Row-Column Grouping Genetic Algorithm for SMT Placement Path Optimization
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Solution Overview
Problem
Existing placement path optimization algorithms for surface mount machines lack a special design for component arrays, leading to poor search capabilities and lengthy processes, as they fail to ensure row-by-row mounting and optimize path length effectively due to a large search space.
Innovation Solution
A placement path optimization method based on a row-column grouping genetic algorithm that groups placement points into rows and columns, using genetic operations to efficiently determine a globally optimal placement sequence, ensuring row-by-row mounting and reducing the search space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general placement path optimization algorithm is used, then the algorithm can be applied to various circuit board layouts, but the search space becomes too large leading to poor search capabilities and lengthy optimization processes
Solution Approach 1:
The patent segments the circuit board into multiple rows based on the component array layout. Each row is processed independently to determine placement sequences, dividing the large-scale optimization problem into smaller sub-problems. This segmentation reduces the search space from considering all placement points globally to optimizing each row separately, thereby reducing computation time while maintaining adaptability to different circuit board layouts.
2Adaptability or versatility
If a general placement path optimization algorithm is used, then the algorithm can handle diverse component arrangements, but it fails to ensure row-by-row mounting which is required for component arrays
Solution Approach 1:
The patent performs preliminary row grouping and sequencing before the actual placement optimization. By pre-identifying rows and determining the optimal placement sequence for each row in advance, the algorithm ensures that row-by-row mounting is maintained throughout the placement process. This preliminary action guarantees mounting sequence accuracy for component arrays while still allowing flexibility in handling different circuit board layouts through adaptive row identification.
3Productivity
If the search space is reduced through row-column grouping, then the optimization process becomes faster, but the device complexity increases due to the need for grouping algorithms
Solution Approach 1:
The patent applies segmentation by dividing the placement problem into row-based sub-problems. The row-column grouping algorithm organizes placement points into structured rows and columns, creating a hierarchical problem structure. This segmentation enables faster optimization by solving smaller sub-problems independently, and the structured grouping provides a clear framework that manages algorithmic complexity through systematic organization rather than random search.
Solution Approach 2:
The patent changes the parameter representation from individual placement point coordinates to row-grouped sequences. By transforming the optimization parameters from x-y coordinates to row-indexed placement orders, the algorithm reduces the search space dimensionality. This parameter transformation simplifies the optimization problem while the row-column grouping structure provides an organized approach to managing the increased algorithmic complexity through systematic parameter organization.
Data Source
AI summary
The present disclosure relates to placement path optimization methods of a surface mount machine and in particular to a placement path optimization method of a surface mount machine based on row-column grouping genetic algorithm. The method includes the following steps: 1. obtaining production parameters and data; 2. grouping placement points into matrix rows based on rows; 3. encoding chromosomes to generate a chromosome population; 4. decoding chromosomes to obtain column grouping results for placement points and calculating placement path lengths; 5. setting parameters and loop conditions for the genetic algorithm; 6. updating optimization results based on crossover operators; 7. updating optimization results based on mutation operators; outputting the optimal placement path. This invention falls within the realm of electrical technology and electrical engineering.