Genetic Algorithm Planning for PCB Component Retrieval and Placement

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Solution Overview

Problem

Existing automatic insertion machines for printed circuit boards require manual planning of component retrieval and placement sequences, which are time-consuming and inefficient, and do not easily adapt to changes in board design.

Innovation Solution

An optimization system using a genetic algorithm to determine optimal component retrieval and placement sequences and configurations based on user-defined constraints, incorporating a penalty mechanism and simulation to predict cycle times.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If experts manually plan the component retrieval and placement sequence, then the placement configuration can be optimized, but the planning process is time-consuming and inefficient

Engineering Contradiction:

Improveplacement configuration optimizationVSAvoidplanning time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent replaces the manual expert planning process with an automated computer-based optimization system that uses genetic algorithms to determine component retrieval sequences and placement configurations, eliminating the time-consuming manual iteration and simulation process

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The optimization system automatically performs the planning task without requiring expert intervention, using algorithms to self-determine the optimal retrieval sequences and placement configurations based on the given parameters and constraints

Inventive Principle:

Principle #25Self-service

2Productivity

If experts manually plan and simulate multiple configurations, then the optimal placement sequence can be found, but the process requires considerable efforts and is not adaptable to design changes

Engineering Contradiction:

Improveinsertion cycle time optimizationVSAvoidplanning process complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces the complex manual planning and simulation process with an automated genetic algorithm-based optimization system that efficiently searches for optimal solutions without requiring multiple rounds of expert simulation and evaluation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs preliminary automated optimization calculations before actual production, using genetic algorithms to pre-determine the optimal retrieval sequences and placement configurations, so that when board designs change, the system can quickly recalculate without requiring extensive manual re-planning

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If a single pick-up mechanism handles all components, then the system is simpler, but it can only handle the same type of components in each complete insertion process

Engineering Contradiction:

Improvecomponent type handling capabilityVSAvoidpick-up mechanism configuration

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies multi-functionality by enabling pick-up mechanisms to handle different types of components through automated sequence optimization, where the same physical mechanism can be programmed to retrieve different component types in different optimal sequences based on the specific board design and component layout

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250292863A1Optimization system for process of component retrieval and placement and method therefor

Publication Date: 2025.09.18 DELTA ELECTRONICS INC(CN)

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, and processes the calculated solutions through the optimization calculation module to output multiple final parameters. The optimization calculation module 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.