Additive Material Composition for Region-Specific Part Properties
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Solution Overview
Problem
Developing or choosing complex or heterogeneous materials for additive manufacturing is a slow, time-consuming, and costly process requiring expert knowledge and intensive testing to achieve specific physical properties, such as thermal conductivity and melting point, especially when these properties are needed in different regions of a manufactured item.
Innovation Solution
A composition determination system trained using a database of known materials and machine learning techniques identifies optimal material compositions for additive manufacturing, which are then used to manufacture items with specific properties, and the system adjusts settings based on feedback from manufactured parts to improve future production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional iterative methods are used to develop complex materials, then material composition can be optimized to achieve specific physical properties, but the process becomes slow, time-consuming, and costly
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with extensive materials database information before actual manufacturing. The composition determination system is prepared in advance with knowledge of material properties, compositions, and relationships, enabling rapid prediction without iterative testing during production.
Solution Approach 2:
The patent replaces the traditional mechanical/physical iterative testing system with a computational machine learning system. Instead of physically testing material compositions through trial and error, the system uses trained models to predict optimal compositions based on input specifications, substituting computational analysis for physical experimentation.
2Manufacturing precision
If expert knowledge and intensive testing are used to achieve specific physical properties, then material performance can be optimized, but the process becomes cost intensive
Solution Approach 1:
The system enables self-service by allowing the composition determination system to automatically determine optimal material compositions without requiring expert human intervention. The machine learning model independently analyzes specifications, searches the materials database, and generates composition recommendations without manual testing or expert analysis.
Solution Approach 2:
The patent introduces an intermediary machine learning system that mediates between material specifications and composition determination. This intermediary layer processes the complex relationship between desired properties and material compositions, translating specifications into optimal formulations without direct human expertise or extensive testing.
3Adaptability or versatility
If heterogeneous material compositions are used to achieve different physical properties in different regions, then item performance can be optimized, but the process becomes even more time consuming and costly
Solution Approach 1:
The system applies local quality by determining different material compositions for different regions or constituent parts of an item based on specific local property requirements. The composition determination system can generate region-specific material formulations tailored to the unique performance needs of each part of the final product.
Solution Approach 2:
The system performs preliminary determination of multiple heterogeneous compositions simultaneously using the pre-trained machine learning model. Instead of iteratively developing each region's material separately through testing, the system predicts optimal compositions for multiple regions in parallel based on their respective specifications.
Data Source
AI summary
A method of additive manufacture of a constituent part of an item is described. The method comprises the following steps. A specification of characteristics is provided for the constituent part. A composition determination system is then used to identify a composition comprising a plurality of component materials predicted to comply with the specification of characteristics. The composition determination system is a system trained using a database of known materials. The constituent part of the item is then manufactured according to the composition identified by the composition determination system using an additive manufacturing system. Items may be manufactured with a plurality of constituent parts each manufactured this way. A method of establishing a computer-implemented composition determination system for manufacture of items and a control system for additive manufacture of an item using heterogeneous materials by an additive manufacturing system are also described.