Robot Welding Sequence Analytics for Human-Robot Task Allocation
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Solution Overview
Problem
Conventional welding systems face inefficiencies due to the reliance on human operators, who may experience boredom, lack of attention, and require breaks, leading to a shortage of skilled labor and suboptimal operation performance.
Innovation Solution
A robotic welding system that integrates human collaboration and operational analytics to track and analyze human and robot contributions during the welding process, allowing for improved operation efficiency by identifying issues and recommending adjustments to optimize future operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human operators perform welding operations, then flexibility and adaptability are maintained, but productivity and reliability deteriorate due to boredom, lack of attention, and breaks
Solution Approach 1:
The welding process is divided into discrete sequences with clearly defined human and robot tasks. The system segments operations into robot-performed welding sequences and human-performed setup/inspection tasks, allowing each to operate at optimal speeds while maintaining overall process flexibility through the structured sequence framework.
Solution Approach 2:
The system implements comprehensive feedback mechanisms including operation tracking, performance monitoring, and analytics that capture data from both human and robot operations. This feedback enables continuous optimization of welding sequences, adjustment of operational parameters, and improvement of overall system productivity while preserving adaptability.
2Adaptability or versatility
If human operators perform welding operations, then complex decision-making is possible, but reliability deteriorates due to fatigue and inconsistency
Solution Approach 1:
The system performs preliminary programming of welding sequences with pre-defined human and robot tasks. Complex decision-making logic is embedded in advance through programmed sequences, allowing robots to execute consistent, reliable operations while humans focus on higher-level judgment tasks such as sequence selection and anomaly resolution.
Solution Approach 2:
The control system acts as an intermediary that coordinates between human operators and robots. It manages task allocation, monitors performance, and ensures consistent execution of welding sequences while capturing and analyzing operational data to improve future performance and reliability.
3Productivity
If robotic systems perform welding operations, then productivity and consistency improve, but adaptability and ease of operation worsen due to programming complexity
Solution Approach 1:
Complex welding processes are broken down into manageable sequences with clearly defined tasks. Each sequence is programmed independently with specific human and robot actions, making the overall system easier to program and maintain while maintaining high productivity through automated execution.
Solution Approach 2:
The control system is designed to be universal, handling both simple and complex welding sequences through a unified programming framework. The system can accommodate various human-robot collaboration patterns and adapt to different welding applications, reducing programming complexity through standardized interfaces and reusable sequence templates.
4Productivity
If more robotic automation is implemented, then productivity improves, but device complexity and initial cost increase
Solution Approach 1:
The system implements automation at the sequence level rather than requiring complete system replacement. Individual welding sequences can be programmed and executed by robots with human collaboration, allowing progressive automation that improves productivity while managing complexity through modular implementation.
Solution Approach 2:
Comprehensive feedback and analytics capabilities are integrated to monitor system performance, track operations, and identify optimization opportunities. This data-driven approach enables continuous improvement of productivity while managing system complexity through informed decision-making about where to apply additional automation.
Data Source
AI summary
In some examples, a robotic welding-type system performs one or more robot operations, and/or accommodates human performance of one or more human operations, during execution of a robot instruction sequence of a part assembly process. Whether a particular operation of the part assembly process is performed by a robot or a human operator is recorded in a database, along with information pertaining to each operation. The record of which/what operations of the part assembly process and/or robot instruction sequence are performed by a human vs. a robot can be analyzed to identify potential issues and/or ways in which future executions of the part assembly process and/or robot welding sequence can be improved to mitigate, avoid, and/or otherwise address the issue(s).