Pointwise Strain Superposition for Meso-Macro Scaling
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Solution Overview
Problem
Current meso-scale models for simulating manufacturing processes with moving heat sources, such as Powder Bed Fusion, face limitations due to high computational costs, restricting their scalability and effectiveness in predicting residual stresses and part distortions across larger scanning volumes.
Innovation Solution
A computer-implemented method that employs a Pointwise Strain Superposition (PSS) scaling procedure to link meso-scale and macro-scale models, calculating incompatible strains and initial states for macro-scale simulations, thereby reducing computational costs and enabling efficient prediction of residual stresses and distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If meso-scale models are used to evaluate thermal history and residual stress fields, then prediction accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent divides the computational domain into multiple sub-domains and applies localized meso-scale modeling only where needed, rather than applying it uniformly across the entire domain. This segmentation allows high accuracy in critical regions while reducing overall computational cost.
Solution Approach 2:
The patent applies meso-scale modeling partially - only in regions where high prediction accuracy is critical - rather than applying it excessively across the entire domain. This partial application maintains necessary accuracy while significantly reducing computational burden.
2Volume of moving object
If meso-scale models are applied to larger scanning volumes, then prediction scope is improved, but computational cost increases prohibitively
Solution Approach 1:
The patent segments the large scanning volume into smaller sub-regions, applying meso-scale modeling only to selected sub-regions where detailed analysis is required. This allows the model to handle larger overall volumes without prohibitively increasing computational cost.
Solution Approach 2:
The patent applies meso-scale modeling partially across the scanning volume - using it only where necessary for accurate prediction - enabling the analysis of larger volumes without the computational cost scaling linearly with volume size.
3Reliability
If full meso-scale simulations are performed for entire manufacturing processes, then prediction completeness is improved, but computational time increases
Solution Approach 1:
The patent segments the manufacturing process simulation into multiple stages, applying meso-scale modeling only to critical stages or regions. This segmentation maintains prediction completeness for essential aspects while reducing overall computational time.
Solution Approach 2:
The patent applies meso-scale modeling partially through the manufacturing process - focusing computational resources on the most critical phases - thereby achieving sufficient prediction completeness without the excessive computational time required for full-process meso-scale simulation.
Data Source
AI summary
A scaling method for simulating any manufacturing process employing a moving heat source is disclosed. The method is intended to melt or sinter a material, wherein the heat source is driven according to a defined path. The method requires a meso-scale model, which evaluates the physical quantities representative of the process-induced thermal history and residual stress and strain fields for each set of process parameters employed for the given material. The meso-scale results, obtained by modeling one or multiple scan lines, are transferred to the elements of the macro-scale finite element mesh based on the defined path. The scaling is performed pointwise and followed by an averaging operation on the values of the physical quantities computed inside each element of the macro-scale finite element mesh. Finally, a macro-scale simulation is executed for evaluating the residual stresses and distortions arising throughout the entire manufacturing process.