Iterative Packing Optimization for Irregular 3D Print Objects
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Solution Overview
Problem
Efficiently packing irregularly shaped objects into a defined space, such as a 3-D print bed, is challenging due to the limitations of human operators' creativity and memory, leading to suboptimal use of space and increased printing runs and delays.
Innovation Solution
An iterative method and system using morphological techniques and a genetic algorithm to precompute layouts, assign items to shapes, score configurations, and select the most efficient configuration for packing irregularly shaped items within a defined space, optimizing the use of space and automating the packing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human operators manually pack irregularly shaped objects into a defined space, then the packing process is simple to operate, but the space utilization efficiency is low
Solution Approach 1:
The system enables self-service automation where the computer automatically performs the packing optimization task that would otherwise require human operators. The genetic algorithm independently evaluates multiple configurations and selects the optimal layout without human intervention, resolving the contradiction by replacing manual operation with autonomous computational service.
Solution Approach 2:
The patent replaces the mechanical/manual packing process with a computational system using genetic algorithms. Instead of human operators physically arranging objects, a computer-based optimization system calculates and determines the optimal configuration, substituting mechanical human labor with computational intelligence to achieve higher space utilization.
2Stability of the object's composition
If human operators use memorized patterns to pack objects, then the operation is consistent and repeatable, but the packing efficiency is limited by operator creativity and memory
Solution Approach 1:
The system transitions from static, fixed packing patterns to dynamic, adaptive optimization. The genetic algorithm continuously evaluates and evolves packing configurations based on the specific characteristics of the objects and space, allowing the solution to adapt and improve rather than relying on predetermined static patterns.
Solution Approach 2:
The system changes the approach from using fixed operational parameters (memorized patterns) to dynamically optimized parameters. The genetic algorithm varies and adjusts packing configurations based on objective functions and constraints, transforming the packing problem from a static pattern-matching task to a dynamic optimization process that achieves superior space utilization.
3Reliability
If multiple printing runs are performed due to inefficient space usage, then all objects can be printed, but the total printing time and operational delays increase
Solution Approach 1:
The system performs preliminary optimization of the packing layout before the actual printing process. By pre-calculating the optimal configuration using genetic algorithms, the system ensures maximum space utilization in advance, preventing the need for multiple printing runs and thereby reducing total printing time while maintaining complete object production.
Solution Approach 2:
The system uses feedback from the optimization process to improve future packing decisions. The genetic algorithm evaluates configurations based on objective functions that consider printing efficiency, and this feedback mechanism allows the system to learn and improve packing strategies over time, reducing operational delays while ensuring all objects are printed.
4Quantity of substance
If a genetic algorithm is used to optimize packing layouts, then the space utilization efficiency improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the packing problem into manageable components that can be processed by the genetic algorithm. By dividing the complex optimization task into discrete evaluation steps and configuration assessments, the system makes the computational problem tractable while still achieving high space utilization through systematic optimization.
Data Source
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AI summary
An iterative method and system for performing the method are described that implement a technique to fit irregularly shaped items into a defined space. In particular, one example may take the form of a method including predetermining one or more layouts for a defined space. Each layout has a plurality of shapes. The method also includes receiving a set having a plurality of items and determining one or more configurations formed by assigning to each shape in the layout an item from the set. The items match the shapes to which they are assigned. Additionally, the method includes scoring each configuration and selecting one configuration based at least in part upon the scoring.