Surface Mount Pick-and-Place Path Optimization During Production
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Solution Overview
Problem
Existing surface mount machines face inefficiencies due to poor optimal solution search capability and inability to perform online optimization of the pick-and-place path, leading to prolonged production times.
Innovation Solution
A method for performing online optimization of the pick-and-place path using tabu search and dynamic programming to continuously improve placement point distribution and sequence, allowing for real-time updates and optimizations during production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional heuristic algorithms are used for pick-and-place path optimization, then initial optimization results can be obtained, but the optimal solution search capability is poor and online optimization cannot be performed
Solution Approach 1:
The patent implements dynamic optimization by introducing a dual-mode system that switches between initial optimization mode (using heuristic algorithms) and online optimization mode (using tabu search). The system continuously monitors production progress and performs real-time path re-optimization, transforming the static optimization approach into a dynamic one that adapts during production to minimize the pick-and-place path length and improve productivity.
Solution Approach 2:
The patent establishes a feedback mechanism where the system monitors production progress, evaluates current path efficiency, and triggers online optimization when improvement opportunities are detected. The optimization results are fed back into the production system, creating a closed-loop control that continuously improves the pick-and-place path based on actual production conditions, thereby reducing path length and increasing productivity.
2Productivity
If fixed optimization results are used during production, then production can proceed steadily, but online optimization and updates cannot be performed
Solution Approach 1:
The system implements dynamic adaptability by designing a dual-mode optimization framework that can switch between fixed execution mode and online re-optimization mode. The tabu search algorithm enables the system to adapt to changing production conditions by performing real-time path optimization, while the overall system maintains stability through structured mode transitions. This resolves the contradiction between steady production and online optimization capability.
Solution Approach 2:
The system performs self-optimization through automated tabu search algorithms that evaluate current production status and independently generate improved path solutions without external intervention. The system monitors its own performance metrics and triggers optimization cycles autonomously, enabling online updates while maintaining steady production flow, thus achieving both productivity and adaptability.
3Manufacturing precision
If component distribution and feeder positioning are fixed before production, then initial optimization can be completed, but further optimization during production is limited
Solution Approach 1:
The patent segments the optimization process into distinct phases: initial optimization (determining component distribution and feeder positioning) and online optimization (refining placement sequences and paths). By separating these functions and applying different algorithms to each phase (heuristic algorithms for initial setup, tabu search for online refinement), the system achieves both precise placement point distribution and continuous productivity improvement during production.
Solution Approach 2:
The system performs preliminary optimization of component distribution and feeder positioning before production begins, establishing a solid foundation for subsequent online optimization. This preliminary action uses heuristic algorithms to quickly determine initial placement sequences, which then serve as the basis for more refined tabu search optimization during production, enabling both precise initial setup and continuous improvement.
Data Source
AI summary
There is provided a method of performing online optimization on a pick-mount path during production of a surface mount machine and a pick-mount path optimization method of a surface mount machine is provided. In order to address the problem of too long pick-mount path resulting from poor optimal solution search capability when the existing surface mount machine performs optimization based on heuristic algorithm by calling software, the method includes: based on a mount optimization solver of the surface mount machine, obtaining an initial mount process optimization result, and transmitting the result into an industrial control computer of the surface mount machine to start surface mount production; based on the tabu search, performing comparison on the pick-mount path optimization results to select an optimal one and transmitting the newly-obtained optimal result into the industrial control computer of the surface mount machine, and then by starting from a next circuit board.