Genetic Packing Algorithm for Additive Manufacturing Build Volumes
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Solution Overview
Problem
Current additive manufacturing techniques face challenges in efficiently packing 3D objects within a build volume to maximize packing density, which is computationally intensive and often results in exponentially growing solution spaces, making it difficult to optimize object placement effectively.
Innovation Solution
The method employs a genetic procedure to classify and pack objects within a build volume by determining subsets of objects based on criteria such as geometrical features and priority, using a genetic algorithm to iteratively evaluate, select, and mutate object positions and orientations to achieve optimal packing density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional packing optimization methods are used to maximize packing density, then manufacturing efficiency is improved, but computational complexity grows exponentially
Solution Approach 1:
The build volume is divided into multiple build volumes portions, and objects are classified into different groups based on geometric features and priority criteria. Genetic algorithms are applied separately to each portion and group, breaking down the exponentially complex global optimization problem into multiple manageable sub-problems that can be solved independently and efficiently.
Solution Approach 2:
Objects are pre-classified into different groups based on geometric features and priority before the packing optimization process. This preliminary classification allows the genetic algorithm to handle similar objects together, reducing the search space and computational complexity while maintaining packing efficiency.
2Productivity
If more objects are packed into the build volume to increase productivity, then manufacturing output is improved, but packing density optimization becomes more difficult
Solution Approach 1:
The build volume is segmented into multiple portions, allowing independent optimization of each portion. This enables handling of larger numbers of objects by distributing them across multiple portions, each optimized separately, thereby maintaining optimization precision while increasing overall productivity.
Solution Approach 2:
The patent applies genetic algorithms to optimize packing in portions of the build volume rather than attempting to optimize the entire build volume at once. This partial action approach makes the optimization process more manageable and scalable to larger numbers of objects.
Data Source
AI summary
Examples of methods are described herein. In some examples, a method includes determining a first portion within a build volume. In some examples, the method includes packing first objects in the first portion. In some examples, the method includes packing using a genetic procedure. In some examples, the method includes packing, using the genetic procedure, second objects in the build volume.