Machine Learning Fastener Seating Control in Power Tools
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Solution Overview
Problem
Existing power tools rely on hard-coded thresholds that cannot adapt to changing conditions or complex applications, such as detecting kickback, and lack advanced control mechanisms for precise fastener seating and torque management.
Innovation Solution
A power tool system incorporating a machine learning controller that processes sensor data using algorithms like neural networks and support vector machines to adjust operational parameters dynamically, based on previous usage data and user inputs, for improved fastener seating and torque control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hard-coded thresholds are used for controlling power tool operation, then device complexity is reduced and ease of manufacture is improved, but adaptability to changing conditions and measurement precision deteriorate
Solution Approach 1:
The patent implements dynamic threshold adjustment by training a machine learning model (neural network) on operational data to adaptively determine fastener seating thresholds. The system transitions from static hard-coded thresholds to dynamic, data-driven thresholds that automatically adjust based on detected patterns in motor current, speed, and operational parameters during fastening operations.
Solution Approach 2:
The system changes the parameters used for control decisions from fixed threshold values to dynamically calculated thresholds based on machine learning analysis of operational data. The neural network processes multiple parameters (motor current, speed, acceleration) and generates adaptive threshold values that optimize fastener seating detection for different fastener types, materials, and operational conditions.
2Device complexity
If hard-coded thresholds are used for controlling power tool operation, then device complexity is reduced, but manufacturing precision and control accuracy deteriorate
Solution Approach 1:
The system implements feedback by continuously monitoring operational parameters (motor current, speed, acceleration) during fastening operations and using this data to train the machine learning model. The neural network learns from actual operational feedback to refine threshold determination, creating a closed-loop system that improves fastener seating precision through iterative learning from real-world performance data.
Solution Approach 2:
The patent replaces traditional mechanical/electrical threshold-based control systems with an intelligent control system using machine learning and neural networks. This substitution enables the system to process complex operational data patterns and make precise control decisions for fastener seating, torque management, and kickback detection that exceed the capabilities of simple threshold comparisons.
3Ease of operation
If traditional threshold-based control is used, then ease of operation is maintained, but productivity and operational efficiency deteriorate due to inability to detect complex conditions
Solution Approach 1:
The system implements self-service by enabling the power tool to automatically learn and optimize its own operational parameters through the machine learning model. The neural network autonomously analyzes operational data, identifies patterns indicating fastener seating, kickback, or over-tightening, and adjusts control thresholds without user intervention, allowing the tool to self-optimize for improved productivity while maintaining ease of operation.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model with operational data before actual fastening operations. The neural network is trained offline on datasets containing various fastening scenarios, enabling it to recognize patterns and make accurate predictions during operation, thus preparing the system in advance for optimal performance without requiring real-time complex computations that would reduce productivity.
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.