VR-Based Object Design Ranking for Constraint-Aware Optimization
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Solution Overview
Problem
Existing object design systems fail to account for new design criteria and design constraints of previous designs, leading to inefficiencies such as increased maintenance time and downtime, as these constraints are often not communicated or easily accessible to new designers.
Innovation Solution
A system and method that utilizes a learned design VR database and simulation engine to simulate and rank candidate designs based on optimization criteria, identifying optimized designs that avoid previous design constraints, thereby reducing maintenance time and downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual design processes are used without access to previous design constraints, then design creativity and flexibility are maintained, but maintenance time and downtime increase due to repeated design errors
Solution Approach 1:
The system performs preliminary analysis of previous design constraints and stores them in a database before new design work begins. This allows new designers to access learned constraints upfront, preventing repeated errors and reducing maintenance time without requiring complex real-time analysis during the design process itself
Solution Approach 2:
A learned design virtual reality database acts as an intermediary between historical design data and new design processes. The database stores and retrieves design constraints, serving as a mediator that transfers knowledge without requiring direct access to original design teams or documentation, thus simplifying the interface while capturing complex constraints
2Productivity
If design constraints from previous designs are stored and accessed, then maintenance time is reduced, but information retrieval complexity increases
Solution Approach 1:
The system replaces manual information retrieval methods (physical documentation, human expertise queries) with an automated computer-based database system. This substitution makes design constraints easily searchable and accessible through digital interfaces, improving productivity while managing information retrieval complexity through automated indexing and retrieval algorithms
3Reliability
If simulation and optimization processes are applied to candidate designs, then design quality improves, but computational time and resources increase
Solution Approach 1:
The system applies simulation and optimization processes selectively to candidate designs rather than all possible designs. By filtering candidate designs based on learned constraints first, then applying comprehensive simulation only to the most promising candidates, the system achieves high design quality while managing computational time through partial application of rigorous analysis
Solution Approach 2:
The system performs preliminary filtering of candidate designs using learned design constraints before conducting full simulation and optimization. This preliminary action eliminates obviously inferior designs early, allowing comprehensive simulation to be applied to fewer candidates, thus maintaining high design quality while reducing overall computational time and resources
Data Source
AI summary
In some examples, design parameter data for an object can be received based on user input. A set of design criteria for the object can be received based on the design parameter data. A search of a learned design virtual reality (VR) database can be implemented to identify a set of candidate designs for the object based on the set of design criteria. The learned design VR database can include a plurality of previously determined designs for the object. Each candidate design for the object can be simulated in a simulation environment based on a learned design simulation database and optimization criteria to identify at least one new design for the object. A ranked design list can be generated ranking each candidate design and the at least one new design for the object based on ranking criteria.