Step Bit Progress Detection from Noisy Drill Sensor Signals
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Solution Overview
Problem
Power tools, such as drills, face challenges in accurately controlling step bits to prevent over-drilling and efficiently detecting the advancement of steps due to noisy sensor feedback from variations in user behavior, workpiece characteristics, and step bit characteristics, leading to difficulties in maintaining desired 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, recognizing patterns over multiple uses to adjust motor control, providing real-time feedback and alerts to users, and automatically adjusting operation based on learned profiles of common hole sizes and step bit types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based control is used to detect step bit advancement, then the system can monitor drilling progress, but the measurement precision deteriorates due to noisy sensor feedback from variations in user behavior, workpiece characteristics, and step bit characteristics
Solution Approach 1:
The patent introduces machine learning algorithms as an intermediary layer between the noisy sensor data and the control decisions. The ML model processes the raw sensor feedback from current, voltage, and torque sensors, extracting meaningful patterns while filtering out noise from user behavior variations and workpiece characteristics. This intermediary processing layer transforms unreliable raw sensor data into reliable step advancement detection.
Solution Approach 2:
The system changes the parameters used for detection by transitioning from direct sensor threshold-based detection to ML-based pattern recognition. The ML model learns optimal parameter combinations and relationships from training data, adapting to different step bit types, materials, and drilling conditions. This parameter transformation enables accurate detection despite variations in raw sensor readings.
2Manufacturing precision
If a machine learning controller is integrated to process sensor data and provide adaptive control, then the manufacturing precision of hole sizes and depths is improved, but the device complexity increases due to additional processing components and algorithms
Solution Approach 1:
The machine learning controller serves multiple functions simultaneously: it detects step bit advancement, determines drilling depth, monitors hole diameter, adapts to different step bit types, and provides predictive control. By consolidating these multiple functions into a single ML-based control system, the patent achieves high manufacturing precision without proportionally increasing device complexity compared to having separate dedicated systems for each function.
Solution Approach 2:
The ML controller automatically adapts and learns from operational data without requiring manual reconfiguration or external intervention. It self-calibrates to different step bit characteristics, materials, and user behaviors through continuous learning from sensor feedback. This self-service capability reduces the operational complexity and eliminates the need for complex manual setup procedures.
3Measurement precision
If the system uses multiple sensor parameters and machine learning processing to detect steps accurately, then the measurement precision is improved, but the loss of time increases due to complex data processing requirements
Solution Approach 1:
The machine learning model is pre-trained offline with extensive sensor data from various drilling conditions, step bit types, and materials. During actual operation, the pre-trained model quickly processes sensor data using learned patterns without requiring complex real-time calculations. This preliminary training action separates the computationally intensive learning phase from the time-critical detection phase, maintaining high precision while minimizing real-time processing time.
Solution Approach 2:
The system processes a subset of the most relevant sensor parameters through the ML model rather than analyzing all possible data points in detail. The ML algorithm identifies and focuses on the most informative features from the sensor data, performing partial processing that achieves sufficient detection accuracy without the computational overhead of exhaustive analysis of all sensor readings.
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.