Scan Parameter Dictionary for Additive Manufacturing Coupon Builds
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Solution Overview
Problem
Conventional additive manufacturing processes, such as direct metal laser sintering, require multiple iterations and laborious testing to optimize part quality and production rate, leading to increased time and cost due to the complex relationship between build parameters and part quality.
Innovation Solution
A system and method utilizing an iterative learning control process to generate a dictionary of optimized scan parameter sets for additive manufacturing, which maps parameters to feature sets of geometric structures, allowing for the fabrication of complex parts with improved quality and reduced development time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional additive manufacturing processes are used with trial build plans, then part quality can be assessed through experimental testing, but the process requires multiple iterations leading to increased time and cost
Solution Approach 1:
The system performs preliminary analysis of the build plan and part geometry to predict potential quality issues before actual manufacturing. By pre-calculating optimal parameters and identifying critical features, the system reduces the need for multiple trial builds and iterative testing, thereby decreasing development time while maintaining part quality assessment capability
Solution Approach 2:
The system creates virtual models and simulations of the additive manufacturing process to predict part quality outcomes. By using digital twins and computational models to replicate the manufacturing process, the system can assess part quality without requiring physical trial builds, significantly reducing time and material consumption while maintaining accurate quality evaluation
2Manufacturing precision
If multiple trial parts are fabricated for testing, then acceptable build parameters can be determined, but material consumption and production costs increase significantly
Solution Approach 1:
The system uses virtual simulations and digital models to test build parameters and predict part quality outcomes. By performing experiments in the virtual domain rather than physically fabricating multiple trial parts, the system determines optimal build parameters while minimizing material consumption to only what is necessary for validation builds
Solution Approach 2:
The system replaces physical trial-and-error experimentation with computational analysis and predictive modeling. By using algorithms to calculate optimal parameters and simulate manufacturing outcomes, the system eliminates the need for multiple physical trial parts, thereby reducing material consumption while achieving the same build parameter optimization
3Manufacturing precision
If conventional iterative testing is performed, then part quality requirements can be met, but the complex relationship between build parameters and part quality requires extensive experimentation
Solution Approach 1:
The system creates computational models that replicate the complex relationships between build parameters and part quality. By using virtual simulations to represent the manufacturing process, the system can analyze parameter interactions without requiring extensive physical experimentation, simplifying the handling of complexity while maintaining accurate quality prediction
Solution Approach 2:
The system replaces complex physical experimentation with computational analysis. By using algorithms and simulation software to model parameter relationships, the system can systematically explore the parameter space and identify optimal settings without the complexity of managing multiple physical trial builds and their associated measurements
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
This approach compresses the material timeline development, reduces material debits, and enables the production of higher-quality parts with reduced iterations, as it generates tailored optimal scan parameter sets for building complex geometries without creating seams, thus enhancing the efficiency and accuracy of the additive manufacturing process.
Implementation Method 1
The AMM may form the object by solidifying successive layers of material one on top of the other on a build plate. Some AM systems use a laser (or similar energy source) and a series of lenses and mirrors to direct the laser over a powdered material in a pattern provided by a digital model
Implementation Method 2
The laser solidifies the powdered material by sintering or melting the powdered material
Implementation Method 3
The laser solidifies the powdered material by sintering or melting the powdered material
Data Source
AI summary
According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a part parameter dictionary module comprising a processor, geometry data for a plurality of geometric structures forming a plurality of parts, wherein the parts are manufactured with an additive manufacturing machine; determining, using the processor of the part parameter dictionary module, a feature set for each geometric structure; generating, using the processor of the part parameter dictionary module, one of a coupon and a coupon set for the feature set; generating an optimized parameter set for each coupon, using the processor of the part parameter dictionary module, via execution of an iterative learning control process for each coupon; mapping, using the processor of the part parameter dictionary module, one or more parameters of the optimized parameter set to one or more features of the feature set; and generating a dictionary of optimized scan parameter sets to fabricate geometric structures with a material used in additive manufacturing. Numerous other aspects are provided.