop feedback system where sensor data is immediately processed and used to adjust motor control parameters in real-time. The feedback loop operates continuously, with the control system constantly comparing actual drilling progress against target parameters and making immediate corrections, minimizing the time delay between detection and correction.
Data Source
AI summary
Devices and methods for automatically controlling a step bit operation in a power tool. The method includes generating, by a sensor of the power tool, sensor data indicative of an operational parameter of the power tool wherein a step bit is coupled to the power tool. An electronic control assembly of the power tool receives the sensor data, where the electronic control assembly includes an electronic processor and a memory. The memory stores a machine learning control program for execution by the electronic processor. The electronic control assembly processes the sensor data using a machine learning control program of the electronic control assembly and generates, using the machine learning program, an output based on the sensor data. The output indicates step bit progress information. The electronic control assembly controls a motor supported by the housing of the power tool based on the output.