PCB Component Placement Sequence Optimization Under Machine Constraints
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Solution Overview
Problem
Existing automatic insertion machines for printed circuit boards require manual expert planning for component retrieval and placement sequences, which is time-consuming and inefficient, as they do not account for machine and environmental constraints, leading to suboptimal production processes.
Innovation Solution
An optimization system utilizing a genetic algorithm to determine optimal component retrieval and placement sequences based on user-defined constraints, incorporating a penalty mechanism and simulation to predict cycle times, while allowing for customization and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual expert planning is used for component retrieval and placement sequences, then the process can be completed with existing machines, but the planning time and cycle time are excessive
Solution Approach 1:
The system enables self-service by automatically generating optimized retrieval and placement sequences through the genetic algorithm, eliminating the need for manual expert planning. The algorithm independently evaluates multiple sequences and selects the optimal one based on cycle time and constraint satisfaction, making the system self-sufficient in the planning task.
Solution Approach 2:
The patent replaces the mechanical expert planning process with an automated computational system. The genetic algorithm substitutes human experts' manual sequence planning with computer-based optimization, using digital computation instead of human cognitive processes to determine the optimal component retrieval and placement sequences.
2Productivity
If manual expert planning is used for component retrieval and placement sequences, then the process can be completed with existing machines, but the cycle time and production efficiency are suboptimal
Solution Approach 1:
The system enables self-service by automatically generating optimized retrieval and placement sequences through the genetic algorithm, eliminating the need for manual expert planning. The algorithm independently evaluates multiple sequences and selects the optimal one based on cycle time and constraint satisfaction, making the system self-sufficient in the planning task.
Solution Approach 2:
The patent replaces the mechanical expert planning process with an automated computational system. The genetic algorithm substitutes human experts' manual sequence planning with computer-based optimization, using digital computation instead of human cognitive processes to determine the optimal component retrieval and placement sequences.
3Adaptability or versatility
If expert planning is performed for each new PCB type, then the planning adapts to specific board requirements, but the re-planning process is time-consuming and repetitive
Solution Approach 1:
The system performs preliminary action by pre-defining the genetic algorithm's evaluation criteria and constraint rules that work across different PCB types. When a new PCB type is introduced, the algorithm quickly adapts by evaluating sequences against the pre-established constraints, eliminating the need for experts to re-plan from scratch and enabling rapid adaptation to new board designs.
Solution Approach 2:
The patent applies parameter changes by allowing the genetic algorithm to adjust retrieval and placement sequences based on varying PCB parameters such as component locations, board dimensions, and constraints. The system maintains adaptability by changing the input parameters for each PCB type while using the same optimization framework, enabling versatile handling of different board designs without re-planning the entire system.
4Manufacturing precision
If multiple simulations are conducted to find the best configuration, then the optimal performance can be identified, but the computational effort and time required are considerable
Solution Approach 1:
The genetic algorithm implements continuity of useful action by maintaining an evolving population of candidate sequences that continuously improve through iterative selection, crossover, and mutation. Instead of conducting discrete simulations to evaluate isolated sequences, the algorithm continuously evolves the population toward optimal solutions, with each generation building upon previous results, thereby reducing total evaluation time while maintaining high optimization accuracy.
Solution Approach 2:
The patent applies feedback by using the fitness evaluation function to provide continuous feedback to the genetic algorithm during the optimization process. The fitness score, calculated based on cycle time and constraint satisfaction, guides the selection and evolution of sequences, allowing the system to efficiently converge on optimal solutions with fewer simulations compared to exhaustive search methods.
Data Source
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AI summary
An optimization system for component retrieval and placement and a method therefor are provided. The optimization system processes multiple parameters inputted by a user through an optimization calculation module (11), and processes the calculated solutions through the optimization calculation module (11) to output multiple final parameters. The optimization calculation module (11) utilizes a genetic algorithm and integrates a precision-designed penalty mechanism, allowing the user to only provide specific constraints and retrieval components that need to be optimized, to ensure that the optimal machine layout as well as the retrieval and placement sequence are found under the given constraints.