Hand-Held Power Tool Signal Learning for Screw Shut-Off Detection
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Solution Overview
Problem
Existing hand-held power tools, such as rotary impact wrenches, require high user concentration to react to changing machine characteristics, leading to issues like over-tightening or stripping screws, due to limitations in automated reaction mechanisms and reliance on absolute threshold values that fail to adapt to varying applications.
Innovation Solution
A method for operating hand-held power tools that uses a learning process to provide model signal forms and threshold values based on screw profiles, allowing the tool to adapt and recognize work progress through operating variable signals, enabling more reliable and reproducible screwing and unscrewing operations without additional sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sensors such as accelerometers are used to detect operating modes, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical/vibration-based sensors (accelerometers) with an electrical measurement system that monitors motor current to detect operating modes. The control unit analyzes current signal characteristics (amplitude, frequency, patterns) to identify impact mode engagement, disengagement, and other operational states without requiring additional physical sensors.
Solution Approach 2:
The patent creates a virtual model of the expected current signal pattern for impact mode operation and compares actual measured current signals against this model. By copying the characteristic current signature of impact operation into a reference pattern, the system can accurately detect operating modes through signal matching rather than physical vibration sensing.
2Ease of operation
If absolute threshold values are used for impact detection, then ease of operation is improved, but adaptability deteriorates
Solution Approach 1:
The patent transitions from static absolute threshold values to dynamic threshold determination based on learned application-specific parameters. The system adapts its detection criteria by storing characteristic current signal patterns from teaching operations and adjusting threshold levels according to the specific application being performed, enabling both ease of operation and application versatility.
Solution Approach 2:
The patent changes the detection parameters from fixed absolute current thresholds to relative thresholds based on learned application characteristics. By storing and comparing against application-specific current signal patterns obtained during teaching operations, the system maintains automatic operation simplicity while adapting to different screw types, materials, and fastening requirements.
3Adaptability or versatility
If a learning process with example applications is implemented, then adaptability is improved, but loss of time during initial setup increases
Solution Approach 1:
The patent performs the parameter learning and threshold calibration during an initial teaching operation that is executed once per application type. By completing the adaptive parameter setup in advance through a single teaching run, the system eliminates the need for repeated adjustments during subsequent operations, reducing overall time loss compared to manual parameter tuning for each application.
Solution Approach 2:
The system automatically learns and stores application-specific current signal patterns and threshold values during teaching operations without requiring external calibration equipment or expert intervention. The control unit self-configures the detection parameters by analyzing actual operational data from the teaching phase, enabling autonomous adaptation that minimizes setup time and user burden.
Data Source
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AI summary
The invention relates to a method for operating a hand-held power tool, the hand-held power tool comprising an electric motor and the method comprising the method steps: S1 providing comparative information, comprising the steps: S1a providing at least one model signal shape, wherein the model signal shape can be assigned to the work progress of the hand-held power tool; S1b providing a correspondence threshold; S2 determining a signal of an operating variable of the electric motor; S3 comparing the signal of the operating variable with the model signal shape and determining a correspondence assessment from the comparison, wherein the correspondence assessment takes place at least partially on the basis of the correspondence threshold; S4 detecting the work progress at least partially on the basis of the correspondence assessment determined in method step S3; wherein the comparative information is provided at least partially on the basis of a learning process. The invention also relates to a hand-held power tool.