A multi-machine cooperative control method and system based on negative entropy increase

Abstract

The invention discloses a multi-machine cooperative control method and system based on negative entropy increase. The transfer learning theory is used to study the dynamic evolution law of multi-dimensional quality state space distribution, and to dynamically predict the multi-dimensional quality state fluctuation law in the intelligent manufacturing process. The search can The influencing factors that make the entropy value of the community network of intelligent manufacturing smaller, and then reveal the law that can make the fluctuation of the quality state smaller, thus forming a method of increasing the negative entropy of the multi-dimensional quality state of the intelligent manufacturing process; and then introducing micro-evolutionary learning A multi-machine cooperative self-adaptive control method is constructed, the structure adjustment control is completed through the negentropy controller, and the process adjustment control is completed through the self-adaptive control, so as to realize the self-adaptive control of the multi-dimensional quality state of the intelligent manufacturing process in a dynamic cycle. The invention can be widely used in the field of product quality assurance of intelligent manufacturing.

Description

technical field [0001] The invention belongs to the technical field of product quality assurance in intelligent manufacturing, and relates to a multi-machine cooperative control method, system, storage medium and terminal equipment based on negative entropy increase. Background technique [0002] From the perspective of quality control of high-quality and high-precision products, with the background of high-end intelligent equipment manufacturing process, based on big data to explore the multi-dimensionality, time-varying and dynamic characteristics of its quality state fluctuations, around the complex intelligent manufacturing process, its The multi-dimensional coupled quality state control method is a research hotspot today, and the specific technical gaps are as follows: [0003] 1) Lack of quality state fluctuation and distribution analysis methods for multi-machine coupling in complex manufacturing processes [0004] For the analysis of the stability of the manufacturi...

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Problems solved by technology

[0003] 1) Lack of quality state fluctuation and distribution analysis methods for multi-machine coupling in complex manufacturing processes

[0004] For the analysis of the stability of the manufacturing process, there is not enough big data based on the time-varying multi-quality states of the manufacturing process, so it cannot reflect the dynamics of each quality state of complex products. The product quality assurance of the existing manufacturing process mainly emphasizes the high precision of manufacturing equipment , the research focuses on the quality fluctuation of the final result of a single process, which makes it difficult to meet the requirements of high reliability and high precision manufacturing under the existing manufacturing process level. The guarantee of the final quality of the product is related to the stability of the quality state of each process

Therefore, research is needed to solve the problem of insufficient stability of the quality state of the manufacturing process caused by multi-machine cooperation, strong coupling, dynamics, and high reliability in these complex manufacturing processes

[0005] 2) There is a lack of analysis methods for the influence of state fluctuations in complex manufacturing processes on product quality

[0007] 3) Lack of decoupling and adaptive quality control methods for multi-machine coupling quality states in complex manufacturing processes

[0008] The existing product quality assurance technology of complex manufacturing process lacks in-depth research on decoupling and adaptive control methods, cannot quantitatively analyze the automatic feedback control mechanism of the manufacturing process, and does not find the manufacturing process from the abnormal change distribution of the stable manufacturing process. The action rules of each manufacturing unit in the process make it impossible to establish an automatic feedback adjustment model for intelligent manufacturing processes based on self-organization theory, and cannot form an execution mechanism for automatic feedback adjustments driven by abnormal quality state fluctuations, and it is also impossible to realize intelligent manufacturing processes. Implementation of intelligent quality control provides assurance

[0009] 4) Lack of evaluation methods for quality state fluctuations in complex manufacturing processes

[0010] Insufficient evaluation of the quality fluctuation of multi-machine coupling in the manufacturing process is reflected in the diffusion of coupling under the complex working conditions of multi-machine collaboration in the manufacturing process, which may make it difficult to guarantee the quality of the final product

For high-precision product quality requirements, the existing quality fluctuation evaluation methods do not fully consider the timing correlation and strong coupling of the intelligent manufacturing process, resulting in the inability to dynamically evaluate the quality status of the manufacturing process

Therefore, it cannot meet the quality assurance requirements of intelligent manufacturing process and high-precision manufacturing equipment

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