AR Disassembly Guidance Using Multimodal LLM Image Analysis
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Solution Overview
Problem
Existing disassembly methods face challenges in efficiently guiding new employees through the disassembly process due to the lack of prior knowledge and the need for manual adaptation for each product, often relying on hard-coded AR implementations and lacking CAD models for automated sequence generation.
Innovation Solution
A method utilizing an image recording apparatus, multimodal Large Language Model (LLM), and AR visualization to automatically generate and visualize disassembly steps, allowing for adaptive disassembly assistance without manual setup for each product type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hard-coded AR implementation is used for disassembly guidance, then the disassembly process can be guided through AR technology, but adjusting the implementation for a new product is very time-consuming
Solution Approach 1:
The system automatically generates disassembly sequences and AR visualizations by analyzing product images and using LLMs, eliminating the need for manual hard-coding for each new product. The system serves itself by autonomously creating the guidance content without requiring human intervention for setup.
Solution Approach 2:
The system changes from a static hard-coded implementation to a dynamic generated implementation. By using image recognition and LLMs to generate disassembly sequences on-demand, the system adapts to different products by changing its operational parameters rather than requiring structural modifications.
2Extent of automation
If automatic generation of disassembly sequences is used, then the process can be automated, but it depends heavily on prior information such as CAD model which is generally not available
Solution Approach 1:
The system extracts necessary information directly from product images rather than requiring external CAD models. By taking out the dependency on prior information and obtaining data directly from the product itself through image capture, the system achieves automation without complex prerequisites.
Solution Approach 2:
The system introduces an intermediary layer consisting of image recognition and LLMs that bridges the gap between raw product images and disassembly sequences. This intermediary processes the images and generates the necessary disassembly information without requiring direct access to CAD models or prior product knowledge.
3Loss of information
If text and image documentation is used for repair instructions, then repair information can be provided, but searching and following the steps is quite cumbersome
Solution Approach 1:
The system transitions from two-dimensional static documentation to three-dimensional interactive AR visualization. By overlaying disassembly instructions and visual cues directly onto the physical product in the user's field of view, the system makes following repair steps intuitive and natural rather than requiring users to cross-reference separate documents.
Solution Approach 2:
The system replaces the mechanical process of searching through and referencing physical or digital documentation with an automated AR-based guidance system. The AR system actively presents the relevant information in context, eliminating the manual searching and navigation required by traditional documentation methods.
Data Source
AI summary
Various teachings of the present disclosure include disassembly assistance methods. An example includes: visually capturing a product to be disassembled; generating image data for the part; requesting a prompt for a multimodal LLM; transmitting the image data to the LLM module and generating the prompt; generating an output with a disassembly step using the multimodal LLM; transmitting the disassembly data to a visualization module; generating visualization data correlating with the disassembly data using the visualization module; transmitting the visualization data to an image-generating device; generating an image based on the visualization data; displaying the generated image using the image-generating device; detecting a trigger signal corresponding with completion of the disassembly step; and repeating a-k until an abort criterion is reached.