Probabilistic Fatigue Assessment for Additive Manufacturing Defects
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Solution Overview
Problem
Current methods for assessing the fatigue strength of component parts, particularly in the aerospace sector, face challenges due to the variability and low reproducibility of Additive Manufacturing (AM) methods, leading to difficulties in meeting strict reliability requirements and standardization, especially when manufacturing defects are present.
Innovation Solution
A computer-implemented method and system for probabilistic fatigue assessment of component parts, utilizing finite element analysis data, distinguishes between surface and internal regions, assigns defect distributions, calculates critical defect dimensions, and determines reliability using statistical models like the Weakest-Link approach and Kitagawa diagrams, enabling a more accurate and less conservative evaluation of fatigue strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional deterministic methods are used for fatigue assessment, then the assessment is simple and deterministic, but the results are overly conservative and do not account for material variability and manufacturing defects
Solution Approach 1:
The patent transforms the deterministic fatigue assessment into a probabilistic approach by changing the parameters from fixed values to probability distributions. Material properties, defect characteristics, and loading conditions are represented as random variables with specific distributions, allowing the assessment to account for variability while maintaining a systematic evaluation framework
Solution Approach 2:
The patent creates a virtual replica of the physical component through finite element modeling, where the geometry, material properties, and defects are copied into a computational model. This virtual model allows for repeated probabilistic simulations without damaging the actual component, enabling comprehensive fatigue assessment
2Reliability
If probabilistic methods are used to account for material variability and defects, then the assessment reliability improves, but the calculation time increases
Solution Approach 1:
The patent performs preliminary finite element analysis to determine stress and strain fields before the probabilistic fatigue assessment. By pre-calculating the mechanical fields and storing them for subsequent probabilistic evaluations, the method avoids repeated computationally expensive stress analyses during the probabilistic simulation phase
Solution Approach 2:
The patent segments the component into finite elements and further divides surface elements into sub-elements for defect placement. This segmentation allows the probabilistic assessment to focus computational resources on critical regions where defects are most likely to initiate fatigue cracks, rather than uniformly processing the entire component
3Reliability
If strict reliability requirements are applied to AM components, then safety is ensured, but the adoption of AM technology is hindered due to excessive conservativity
Solution Approach 1:
The patent applies different quality requirements and defect distributions to different regions of the component based on their functional importance and stress states. Critical regions with high stress concentrations receive more stringent defect acceptance criteria, while non-critical regions allow for larger defect tolerances, creating a differentiated quality approach that is both safe and practical for AM manufacturing
Data Source
AI summary
A computer-implemented method for the probabilistic assessment of fatigue of component parts in the presence of manufacturing defects comprises the following steps: importing predefined input data concerning at least one part of a component to be assessed; distinguishing a surface region of the part from an internal region; determining the volume and stress/deformation applied to the surface region and to the internal region; assigning the distribution of defects to a group of elements defined by the user; determining the maximum dimension of the defect in each surface or internal region considered; determining the critical dimension of the defect in each surface region or internal region by considering the fatigue strength of the material at the number of cycles under consideration, the stress/deformation applied and the position of each surface or internal region; calculating the reliability of at least one of the surface or internal regions considered.