Topology Optimization for Additive Manufacturing Residual Stress
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing CAD and CAE systems fail to accurately account for manufacturing-induced stresses in additive manufacturing, leading to subpar quality parts that cannot sustain service and life loads, as they ignore residual stresses and deformations introduced during the AM process.
Innovation Solution
A holistic topology optimization approach that includes manufacturing-induced states in the design process, using adjoint sensitivity equations to iteratively optimize design variables and recalculating their influence on the AM process, ensuring the part can withstand service and life loads by simulating the AM process and accounting for residual stresses and deformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing CAD and CAE systems are used for design optimization, then design complexity is reduced and manufacturing is simplified, but manufacturing-induced stresses and deformations are not accounted for, resulting in parts that cannot sustain service and life loads
Solution Approach 1:
The patent applies preliminary action by performing topology optimization that anticipates and accounts for manufacturing-induced stresses and deformations before the actual additive manufacturing process. The optimization algorithm pre-calculates the distribution of residual stresses and thermal deformations that will occur during AM, and uses this information to adjust the design geometry in advance. This allows the final part to withstand service loads despite the complex manufacturing process, resolving the contradiction between reliability and design complexity.
Solution Approach 2:
The patent segments the design optimization process into multiple stages: (1) initial geometry design, (2) simulation of AM process to calculate manufacturing-induced stresses and deformations, (3) topology optimization based on combined service and manufacturing loads, and (4) iterative refinement. This segmentation allows the complex problem of accounting for manufacturing effects to be broken down into manageable computational steps, improving reliability without overwhelming design complexity.
2Manufacturing precision
If topology optimization is performed without accounting for manufacturing-induced states, then computational time is reduced and design simplicity is maintained, but part quality is subpar and warping occurs
Solution Approach 1:
The patent implements feedback by using the results of AM process simulation (manufacturing-induced stresses and deformations) to feed back into the topology optimization algorithm. The optimization iteratively adjusts the design geometry based on feedback from simulated manufacturing outcomes, allowing the design to compensate for expected warping and dimensional inaccuracies. This feedback loop significantly improves manufacturing precision but increases computational time due to the iterative nature of the process.
Solution Approach 2:
The patent changes design parameters (geometry, topology, material distribution) based on simulated manufacturing-induced stresses and deformations. The optimization algorithm modifies structural parameters to compensate for expected manufacturing defects, thereby improving part quality. However, this parameter optimization requires extensive computational iterations, increasing the loss of time.
3Ease of manufacture
If residual stresses and deformations from AM process are ignored, then design process is simpler and faster, but post-processing activities increase and manufacturing costs rise
Solution Approach 1:
The patent applies preliminary anti-action by designing structures that pre-compensate for manufacturing-induced stresses and deformations. The topology optimization algorithm intentionally introduces counteracting geometric features that offset expected warping and residual stress effects during additive manufacturing. This preliminary counter-measure reduces or eliminates the need for post-processing activities such as heat treatment, machining, or rework, thereby improving manufacturing efficiency and reducing costs despite the more complex design process.
Data Source
AI summary
An example embodiment designs a real-world object by defining a first model of the object being produced using an additive manufacturing (AM) process, where behavior of the object being produced is given by a first equation which includes a first plurality of corresponding sensitivity equations for a first plurality of design variables. Similarly, such an embodiment defines a second model of the object after being produced, wherein behavior of the object after being produced is given by a second equation which includes a second plurality of corresponding sensitivity equations for a second plurality of design variables. In turn, the second model is iteratively optimized with respect to a given one of the second plurality of design variables using both the first plurality of corresponding sensitivity equations and the second plurality of corresponding sensitivity equations.