Multi-variable Design Optimization for Environmental Impact
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Solution Overview
Problem
Conventional life-cycle assessment (LCA) methods are cumbersome and time-consuming for product designers, requiring iterative configurations to reduce environmental impact, as it is not intuitive how to modify product attributes to achieve desired environmental outcomes, and often necessitate specialized expertise and significant resources.
Innovation Solution
A system and method that includes a design tool with modules for inventory creation, model creation, and optimization, allowing for the concurrent modeling and optimization of multiple variables to achieve target goals such as reduced environmental impact, using software or hardware configurations that can automate the design process for various types of products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional LCA methods are used to evaluate environmental impact, then comprehensive environmental assessment is achieved, but the process becomes time-consuming and laborious for designers
Solution Approach 1:
The system pre-calculates and stores environmental impact data for various materials and processes in databases before the design phase. During design iteration, designers can directly query pre-computed environmental impact values without performing full LCA calculations, significantly reducing evaluation time while maintaining comprehensive environmental assessment capability
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a bridge between design configurations and environmental impact results. This system automatically performs LCA calculations based on selected design parameters and returns optimized recommendations, eliminating the need for designers to manually iterate through multiple configurations
2Adaptability or versatility
If designers manually iterate multiple product configurations to reduce environmental impact, then potential design improvements are explored, but the process requires significant time and resources
Solution Approach 1:
The system enables self-service design optimization by automatically analyzing design configurations and generating optimized recommendations without requiring designer intervention. The computational system autonomously explores multiple design configurations, evaluates their environmental impacts, and presents optimized solutions, allowing designers to achieve comprehensive design exploration with minimal time investment
Solution Approach 2:
The patent systematically varies design parameters such as material composition, process selection, and product configuration to explore the design space. By automatically adjusting these parameters and evaluating their environmental impacts, the system efficiently identifies optimal design configurations without requiring manual iteration through all possible combinations
3Reliability
If generic design optimization techniques are applied, then design improvement is achieved, but specialized expertise and additional resources are required
Solution Approach 1:
The system is designed as a universal platform that integrates multiple functions including environmental impact assessment, design optimization, and recommendation generation into a single tool. This multi-functional system can be applied across different product types and design contexts without requiring specialized expertise, making advanced optimization techniques accessible to general-purpose product designers
Data Source
AI summary
In a computer-implemented method of designing at least one system to achieve a target goal, an inventory of a plurality of variables that affect the design of the at least one system is created, where the inventory includes a feasibility range for each of the plurality of variables contained in the inventory is created. In addition, initial values are assigned for the plurality of variables in the inventory and a model of input to output correlations of the plurality of variables is created by commissioning the plurality of variables from the initial values. Moreover, a design of the at least one system to achieve the target goal is optimized by manipulating one or more of the plurality of variables through application of the model.