2D Irregular Part Packing With Expanded-Area Overlap Optimization
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Solution Overview
Problem
Traditional methods for packing two-dimensional parts fail to account for complex interactions between parts, irregular shapes, and lack a dynamic iterative mechanism, leading to inefficiencies and suboptimal material utilization, especially with large numbers of parts.
Innovation Solution
A method involving expanded areas overlap optimization and dynamic iteration, using an improved genetic algorithm to encode, mutate, and decode pattern positions, optimizing the packing process by focusing on the overlap rate of expanded areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional geometric alignment methods are used for packing, then the method is simple and easy to implement, but packing efficiency and material utilization deteriorate when dealing with complex shapes and large quantities of parts
Solution Approach 1:
The patent replaces traditional mechanical geometric alignment methods with a genetic algorithm-based computational system. The genetic algorithm automatically evolves optimal packing arrangements through selection, crossover, and mutation operations, substituting manual geometric reasoning with automated evolutionary computation to handle complex shapes and large quantities of parts efficiently
Solution Approach 2:
The packing system performs self-optimization through the genetic algorithm mechanism. The algorithm automatically adjusts part arrangements, rotations, and orientations without external intervention, allowing the system to self-evolve toward optimal packing solutions that maximize material utilization while handling complex geometries
2Loss of time
If traditional packing methods are used, then the process is fast and simple, but it fails to account for complex interactions between parts, minimum gaps, and irregular shapes leading to suboptimal material utilization
Solution Approach 1:
The patent performs preliminary actions by pre-processing the part data to extract geometric features, calculate bounding boxes, and prepare optimization parameters before running the genetic algorithm. This preliminary preparation enables the algorithm to efficiently evaluate complex interactions between parts, minimum gap constraints, and irregular shapes during the optimization process, achieving both speed and optimal material utilization
3Quantity of substance
If existing algorithms are used for packing, then they can handle basic packing tasks, but they become inefficient when dealing with a large number of parts and cannot adaptively adjust strategies dynamically
Solution Approach 1:
The patent implements dynamic adaptability through the genetic algorithm's evolutionary process, which dynamically adjusts packing strategies based on the population's performance. The algorithm adapts to varying numbers of parts and complex geometries by evolving solutions through selection pressure, maintaining efficiency even as the quantity of parts increases
Solution Approach 2:
The genetic algorithm incorporates feedback mechanisms where the fitness function continuously evaluates packing quality based on material utilization, gap constraints, and part interactions. This feedback drives the evolutionary process to adaptively adjust arrangements, ensuring efficient handling of large numbers of parts while maintaining optimal solutions
Data Source
AI summary
A method for efficient packing of two-dimensional irregular patterns via expanded areas overlap optimization and dynamic iteration is provided. This method focuses on optimizing layout efficiency and improving material utilization when arranging irregular two-dimensional parts on plates. By obtaining binarized images from the AutoCAD drawings, sorting pattern images by size, and performing a series of expansions, an improved genetic algorithm is used to optimize the positioning of pattern images on the plate. The optimization target is to maximize the overlap rate between the expanded area from the second expansion of the current pattern image and the expanded area of the plate image, as well as the already arranged pattern images.