Multivariate Stereolithography Testing for Material Development
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Solution Overview
Problem
Current additive manufacturing methods lack an efficient and resource-effective way to test and develop novel material chemistries for stereolithography, requiring extensive testing and material volumes, which is costly and time-consuming.
Innovation Solution
A method involving a systematic approach to generate and photocure test builds with variable test regions, allowing for the rapid identification of target device settings and material parameters through multivariate testing, using a stereolithographic additive manufacturing device capable of varying exposure energy profiles across different areas of a test build.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional additive manufacturing testing methods are used, then material development can be achieved, but extensive testing and large material volumes are required, making it costly and time-consuming
Solution Approach 1:
The test build is divided into multiple test regions, each with different variable values (e.g., exposure energy, layer thickness). This segmentation allows simultaneous testing of multiple material parameter combinations in a single build, dramatically reducing the number of builds required and accelerating material development while minimizing time loss.
Solution Approach 2:
The patent introduces a spatial dimension to testing by creating test regions at different locations within a single test build. Each region can have different variable values, transforming the testing approach from sequential (one build per test) to parallel (multiple tests in one build), thereby improving productivity and reducing development time.
2Productivity
If traditional additive manufacturing testing methods are used, then material development can be achieved, but extensive material volumes are required, making it costly
Solution Approach 1:
The test build is divided into multiple test regions, each with different variable values (e.g., exposure energy, layer thickness). This segmentation allows simultaneous testing of multiple material parameter combinations in a single build, dramatically reducing the number of builds required and accelerating material development while minimizing time loss.
Solution Approach 2:
Multiple test conditions and variable combinations are merged into a single test build. Instead of creating separate builds for each test condition, the patent combines them spatially within one build, reducing the total material volume required while improving development efficiency.
3Productivity
If multivariate testing with variable test regions is implemented, then rapid identification of device settings is achieved, but the device must vary exposure energy profiles across different areas
Solution Approach 1:
The additive manufacturing device dynamically adjusts exposure energy profiles for different regions during the photocuring process. This dynamic capability allows the device to implement multivariate testing by varying parameters across test regions, enabling rapid identification of optimal device settings while managing the complexity through software-controlled parameter variation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the rapid and systematic identification of target device settings for additive manufacturing, reducing material usage and development time, while ensuring the material meets design goals and regulatory standards.
Implementation Method 1
photocuring a first test build based on the first test file
Data Source
AI summary
A method includes: accessing a first selection of a first test variable; based on the selection, photocuring a first test build by varying a value of the first test variable over a first set of test regions; accessing a first set of measurements of the first test build; calculating a target range of the first test variable based on the first set of measurements; accessing a second selection of a second test variable; based on the second selection, photocuring a second test build by varying a value of the second test variable over a second set of test regions while maintaining a target value of the first test variable within the target range of the first test variable; accessing a second set of measurements of the second test build; and calculating a second target range of the second test variable based on the second set of measurements.