AI Assembly Instruction Regeneration for Flexible Error Recovery
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Solution Overview
Problem
Existing flexible assembly systems are limited by their reliance on predefined expert knowledge, making them inflexible and requiring manual intervention for error handling, which reduces assembly efficiency.
Innovation Solution
The proposed solution involves using a unimodal model to generate machine instructions from process text, allowing for real-time adaptation and error handling without manual intervention. When errors occur, the system determines remaining text based on error messages and regenerates machine instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If expert systems with predefined rules are used, then assembly process can be automated, but flexibility and adaptability to personalized demands are reduced
Solution Approach 1:
The patent replaces traditional expert systems with a large language model (LLM)-based control system. The LLM processes natural language assembly instructions and generates adaptive machine code, substituting the rigid mechanical reasoning of expert systems with flexible neural network-based inference. This enables the system to handle diverse assembly tasks without predefined rules while maintaining automation.
Solution Approach 2:
The system dynamically changes the control parameters by generating different machine instructions based on the specific assembly task requirements. The LLM adapts its output based on input instructions, allowing the same automated system to handle varying assembly operations with different parameters, thereby achieving both automation and flexibility.
2Device complexity
If manual intervention is used for error handling, then system complexity is reduced, but assembly efficiency is significantly reduced
Solution Approach 1:
The patent implements self-service error handling where the LLM automatically detects assembly errors through exception capturing modules and regens appropriate machine instructions without human intervention. The system monitors execution, identifies anomalies, and autonomously corrects them by regenerating instructions, thereby maintaining high assembly efficiency while managing complexity through intelligent automation.
Solution Approach 2:
The system incorporates feedback mechanisms where exception information from the assembly execution is fed back to the LLM. The LLM uses this feedback to identify errors and regenerate corrected instructions, creating a closed-loop system that continuously monitors and corrects itself, eliminating the need for manual error handling and maintaining high productivity.
3Stability of the object's composition
If predefined expert knowledge is used, then assembly process is standardized, but adaptability to diverse production orders and small batches is prevented
Solution Approach 1:
The patent creates a universal control system based on LLMs that can handle multiple assembly tasks and product types through a single unified architecture. The system processes diverse assembly instructions in natural language and generates appropriate machine code, making it multi-functional and adaptable to various production orders without requiring separate specialized systems for each task.
Solution Approach 2:
The system transitions from static predefined rules to dynamic adaptive reasoning. The LLM continuously learns and adapts to new assembly requirements in real-time, allowing the standardized system to dynamically adjust its behavior for diverse production orders and small batch customized products while maintaining overall process stability.
Data Source
AI summary
An assembly method includes acquiring process text, inputting the process text into a unimodal model, and generating machine instructions, where the machine instructions are represented in computer language; controlling an assembly device to execute the machine instructions; in response to receiving a first error message sent by the assembly device, determining the remaining text based on the first error message, using the remaining text as new process text, and returning to the step of inputting the process text into the unimodal model and generating the machine instructions; and in response to receiving no first error message sent by the assembly device, completing assembly.