Vehicle Assembly Process Sequencing From Historical Workstation Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Designing a manufacturing process for vehicles is time-consuming due to the complexity of allocating parts and steps across multiple workstations, with unclear resource allocation for new or existing processes, especially when precedence and clearance issues are involved.
Innovation Solution
A method that predicts the sequence of manufacturing process segments by retrieving historical process segments from a database, determining similarity values using data analysis models, and generating a target process sequence with a sequence inference model to classify and allocate process steps and workstations effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If engineers manually determine resource allocation for manufacturing processes, then expertise-based decisions can be made, but the process becomes time-consuming
Solution Approach 1:
The system creates surrogate process segments by copying and adapting historical process segments from a database. Instead of manually designing new processes, the system retrieves historical segments that match the target plan and modifies them, significantly reducing design time while maintaining quality through proven historical patterns
Solution Approach 2:
The system performs preliminary actions by pre-storing historical process segments in a database with their metadata. When a new process needs to be designed, the system can quickly retrieve and adapt these pre-prepared segments rather than creating everything from scratch, reducing the time required for process design
2Productivity
If historical process segments are retrieved and adapted, then resource allocation can be optimized, but determining similarity and selecting appropriate segments increases system complexity
Solution Approach 1:
The system segments the complex task of process design into manageable components: retrieving historical segments, analyzing their similarity to the target plan using metadata and natural language processing, and selecting the most appropriate segments. This segmentation makes the overall complex system more manageable and controllable
Solution Approach 2:
The system introduces metadata as an intermediary layer between historical process segments and the target plan. By comparing metadata (process steps, components, workstations) and using natural language processing on textual descriptions, the system simplifies the similarity determination process without requiring direct complex analysis of entire process segments
3Adaptability or versatility
If process segments are divided across multiple workstations, then manufacturing flexibility is improved, but resource allocation becomes uncertain and more difficult to manage
Solution Approach 1:
The system creates a universal framework for resource allocation that works across multiple workstations and different process segments. By using standardized metadata schemas and a unified similarity analysis approach, the system can consistently allocate resources across diverse manufacturing scenarios, reducing uncertainty while maintaining flexibility
Data Source
AI summary
A method of predicting sequence for a manufacturing process to assemble a product based on a target plan for the manufacturing process includes retrieving, from a historical process database, a plurality of surrogate process segments based on the target plan, determining, for each surrogate process segment from the plurality of surrogate process segments, a segment similarity value based on a data analysis model, and generating a target process defining a process sequence for performing the manufacturing process based on the segment similarity values of the plurality of surrogate process segments and a sequence inference model that classifies the process segment of the target plan to sequences defined by the plurality of surrogate process segments. The target process includes data that defines a plurality of selected process steps and one or more workstations for the process segment of the target plan.