Production Line Linkage Configuration With Multi-Level Iteration
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Solution Overview
Problem
Existing workshop production line design methods lack an effective linkage response mechanism, making it difficult to self-adaptively and quickly adjust configuration parameters, structure, and control schemes to meet individualized demands and optimize whole line integration.
Innovation Solution
A self-adaptive configuration method and system that performs multi-level iterative optimization on the production line linkage design framework, utilizing digital twin technology, industrial control networks, and decoupling algorithms to adjust and optimize device control, motion, and logistics, enabling quick design and adjustment of production lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static configuration design method is used, then device control and integration can be implemented, but the workshop cannot quickly adjust to individualized demands and product changes
Solution Approach 1:
The patent segments the production line configuration into multiple independent modules (construction type, motion type, control type, optimization type) that can be independently configured and adjusted. Each module represents a discrete functional unit that can be modified without affecting the entire system, enabling quick adaptation to individualized demands while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent transforms the static configuration design into a dynamic system through multi-level iterative optimization. The configuration parameters, structure, and control schemes can automatically adjust and evolve through iterative optimization processes, allowing the workshop to respond dynamically to product changes and individualized demands rather than requiring fixed pre-planning for every scenario.
2Productivity
If multi-level iterative optimization is implemented, then self-adaptive quick adjustment and design can be achieved, but the system complexity increases
Solution Approach 1:
The multi-level iterative optimization is segmented into four distinct configuration types (construction, motion, control, optimization), each handled by specialized algorithms and processes. This segmentation allows the complex optimization task to be divided into manageable sub-tasks that can be processed independently and iteratively, improving design efficiency while preventing the system from becoming an unmanageable monolith.
Solution Approach 2:
The patent performs preliminary configuration and optimization iterations in the virtual simulation system before finalizing the actual production line setup. By conducting multi-level iterative optimization in advance within the digital twin environment, the system prepares optimal configurations beforehand, reducing on-site adjustment time and improving overall design efficiency while containing system complexity within the virtual planning phase.
3Reliability
If digital twin technology is used for real-time communication, then motion synchronization is achieved, but information processing requirements increase
Solution Approach 1:
The patent creates digital twin copies of physical devices that mirror their real-world counterparts in the virtual simulation system. These digital copies enable real-time communication and motion synchronization without requiring excessive information processing of the actual physical systems. The digital twins handle the computational burden of real-time data exchange and synchronization, allowing reliable coordinated operation while managing information processing requirements through virtual representation rather than direct processing of all physical system data.
Data Source
AI summary
Disclosed are a self-adaptive configuration method and system for linkage response of a construction type, a motion type, a control type and an optimization type. The disclosure aims to provide the self-adaptive configuration method and system for linkage response of quick adjustment and design of a workshop production line. The self-adaptive configuration method comprises the following steps of step A: construction type configuration; step B: motion type design; step C: control type design; and step D: optimization type evolution, wherein the step D comprises first-level iterative optimization, second-level iterative optimization and third-level iterative optimization. A closed optimization cycle is formed by the first-level iterative optimization, the second-level iterative optimization and the third-level iterative optimization jointly, and the multi-level iterative optimization is performed on the production line linkage design framework, so that the workshop production line can be self-adaptively and quickly adjusted and designed.