Collaborative Robot Teaching Interface for Low-Volume Welding Paths
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Solution Overview
Problem
Current automated robot welding and cutting systems require skilled operators for setup and programming, are costly, and lack versatility for high mix, low volume production environments, posing a challenge for smaller manufacturers due to high capital investment and complex programming requirements.
Innovation Solution
A dynamic programmable teaching system that allows operators to intuitively program and control collaborative robot welding and cutting systems using graphical interfaces and lighted pushbuttons, enabling real-time adjustments and optimization of processing parameters without extensive programming knowledge, and allowing template programs to be reused with minimal adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automated robot welding and cutting systems are used, then manufacturing precision and automation extent are improved, but device complexity and operator skill requirements increase
Solution Approach 1:
The system performs self-teaching by automatically recording processing paths and parameters during demo runs, eliminating the need for complex manual programming. The robot system captures its own motion data and operational parameters autonomously during the demonstration phase.
Solution Approach 2:
Traditional manual teaching methods involving complex coordinate programming are replaced with intuitive demo-run-based teaching. The system substitutes sophisticated programming mechanics with simple demonstration-based parameter capture, where operators guide the robot through desired operations without writing code.
2Manufacturing precision
If skilled operators are used for setup and programming, then manufacturing precision is improved, but loss of time and productivity are worsened due to trial and error runs
Solution Approach 1:
The system performs preliminary demo runs to capture processing paths and parameters before actual production. These demonstration runs establish the complete operational template in advance, eliminating the need for time-consuming trial and error adjustments during setup.
Solution Approach 2:
The system uses feedback from demo runs to automatically refine and optimize processing parameters. By analyzing the captured data from demonstration operations, the system self-corrects and optimizes welding and cutting parameters, reducing the need for manual trial and error iterations.
3Reliability
If traditional programming methods are used, then reliability is improved through consistent automated operations, but ease of operation deteriorates due to lack of versatility for high mix low volume production
Solution Approach 1:
The system dynamically adapts between different production modes by switching between demo-run-based teaching for new operations and template execution for repeat operations. This dynamic flexibility allows the same system to handle both high-volume production with automated templates and low-volume custom work with intuitive demo teaching.
Solution Approach 2:
The system serves multiple functions through a unified interface: it can perform complex welding and cutting operations, capture demo runs, generate processing templates, and execute repeated operations. This multi-functionality allows a single system to handle diverse production requirements from high-volume to low-volume manufacturing.
Data Source
AI summary
A dynamic programmable teaching system adapted to program and control the operation of collaborative robot welding and cutting systems, minimize the need to use a teach pendant for programming and to provide visual feedback in order to optimize or correct operational processing parameters for any given welding or cutting task.