Beam-Type Single-Gantry SMT Placement Head Task Assignment Optimization
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Solution Overview
Problem
Current optimization methods for beam-type single-gantry surface mounters are inefficient due to lack of effective research frameworks, leading to high optimization times and unstable results, as they fail to consider nozzle replacement and simultaneous component pickup effectively.
Innovation Solution
A comprehensive optimization approach that includes a new research framework for placement heads tasks assignment, utilizing expert knowledge to minimize nozzle replacements and maximize simultaneous pickups by optimizing nozzle assignment and component allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing optimization methods are used for beam-type single-gantry surface mounters, then the optimization process can be completed, but the optimization time is excessively long and the results are unstable
Solution Approach 1:
The patent divides the placement heads tasks assignment problem into two independent sub-problems: nozzle assignment problem and component allocation problem. The nozzle assignment problem determines which nozzle type is installed on each placement head, while the component allocation problem determines which component type each placement head picks up. This segmentation allows each sub-problem to be solved separately with dedicated optimization strategies, reducing overall computation time and improving result stability.
Solution Approach 2:
The patent performs preliminary classification of components based on their pickup characteristics before the main optimization process. Components are categorized into different types (e.g., single pickup, simultaneous pickup) based on their physical properties and placement requirements. This preliminary action reduces the complexity of the subsequent optimization by pre-organizing the search space and providing initial constraints for the optimization algorithms.
2Productivity
If nozzle replacement and simultaneous pickup are not effectively considered, then the optimization framework is simpler, but production efficiency is significantly reduced
Solution Approach 1:
The patent introduces dynamic nozzle assignment where nozzle types can be changed between different pick-and-place cycles. The system determines the optimal nozzle type for each placement head in each cycle based on the component allocation for that cycle. This dynamic adjustment allows the system to adapt to different component types and maximize simultaneous pickup opportunities, significantly improving production efficiency.
Solution Approach 2:
The patent merges multiple operations into simultaneous pickup operations where multiple placement heads pick up different component types at the same time. By coordinating the movements and tasks of multiple placement heads, the system performs multiple pickups in parallel, reducing the total cycle time and improving overall productivity without requiring complex additional hardware.
3Productivity
If comprehensive optimization considering all factors is implemented, then production efficiency improves by up to 20.9%, but the optimization framework becomes significantly more complex
Solution Approach 1:
The patent segments the comprehensive optimization problem into distinct modules: nozzle assignment module, component allocation module, and evaluation module. Each module handles specific aspects of the optimization independently. The nozzle assignment module focuses on selecting optimal nozzle types, the component allocation module focuses on assigning components to placement heads, and the evaluation module assesses the overall solution quality. This modular segmentation makes the complex framework more manageable and implementable.
Solution Approach 2:
The patent implements a feedback mechanism where the optimization results are evaluated based on multiple criteria including total pickup time, nozzle replacement frequency, and simultaneous pickup utilization. The evaluation feedback guides the iterative optimization process, allowing the system to adjust and improve solutions over multiple cycles. This feedback-driven approach ensures that the comprehensive optimization achieves measurable productivity improvements while keeping the framework structured and controllable.
Data Source
AI summary
An optimization approach for placement heads tasks assignment of beam-type single-gantry surface mounter is disclosed, building a comprehensive framework that simultaneously achieves all optimization objectives in reasonably short time. Specific steps are as follows: (a) importing user-defined production parameters and forming raw production data, (b) pre-processing the raw production data and obtaining the intermediate production data that facilitates the subsequent processing, (c) verifying the loop condition of the “placement heads tasks assignment”, (d) performing nozzle assignment, (e) confirming all the nozzle assignment exchange possibilities, (f) verifying whether the loop condition of “component allocation” are fulfilled, (g) performing component allocation, and (h) outputting the optimal placement heads tasks assignment results.