Guangdong University of Technology

Other

Jiewu Leng

Qiang Liu

Duxi Yan

Xin Chen

Ding Zhang

Hao Zhang

Lijun Wei

Man Zhou

Guolei Ruan

Caiyu Xu

Hu Zhang

Jiongyu Chen

Yuxuan Xiao

Qianyi Su

Alan Zhou

Yougui Yang

Dewen Wang

Kuanyuan Li

Shide Ye

Yuan Song

Weinan Sha

Jianbo Jing

Guixiang Lin

Xiaofeng Zhu

Hongye Su

Zisheng Lin

Wenshun Deng

A system and method for monitoring the failure of assembly tooling for a mass-individualized production line are provided. The system realizes real-time monitoring and prediction of the remaining service life of the assembly tooling in the mass-individualized production line through the cooperation of a manufacturing execution system (MES), a supervisory control and data acquisition (SCADA) system, an assembly tooling failure prediction system, a controller network, and an assembly line. When the remaining service life reaches a certain threshold, the system sends an early warning to an operator, and provides decision support for the operator to replace the assembly tooling. The assembly tooling failure prediction system is built with an assembly tooling failure prediction model for predicting the remaining service life of the assembly tooling in real time, which avoids the influence of human factors and greatly improves the prediction accuracy.

System and method for monitoring failure of assembly tooling for mass- individualized production line

The disclosure discloses an inhomogeneous sample equalization method and system for a product assembly process. The method includes the following steps of: A: calculating a similarity among different samples; B: constructing a fuzzy compatibility matrix S for representing the similarity among all the samples, and constructing a fuzzy compatibility space X with different granule layers through the fuzzy compatibility matrix S; C: based on a granular calculating mode, screening out a granule layer with a maximum comprehensive value of an information increment and the similarity among the samples from the fuzzy compatible space X to serve as an optimal granule layer; and D: carrying out equalization processing on the sample of the optimal granule layer.

Inhomogeneous sample equalization method and system for product assembly process

A method for constructing and training a Decentralized Migration Diagram Neutral Network (DMDNN) model for a production process, including: determining a production task input management node, distributed management nodes and a granularity of each of the distributed management nodes, and constructing a production system network; constructing network calculation nodes on each of the distributed management nodes; constructing and training a DMDNN model; and applying the trained DMDNN model in management and control of the production process.

Method for constructing and training decentralized migration diagram neural network model for production process

Disclosed is a blockchain-enabled edge computing method for production scheduling. The method includes modeling a smart contract between a device and a manufacturing unit, and using the smart contract to perform production scheduling on the device inside the manufacturing unit; one of the manufacturing units includes multiple devices; mounting each device on the blockchain operating node, the MES issues production instructions to the nodes of each manufacturing unit, at the same time, the nodes acquire production data of the device through multiple data sources of the device, the operating state data and process parameter data of each device are acquired in real time, and the data is directly chained from the device level; according to the production instructions and device parameters obtained by the manufacturing unit, using edge computing to dynamically adjust the device load, efficiency, and utilization.

Blockchain-enabled edge computing method for production scheduling

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.

Self-adaptive configuration method and system for linkage response of construction type, motion type, control type and optimization type

Disclosed is an approximate physical simulation integrated debugging method based on digital twinning, which includes the following steps of: a production line simulation model building step, a twinning step, a system debugging step, a hardware-in-the-loop debugging step, and a device-in-the-loop debugging step. The approximate physical simulation integrated debugging system based on digital twinning includes a production line simulation model building module, a twinning module, a system debugging module, a hardware-in-the-loop debugging module, and a device-in-the-loop debugging module. According to the approximate physical simulation integrated debugging method and system based on digital twinning, since a local component twin after being successfully debugged is replaced with an entity component, when a problem occurs after replacement, only the local component twin needs to be debugged and optimized again, thus saving time and reducing cost.

Approximate physical simulation integrated debugging method and system based on digital twinning

A generalization and encapsulation method based on a digital twin (DT) model of a workshop includes: classifying a device in a production line according to a basic operation and a functional characteristic of a process of the device; abstracting a commonality in terms of process action mode, process algorithm and action trigger mechanism; encapsulating according to a sequence characteristic of a process; comparing processes, and generalizing and encapsulating; encapsulating according to a time sequence, a space sequence and a logic characteristic of a specific process; storing a generalized and encapsulated process in a database; and calling the generalized and encapsulated process from the database to a device or a process. The generalization and encapsulation system includes an abstract process encapsulation module, a continuous process encapsulation module, a process action encapsulation module, a process algorithm encapsulation module, a database and a fast calling module.

Generalization and encapsulation method and system based on digital twin model of workshop

A method for designing a production line based on digital twin (DT), includes: determining a layout strategy of a production line; customizing a DT model of production device based on a pre-built universal DT model; allowing the production device to interact with a virtual model in real time; simultaneously testing and debugging a configuration of the production line; analyzing a test and debug result to derive a defect of the production line; modifying a virtual dynamic model; repeating until an optimal result is generated through optimization and designing the product line according to the optimal result. Based on the realization of virtual and real linkage, the present disclosure performs joint debugging on a physical entity and a virtual model in the production line so as to comprehensively consider uncertainty factors of the device and better guide the modification of the simulation model according to the test result.

Method for designing production line based on digital twin

A parallel control method for an intelligent workshop is provided, comprising the following steps: step A: constructing a parallel control simulation platform; step B: establishing a parallel execution mechanism; and, step C: correcting and optimizing a parallel control system. A parallel control system for an intelligent workshop is provided, comprising: an MES module configured to issue production instructions to unit management modules; the unit management modules configured to convert the received production instructions into machine instructions and synchronously issue the machine instructions to underlying PLCs by a bus control network module, and drive the parallel control simulation platform and a field device to move by a soft PLC and a hard PLC; the bus control network module configured to establish a communication network among the MES module, an SCADA module, an industrial personal computer, physical devices and a whole-line simulation model; and, the SCADA module.

Parallel control method and system for intelligent workshop

The present invention relates to the field of intelligent machining, in particular to a parallel control method based on multi-period differential sampling and digital twinning technologies, the method comprising the following steps of: a. detecting machining conditions of dotting machine equipment by using a multi-period differential sampling technology; b. establishing a digital twinning control model; and c. controlling a simulation model of the dotting machine equipment according to a detection judgment result so as to perform parallel control on the dotting machine equipment. According to the parallel control method based on multi-period differential sampling and digital twinning modelling provided by the present invention, for the digital twinning model of the dotting machine equipment, the parallel control method establishes a simulation model and a detection model of the dotting machine equipment by using a virtual-real synchronization technology; simulation dotting machine equipment operates in synchronization with the physical dotting machine equipment.

Parallel control method based on multi-period differential sampling and digital twinning technologies

The present invention relates to the technical field of industrial automation, and in particular to a method and system for quick customized-design of an intelligent workshop. The method comprises the following steps: step A: acquiring design requirement information of a production line, and performing modeling in a simulation system according to the design requirement information; step B: performing action planning of a physical stand-alone device, performing logistics and motion planning of articles being processed, and compiling motion and action control scripts; step C: establishing, by the digital twin technology, a communication channel among a PLC system of the workshop digitization model, a PLC system of a physical workshop device and a host computer; and, step D: outputting a three-dimensional digital twin model as a blueprint for follow-up design and development of the stand-alone device, a control system and an execution system.

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Guangzhou, China

Jiewu Leng