Welding Motion Control Using a Simulated Molten Pool View
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Solution Overview
Problem
Existing welding technologies face challenges in accurately replicating the skilled worker's dark cognitive motions during arc welding due to the obstruction of the robot arm and the difficulty in quantifying and verbalizing these skills, leading to suboptimal welding quality.
Innovation Solution
A welding control device that includes a welding DB, a state sensor, a simulated environment, and a motion control unit to capture and record the worker's motion data in a simulated environment, allowing the robot to replicate the skilled worker's motions accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If direct teaching is performed by moving the robot arm while observing the molten pool, then highly accurate motion data can be recorded, but the robot arm blocks the field of view and prevents accurate observation of the molten pool
Solution Approach 1:
A simulated environment acts as an intermediary between the worker and the real welding scene. The simulated torch in the virtual environment allows the worker to observe molten pool state and perform teaching operations without the physical robot arm blocking the view in the real environment. This mediator enables simultaneous observation and teaching.
Solution Approach 2:
The patent creates a copy of the real welding environment as a simulated environment. The simulated molten pool visually matches the real molten pool, allowing the worker to teach based on the simulated environment while the robot performs actual welding. The simulated torch position corresponds to the real robot torch position, enabling accurate motion data recording without view obstruction.
2Productivity
If off-line teaching is used to automatically calculate motion data, then teaching efficiency is improved, but only simple settings are supported and highly accurate motion control of 1 mm or less cannot be achieved
Solution Approach 1:
The teaching process is segmented into two parts: off-line teaching for simple settings (welding speed, height) and simulated environment teaching for high-precision motion control. This segmentation allows each method to be used for its strengths, combining efficiency and precision.
Solution Approach 2:
Off-line teaching performs preliminary setup of basic welding parameters before the actual teaching phase. This preliminary action prepares the system for subsequent high-precision teaching in the simulated environment, improving overall efficiency while maintaining accuracy.
3Manufacturing precision
If direct teaching is performed in the real welding environment, then highly accurate motion data can be recorded, but it is difficult to reflect the worker's dark cognitive motion that is difficult to quantify and verbalize
Solution Approach 1:
The simulated environment provides continuous visual feedback of the molten pool state during teaching, allowing the worker to periodically adjust motions based on observed state. This continuous feedback loop captures the iterative decision-making process inherent in dark cognition, enabling it to be reflected in the recorded motion data.
Data Source
AI summary
A master unit includes a welding DB in which prescribed motion data associated with an object to be welded is stored, a state sensor which measures, as welding state data, a situation of welding by a robot which executes welding in a real environment according to the prescribed motion data, a simulated environment which imitates the real environment and notifies a worker of the welding state data, and a motion control unit which receives, as an input, worker motion data indicating a motion of welding by the worker from the simulated environment, operates the robot in the real environment by using the worker motion data instead of the prescribed motion data, and records, as new prescribed motion data, the input worker motion data in the welding DB.