Smart Segmentation for Additive Manufacturing Parameter Optimization
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Solution Overview
Problem
Conventional additive manufacturing processes require iterative and time-consuming parameter set adjustments for complex part geometries, leading to high resource consumption and material waste, as they lack efficient methods for optimizing build parameters across different geometric regions.
Innovation Solution
A system and method utilizing an iterative learning control process and smart segmentation to generate optimized parameter sets for each layer of a part, leveraging sensor data and noise reduction techniques to automate the optimization of build parameters, allowing for faster and more accurate determination of optimal parameters for various regions within a part.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional iterative parameter adjustment methods are used for complex part geometries, then parameter optimization can be achieved, but the process becomes extremely time-consuming and resource-intensive
Solution Approach 1:
The patent divides the build volume into multiple segments based on geometric features and thermal characteristics. Each segment is assigned specific parameter sets, allowing parallel optimization of multiple regions simultaneously. This segmentation approach breaks down the complex global optimization problem into manageable local problems, dramatically reducing the iterative cycle time while maintaining optimization quality.
Solution Approach 2:
The system performs preliminary thermal analysis and geometric segmentation before the actual additive manufacturing process. Build parameters are pre-calculated and assigned to different segments based on predicted thermal behavior and geometric complexity. This preliminary action eliminates the need for time-consuming iterative adjustments during production, as parameters are optimized in advance.
2Manufacturing precision
If iterative trial parameter sets are used to meet manufacturing requirements, then part quality can be improved, but material consumption and resource usage increase significantly
Solution Approach 1:
By segmenting the build volume into regions with similar thermal and geometric characteristics, the system can apply appropriate parameter sets to each segment from the first build attempt. This prevents material waste that would otherwise occur during iterative trial builds, as each segment is pre-optimized for its specific requirements.
Solution Approach 2:
The system dynamically adjusts build parameters (laser power, scan speed, hatch spacing) based on segment-specific thermal analysis and geometric features. By changing parameters appropriately for each segment before manufacturing, the system achieves high part quality without requiring multiple trial builds that would consume additional material.
3Manufacturing precision
If multiple parameter sets are used for different geometric regions, then part quality improves, but the complexity of the build plan generation increases
Solution Approach 1:
The build volume is automatically segmented into regions based on geometric features and thermal characteristics using algorithmic analysis. This automated segmentation process manages the complexity of generating build plans with multiple parameter sets by providing clear, objective criteria for region division, making the overall process more systematic and less complex than manual approaches.
Solution Approach 2:
The system performs self-analysis of the part geometry and thermal behavior to automatically generate appropriate parameter sets for different segments. This self-service capability reduces the need for manual intervention and complex external tooling, simplifying the build plan generation process while maintaining high part quality through region-specific parameter optimization.
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 significantly reduces the cycle time for parameter development, saves resources, and enhances the quality of the final build by enabling rapid optimization of process parameters for complex geometries, resulting in higher quality parts with reduced material waste and improved efficiency.
Implementation Method 1
The metal powder on the build plate is fused into a solid part by melting it locally using the focused laser beam
Implementation Method 2
The laser solidifies the powdered material by sintering or melting the powdered material
Implementation Method 3
generating a parameter set for each layer that forms the part, via execution of an iterative learning control process for each layer
Implementation Method 4
applying a noise reduction process to the raw power data
Data Source
AI summary
According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a platform comprising a segmentation module and a processor, a defined geometry for one or more geometric structures forming one or more parts, wherein the parts are manufactured with an additive manufacturing machine; generating a build file including an initial parameter set to fabricate each part; fabricating the part based on the build file; receiving sensor data for the fabricated part; generating a parameter set for each layer that forms the part, via execution of an iterative learning control process for each layer; generating raw power data for each layer that forms the part, using the processor, based on the generated parameter set; applying a noise reduction process to the raw power data; and generating a segmented build file, using the segmentation module, via application of the noise reduction process on the raw power data. Numerous other aspects are provided.