3D Print Attribute Inference for Incomplete Object Model Data
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Solution Overview
Problem
Additive manufacturing techniques face challenges in generating three-dimensional objects with specified properties when the object model data is incomplete or insufficient, as it may lack information on intended properties such as color, strength, or resilience, especially when properties are specified for surfaces but not interiors.
Innovation Solution
A method is introduced where attributes are inferred based on user input, available data, and object generation apparatus information to derive object generation instructions, allowing for the selection of appropriate print materials and agents to achieve desired properties, even when the object model data is deficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If object model data is used to generate three-dimensional objects, then manufacturing efficiency is improved, but the quality and appearance of objects deteriorate when property information is incomplete
Solution Approach 1:
The system performs preliminary inference of object properties (color, strength, resilience) before the additive manufacturing process begins. By analyzing the object model data and automatically determining appropriate material properties and print parameters in advance, the system ensures both manufacturing efficiency and object quality without requiring complete property specifications in the input data.
2Device complexity
If properties are specified for surfaces only, then manufacturing complexity is reduced, but the reliability of object generation deteriorates due to insufficient interior property information
Solution Approach 1:
The system applies different property inference strategies to different regions of the object. When only surface properties are specified, the system infers appropriate interior properties by analyzing the object's geometry, function, and structural requirements. This allows the system to maintain manufacturing simplicity while ensuring reliable object generation by treating surface and interior regions with appropriate property specifications.
3Manufacturing precision
If attribute inference is performed to complete object model data, then manufacturing precision is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system performs self-service attribute inference by automatically analyzing the object model data and determining appropriate properties without requiring external intervention or complex manual specification. The inference system uses the available object data (geometry, intended function, surface properties) to autonomously complete the property specifications, thereby improving manufacturing precision while keeping the processing complexity manageable through automated decision-making algorithms.
Data Source
AI summary
In an example, a method includes receiving object model data describing at least a portion of an object to be generated by additive manufacturing. Object generation instructions for generating the object in its entirety may be derived based on the object model data. Where it is determined that the object model data comprises a data deficiency for deriving the object generation instructions, at least one attribute for the object may be inferred and object generation instructions may be derived based on the object model data and the inferred attribute.