An Intelligent Quality Control Method for Manufacturing Processes Based on a Human–Cyber–Physical Knowledge Graph

Quality management is a constant and significant concern in enterprises.Effective determination of correct solutions for comprehensive problems helps avoid increased backtesting costs.This study proposes an intelligent quality control method for manufacturing processes based on a human–cyber–physical(HCP)knowledge graph,which is a systematic method that encompasses the following elements:data management and classification based on HCP ternary data,HCP ontology construction,knowledge extraction for constructing an HCP knowledge graph,and comprehensive application of quality control based on HCP knowledge.The proposed method implements case retrieval,automatic analysis,and assisted decision making based on an HCP knowledge graph,enabling quality monitoring,inspection,diagnosis,and maintenance strategies for quality control.In practical applications,the proposed modular and hierarchical HCP ontology exhibits significant superiority in terms of shareability and reusability of the acquired knowledge.Moreover,the HCP knowledge graph deeply integrates the provided HCP data and effectively supports comprehensive decision making.The proposed method was implemented in cases involving an automotive production line and a gear manufacturing process,and the effectiveness of the method was verified by the application system deployed.Furthermore,the proposed method can be extended to other manufacturing process quality control tasks.

Construction of Human Digital Twin Model Based on Multimodal Data and Its Application in Locomotion Mode Identifcation

With the increasing attention to the state and role of people in intelligent manufacturing, there is a strong demand for human-cyber-physical systems (HCPS) that focus on human-robot interaction. The existing intelligent manufacturing system cannot satisfy efcient human-robot collaborative work. However, unlike machines equipped with sensors, human characteristic information is difcult to be perceived and digitized instantly. In view of the high complexity and uncertainty of the human body, this paper proposes a framework for building a human digital twin (HDT) model based on multimodal data and expounds on the key technologies. Data acquisition system is built to dynamically acquire and update the body state data and physiological data of the human body and realize the digital expression of multi-source heterogeneous human body information. A bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) based network is devised to fuse multimodal human data and extract the spatiotemporal features, and the human locomotion mode identifcation is taken as an application case. A series of optimization experiments are carried out to improve the performance of the proposed BiLSTM-CNN-based network model. The proposed model is compared with traditional locomotion mode identifcation models. The experimental results proved the superiority of the HDT framework for human locomotion mode identifcation.

Human Cyber Physical Systems(HCPSs)in the Context of New-Generation Intelligent Manufacturing

An intelligent manufacturing system is a composite intelligent system comprising humans,cyber systems,and physical systems with the aim of achieving specific manufacturing goals at an optimized level.This kind of intelligent system is called a human-cyber-physical system(HCPS).In terms of technology,HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing.It can be concluded that the essence of intelligent manufacturing is to design,construct,and apply HCPSs in various cases and at different levels.With advances in information technology,intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing,and is evolving toward new-generation intelligent manufacturing(NGIM).NGIM is characterized by the in-depth integration of new-generation artificial intelligence(AI)technology(i.e.,enabling technology)with advanced manufacturing technology(i.e.,root technology);it is the core driving force of the new industrial revolution.In this study,the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs,and the implications,characteristics,technical frame,and key technologies of HCPSs for NGIM are then discussed in depth.Finally,an outlook of the major challenges of HCPSs for NGIM is proposed.

Toward New-Generation Intelligent Manufacturing

Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms： digital manufacturing, digital-networked manufacturing, and newgeneration intelligent manufacturing. New-generation intelligent manufacturing represents an indepth integration of new-generation artificial intelligence （AI） technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises＇ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new indus- trial revolution and will continue to he the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyher-physical systems （HCPSs） reveal the tech- nological mechanisms of new-generation intelligent manufacturing and can effectively guide related the- oretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technol- ogy roadmap for ＂parallel promotion and integrated development＂ should he developed in order to drive forward the intelligent transformation of the manufacturing industry in China.

Architecture-level particular risk modeling and analysis for a cyber-physical system with AADL

Cyber-physical systems(CPSs)are becoming increasingly important in safety-critical systems.Particular risk analysis(PRA)is an essential step in the safety assessment process to guarantee the quality of a system in the early phase of system development.Human factors like the physical environment are the most important part of particular risk assessment.Therefore,it is necessary to analyze the safety of the system considering human factor and physical factor.In this paper,we propose a new particular risk model(PRM)to improve the modeling ability of the Architecture Analysis and Design Language(AADL).An architecture-based PRA method is presented to support safety assessment for the AADL model of a cyber-physical system.To simulate the PRM with the proposed PRA method,model transformation from PRM to a deterministic and stochastic Petri net model is implemented.Finally,a case study on the power grid system of CPS is modeled and analyzed using the proposed method.