Surface Mounter Placement Optimization With Adaptive Tabu Search
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Solution Overview
Problem
Existing optimization methods for the placement process of surface mounters have not achieved joint optimization of all sub-problems, are ineffective in scenarios with limited feeders, and suffer from insufficient adaptivity, leading to excessive equivalent pickup operations, long pick-and-place paths, and prolonged total production time.
Innovation Solution
A method based on heuristic adaptive tabu search is employed to optimize the placement process of surface mounters. This involves encoding and decoding component allocation and pick-and-place path optimization problems using heuristic adaptive information link algorithms, and using tabu search to schedule low-level heuristic algorithms for joint optimization of sub-problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing optimization methods are used for the placement process, then the optimization can be performed, but the total production time is prolonged and production efficiency is reduced
Solution Approach 1:
The patent segments the placement process into multiple sub-problems including component allocation, nozzle row allocation, head sorting, and pick-and-place path optimization. Each sub-problem is optimized independently using specialized algorithms, and the results are integrated to achieve overall process optimization. This segmentation allows for targeted optimization of each aspect without being constrained by the limitations of existing holistic optimization methods.
Solution Approach 2:
The patent employs adaptive tabu search algorithms that dynamically adjust optimization parameters and strategies based on the specific production scenario. The optimization method adapts to different feeder configurations, component types, and placement requirements, transforming the static optimization approach into a dynamic one that can respond to varying production conditions and achieve better time efficiency.
2Adaptability or versatility
If existing optimization methods are used, then optimization can be performed, but adaptivity is insufficient leading to excessive equivalent pickup operations
Solution Approach 1:
The optimization method uses adaptive tabu search algorithms that dynamically adjust to different production scenarios, feeder configurations, and component characteristics. This dynamic adaptivity enables the system to automatically optimize for minimal equivalent pickup operations based on the specific situation, rather than relying on fixed optimization rules that lack flexibility.
Solution Approach 2:
The patent changes key parameters such as nozzle row allocation, head sorting sequences, and pick-and-place path parameters adaptively based on production requirements. By dynamically adjusting these parameters rather than using fixed values, the system achieves better adaptivity to different scenarios and reduces unnecessary pickup operations.
3Productivity
If existing optimization methods are used, then the placement process can be optimized, but the pick-and-place path length is excessive
Solution Approach 1:
The patent separates the path optimization into distinct phases: component allocation phase, nozzle row allocation phase, head sorting phase, and pick-and-place path optimization phase. This segmentation allows each phase to be optimized independently with appropriate algorithms, resulting in shorter overall path lengths compared to unsegmented optimization approaches.
Solution Approach 2:
The optimization method performs preliminary optimization of component allocation, nozzle row allocation, and head sorting before executing the actual pick-and-place path optimization. These preliminary actions prepare the system in advance with optimized configurations, enabling the final path optimization to achieve shorter path lengths without compromising other aspects of the process.
4Productivity
If existing optimization methods are used, then optimization can be performed, but joint optimization of all sub-problems is not achieved
Solution Approach 1:
The patent divides the complex optimization problem into manageable sub-problems (component allocation, nozzle row allocation, head sorting, path optimization) that can be solved using specialized algorithms for each. This segmentation reduces the overall complexity while achieving joint optimization, as each sub-problem is optimized independently and then integrated into a cohesive solution.
Solution Approach 2:
The system uses dynamic coordination between the different optimization modules through adaptive tabu search, which dynamically adjusts the interaction and integration of various sub-problem optimizations. This dynamic coordination enables joint optimization of all sub-problems while managing system complexity through intelligent integration rather than rigid coupling.
Data Source
AI summary
A method for optimizing the placement process of a surface mounter using a heuristic adaptive tabu search is presented, relevant to surface-mount technology. The method includes encoding and decoding heuristic adaptive information link, where encoded information cover component allocation sequence, head sequence, heuristic algorithm selection, and pick-and-place path optimization sequence. The decoded results configure the component allocation algorithm and pick-and-place path optimization algorithm, and the optimized placement process is derived using these configured algorithms. The component allocation algorithm includes both the available feeder-oriented heuristic algorithm and the assigned feeder group-oriented heuristic algorithm, suitable for different feeder scenarios. Optimizing the selection of these algorithms achieves adaptive optimization for various production scenarios. The tabu search algorithm conducts neighborhood search operations on the adaptive information link, addressing component allocation and pick-and-place path optimization simultaneously. This approach synergistically optimizes the number of equivalent pick-up operations and pick-and-place path length, significantly enhancing production efficiency.