Siemens Energy Global GmbH & Co. KG (20240354454). STOCHASTIC STRUCTURES simplified abstract
Contents
STOCHASTIC STRUCTURES
Organization Name
Siemens Energy Global GmbH & Co. KG
Inventor(s)
Jan-Hendrik Groth of Nottingham (GB)
Mirco Magnini of Nottingham (GB)
Christopher Tuck of Nottingham (GB)
STOCHASTIC STRUCTURES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240354454 titled 'STOCHASTIC STRUCTURES
The method described in the abstract involves creating a stochastic structure by selecting a parent structure with an array of unit cells, each defined by size, geometry, relative density, and nodes. Implicit functions of objects are placed at each node, with parameters for position and extent randomized using statistical distributions.
- Select a parent structure with an array of unit cells
- Define each unit cell with size, geometry, relative density, and nodes
- Randomize parameters of objects using statistical distributions
- Create a randomised array of unit cells representing the stochastic structure
- Render iso-surfaces from the array to form a solid model
- Optionally print the structure using additive manufacturing
Potential Applications: - Advanced manufacturing processes - Architectural design - Medical imaging and modeling - Scientific research in materials science - Gaming and animation for realistic environments
Problems Solved: - Efficient generation of complex stochastic structures - Accurate representation of random variations in 3D models - Streamlined additive manufacturing processes
Benefits: - Enhanced design flexibility - Improved accuracy in modeling and simulation - Cost-effective production of intricate structures
Commercial Applications: Title: Advanced Stochastic Structure Generation for Additive Manufacturing This technology can revolutionize the additive manufacturing industry by enabling the production of highly detailed and customized structures with minimal manual intervention. Industries such as aerospace, automotive, and healthcare could benefit from the precise and efficient manufacturing capabilities offered by this innovation.
Prior Art: To explore prior art related to stochastic structure generation and additive manufacturing processes, researchers can look into patents, academic papers, and industry publications focusing on 3D modeling, computational design, and material science.
Frequently Updated Research: Researchers in the fields of computational geometry, computer graphics, and additive manufacturing are continuously exploring new algorithms and techniques for enhancing stochastic structure generation and 3D printing processes. Stay updated on conferences, journals, and research collaborations in these areas to access the latest advancements in the field.
Questions about Stochastic Structure Generation for Additive Manufacturing:
1. How does randomizing parameters of unit cells contribute to the creation of complex structures? Randomizing parameters adds variability and realism to the structure, allowing for the generation of intricate and unique designs.
2. What are the potential challenges in implementing this method in large-scale manufacturing processes? Large-scale implementation may require optimization of computational resources and efficient handling of complex geometries to ensure cost-effective and timely production.
Original Abstract Submitted
a method of forming a stochastic structure, the method comprising the steps: selecting a parent structure, the parent structure defining an array of unit cells, initially the array of unit cells is uniform, defining each unit cell of the array of unit cells a size, a geometry, a relative density and at least one node, placing an implicit function of an object or part of an object at each node, wherein the object having parameters to define its position xc, yc, zc, and extent rx, ry, and rzor a shape defined by an equation having at least the parameters x, y and z, randomising any one or more of the parameters by applying a statistical distribution, the statistical distribution having a standard deviation �, the standard deviation � controls at least one of the three dimensions where random values of each parameter are created and selecting a value greater than zero of the standard deviation � to define the degree of randomisation for any one or more of the parameters, thereby creating a randomised array of unit cells representative of the stochastic structure wherein the randomised array of unit cells forms three-dimensional volume data comprising iso-surfaces, the method comprising rendering the iso-surfaces with polygons having faces and vertices to form a solid model of the stochastic structure and optionally printing the stochastic structure in an additive manufacturing process.