Additive Manufacturing Parameter Adjustment Using Standard Test Pieces
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Solution Overview
Problem
Additive manufacturing devices face challenges in achieving optimum operation conditions due to various factors such as raw material variations and environmental differences, leading to inconsistent performance and quality, despite attempts at big data analysis which are uncertain and unreliable.
Innovation Solution
A device adjustment instrument that selects standard test piece data and generates a modeling parameter set based on modeling specification data, using standard test piece data and test modeling result data to adjust the operation conditions of additive manufacturing devices, ensuring optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If big data analysis is used to determine operation conditions, then a wide range of factors can be considered, but the result becomes uncertain and unreliable due to too many intertwined factors
Solution Approach 1:
The patent segments the complex operation condition determination into two distinct phases: (1) a learning phase using big data analysis to build a predictive model that captures the relationships between multiple factors and operation results, and (2) a determination phase that uses this pre-built model to reliably determine optimal conditions. This segmentation isolates the complexity to the model-building stage while ensuring reliable execution during the determination stage.
Solution Approach 2:
The patent performs preliminary big data analysis and model construction before the actual operation condition determination. By pre-processing the data and establishing the predictive model in advance, the system eliminates the uncertainty that would arise from performing complex analysis during the determination phase, thereby ensuring reliable and consistent results.
2Reliability
If standard test pieces are used for device adjustment, then the adjustment process becomes efficient and reliable, but the applicability to diverse object specifications is limited
Solution Approach 1:
The patent changes the parameters of standard test pieces dynamically based on the target object's specifications. The learning device adjusts parameters such as test piece geometry, material properties, and processing conditions to match the characteristics of the object to be manufactured. This allows standard test pieces to maintain their simplicity and measurement reliability while adapting to diverse object specifications through parameter modification.
Data Source
AI summary
A device adjustment instrument is provided with a storage unit for storing standard test piece data corresponding to each of a plurality of standard test pieces which can be manufactured by additive manufacturing devices and a standard parameter set for when the standard test pieces are manufactured, a selection unit for selecting standard test piece data that match modeling specification data of a specified object from a plurality of standard test piece data on the basis of the modeling specification data, and an adjustment unit for generating a modeling parameter set for adjusting an operating condition of the additive manufacturing devices on the basis of the selected standard test piece data and the test modeling result data manufactured by the additive manufacturing devices using the standard parameter set corresponding to the standard test piece data.