Multi-Agent Materials Design for Integrated Manufacturing Routes
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Solution Overview
Problem
Conventional automated materials design techniques fail to integrate material composition and manufacturing processing steps, neglect multiple manufacturing routes, and overlook manufacturability, ESG norms, and cost considerations, leading to inefficient and resource-intensive material development processes.
Innovation Solution
A multi-agent reinforcement learning (RL) system is employed to automate material composition and manufacturing process design, integrating RL agents for composition selection and sequential process steps, with reward functions incorporating ESG norms, cost, and manufacturability to optimize material properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional automated materials design techniques are used, then material composition and properties can be correlated, but the entire manufacturing process chain is not considered leading to incomplete design
Solution Approach 1:
The patent segments the materials design process into distinct components: composition design, process parameter optimization, and property prediction. Each component is modeled separately using machine learning algorithms, allowing for specialized optimization of each aspect while maintaining overall system integration through the common objective of achieving target material properties.
Solution Approach 2:
The patent merges composition design and process parameter optimization into a unified automated design framework. By integrating these previously separate design activities into a single system that simultaneously optimizes both composition and processing parameters, the patent achieves comprehensive process chain consideration that neither approach could accomplish alone.
2Manufacturing precision
If extensive experimental trials are conducted, then accurate material properties can be achieved, but time and resource requirements increase significantly
Solution Approach 1:
The patent performs preliminary computational optimization of composition and process parameters using machine learning models before actual manufacturing or experimentation. This preliminary action identifies optimal design candidates in silico, significantly reducing the number of physical experimental trials needed to achieve target material properties, thereby saving time and resources.
Solution Approach 2:
The patent creates computational models that replicate the complex relationships between composition, processing parameters, and material properties. These virtual models serve as copies of the physical manufacturing process, allowing for extensive testing and optimization in the digital domain without consuming physical materials or time, while still achieving accurate property predictions.
3Adaptability or versatility
If multiple manufacturing process routes are available, then design flexibility increases, but selecting the optimal route becomes more complex
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning models evaluate each manufacturing route based on its ability to achieve target properties, cost considerations, and process feasibility. This feedback guides the selection and optimization of the most appropriate route, simplifying the decision-making process while maintaining access to multiple options and their respective advantages.
4Ease of operation
If conventional design methods are used, then experienced designers can guide the process, but the process remains costly and resource-intensive
Solution Approach 1:
The patent enables the design system to perform self-optimization through machine learning algorithms that automatically analyze data, identify patterns, and determine optimal composition and processing parameters without requiring extensive human intervention. This self-service capability reduces reliance on manual trial-and-error approaches while minimizing physical resource consumption by performing computations digitally.
Data Source
AI summary
The disclosure relates generally to methods and systems for automated design of materials and the manufacturing process for desired properties. Conventional automated materials design techniques do not perform an integrated design of (i) a material composition and (ii) their manufacturing processing steps. The present disclosure addresses this gap by using a multi-agent setup for automated design, wherein a distinct Reinforcement learning (RL) agent is used to mirror the composition selection (CS) and various sequential manufacturing process steps (PS) involved in its manufacturing route. The distinct RL agents learn from both past design data and computational models (empirical/analytical/physics-based models) representing the design process. The present disclosure also integrates other important parameters such as manufacturability, ESG norms, cost, process energy etc. and their relative importance into the design decision making process of the RL agents by expressing them as reward components upon which the RL agents are trained.