Power Tool ML Control for Fastener Seating and Kickback
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Solution Overview
Problem
Existing power tools rely on hard-coded thresholds that cannot adapt to changing conditions or complex situations, such as kickback, leading to inefficiencies and potential damage.
Innovation Solution
Implementing a machine learning controller that analyzes sensor data to dynamically adjust tool operations based on previous usage and user inputs, using algorithms like neural networks and support vector machines to optimize fastener seating and torque control.
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 complex situations
Solution Approach 1:
The patent implements a machine learning model that dynamically adjusts control parameters based on real-time sensor data and historical usage patterns. The system transitions from static hard-coded thresholds to dynamic adaptive control, where the machine learning model continuously learns and adjusts to changing conditions during fastener seating operations.
Solution Approach 2:
The machine learning controller performs self-learning and self-adjustment by analyzing sensor data and operational outcomes. The system automatically improves its control algorithms through continuous operation without requiring manual reprogramming, enabling it to adapt to different fastener types, materials, and operating conditions autonomously.
2Productivity
If machine learning algorithms are implemented to adapt to changing conditions, then the system becomes more versatile and efficient, but the device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical control systems with fixed thresholds with an intelligent machine learning-based control system. This substitution enables the system to process complex sensor data, recognize patterns, and make adaptive decisions that improve fastener seating efficiency while handling varying operational conditions.
Solution Approach 2:
The machine learning controller is designed to handle multiple functions including real-time monitoring, pattern recognition, parameter optimization, and anomaly detection. This multi-functional approach consolidates various control tasks into a single adaptive system that can manage different fastening scenarios without requiring separate control mechanisms for each situation.
3Manufacturing precision
If machine learning algorithms are used for real-time control, then the seating precision is improved, but the computational requirements and processing time increase
Solution Approach 1:
The machine learning model is pre-trained offline using extensive datasets to learn optimal control strategies and patterns. This preliminary training phase allows the model to capture complex relationships in advance, so that during real-time operation, the system can make rapid predictions and adjustments without performing computationally intensive training calculations, thus maintaining high precision while minimizing processing time.
4Adaptability or versatility
If hard-coded control thresholds are used, then the system is reliable and predictable, but it cannot handle complex situations like kickback
Solution Approach 1:
The patent implements a closed-loop feedback system where sensor data from fastener seating operations is continuously fed back to the machine learning controller. The system monitors operational parameters, compares them against learned patterns, and adjusts control actions in real-time. This feedback mechanism enables the system to detect and respond to complex situations like kickback while maintaining reliability through continuous adaptation based on actual operational outcomes.
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.