Electric Tool Control With Learned Motion Replay for Repetitive Tasks
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Solution Overview
Problem
Conventional electric tools require high user concentration for repetitive operations, leading to potential distractions and inconsistencies, especially during tasks like fastening multiple screws, which can result in user fatigue and increased risk of accidents.
Innovation Solution
An electric tool with a controller that operates in learning and execution modes, recording and replaying varying patterns of operation, including motor parameters and user input, to automate repetitive tasks without user intervention, utilizing an AI mechanism to discard deviated profiles and average optimal ones for precise playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual control mechanism is used, then user can control the electric tool, but user concentration and labor intensity increase significantly during repetitive operations
Solution Approach 1:
The system records the user's operation pattern during a learning phase and creates a digital copy of this pattern. During execution phase, this copied pattern is replayed automatically to control the electric tool, eliminating the need for continuous manual control and ensuring consistent reproduction of the desired operation pattern across multiple tasks.
Solution Approach 2:
The electric tool system performs self-control by automatically replaying the recorded operation pattern without requiring continuous user intervention. The system serves itself by using its own recorded behavior to control its operation, thereby reducing user labor intensity and maintaining operational consistency during repetitive tasks.
2Productivity
If simple control mechanism is used, then device complexity is low, but productivity decreases due to user fatigue and distractions during repetitive operations
Solution Approach 1:
The control mechanism dynamically switches between two operational modes: learning mode and execution mode. During learning mode, the system records the user's operation pattern. During execution mode, it automatically replays this pattern. This dynamic mode switching enables the system to adapt from manual control to automated control, significantly improving productivity during repetitive operations while managing device complexity through structured operational phases.
Solution Approach 2:
The system performs preliminary action by recording the desired operation pattern during a learning phase before actual repetitive production begins. This pre-recorded pattern serves as a template that guides subsequent operations, allowing the system to execute repetitive tasks automatically without requiring user intervention during the actual work phase, thereby improving productivity.
3Manufacturing precision
If manual precise control is required throughout operation, then operational precision can be maintained, but user fatigue increases leading to loss of concentration
Solution Approach 1:
The system captures the precise operation pattern during a learning phase and stores it as a reference template. During subsequent execution phases, this copied pattern is replayed automatically to control the electric tool's operation. This approach maintains manufacturing precision by consistently reproducing the recorded pattern while eliminating the need for continuous user concentration over extended operation durations.
Data Source
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AI summary
An electric tool includes a motor (12) for driving a working element, a controller (2) connected to the motor, a first user input device connected to the controller, and a memory (6) connected to the controller. The controller is configured to operate in a learning mode and an execution mode. In the learning mode the controller is adapted to record a varying pattern of operation of electric tool and store the varying pattern in the memory. In the execution mode, the controller is adapted to control the electric tool to operate without user intervention by replaying the varying pattern stored in the memory. With the learning mode, end users can pay less attention to the operation since the electric tool can replay the working process in the execution mode to prevent material being damaged for the same type of work.