Additive Manufacturing Parameter Screening for Efficient Print Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for optimizing process parameters in additive manufacturing are limited by their inability to systematically identify and optimize the large number of parameters affecting material properties, often requiring extensive resources and only considering a small number of parameters.
Innovation Solution
A method and system that includes a screening phase to determine significant process parameters and an optimization phase to find optimal values, using experimental designs such as Plackett-Burman and central composite designs to execute multiple print runs and measure output responses, thereby identifying and optimizing key parameters for target material properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a random parameter approach with various parameter values is used to optimize process parameters, then the effect of each parameter can be determined, but the number of experimental runs increases significantly and resource consumption increases
Solution Approach 1:
The optimization process is divided into two distinct phases: a screening phase that identifies significant parameters using a limited number of experimental runs, and an optimization phase that fine-tunes only those significant parameters. This segmentation reduces the total number of runs required compared to evaluating all parameters equally.
Solution Approach 2:
The method extracts and isolates only the significant parameters from the complete parameter set during the screening phase. By taking out only the relevant parameters for further optimization, the system avoids wasting resources on parameters that have minimal impact on the output responses.
2Manufacturing precision
If statistical design with response surface is employed to find optimal parameters, then optimization accuracy improves, but the number of experimental runs and resource requirements increase
Solution Approach 1:
The method segments the optimization process into screening and optimization phases, applying full statistical design with response surface methodology only to the significant parameters identified in the first phase. This selective application reduces the total number of experimental runs required compared to applying statistical design to all parameters.
Solution Approach 2:
The optimization effort is concentrated locally on the significant parameters that have the greatest impact on output responses, rather than distributing resources uniformly across all parameters. This local quality approach ensures high optimization accuracy for critical parameters while reducing overall resource consumption.
3Quantity of substance
If only a small number of process parameters are considered due to cost and time constraints, then resource requirements are reduced, but the ability to identify significant parameters is limited
Solution Approach 1:
The two-phase approach segments the parameter evaluation process, first using a limited number of runs to screen all parameters and identify significant ones, then applying more rigorous optimization only to those significant parameters. This allows comprehensive parameter assessment without proportionally increasing resource requirements.
Solution Approach 2:
The screening phase uses a minimal number of experimental runs (partial action) to identify significant parameters, rather than performing full optimization analysis on all parameters. This partial action is sufficient for the purpose of parameter selection and significantly reduces resource requirements.
Data Source
Figure 1~2
Figure 3~5
Figure 6
AI summary
A method is provided for determining optimal values of significant process parameters in an additive manufacturing (AM) process for printing a part from a specified process material. The method involves defining a set of target output material properties to be optimized and identifying an initial set of process parameters pertaining to the AM process. The method broadly comprises a screening phase and an optimization phase. The screening phase involves generating and executing a first experiment design, and determining, based on a first output response, a subset of significant process parameters that affect the target output material properties. The optimization phase involves generating and executing a second experiment design, and determining, based on a second output response, optimal values for the significant process parameters that maximize or minimize the target output material properties.