As businesses started to embrace Industry 4.0, along came the Fifth Industrial Revolution. Industry 5.0 complements the existing Industry 4.0 paradigm for the not-too-distant future by highlighting research and innovation as drivers for a transition to a sustainable, human-centric, and resilient industry. The core values of Industry 5.0 including human-centricity, sustainability, and resilience have prompted formal discussions that manufacturing should be human-centric. Human-centric smart manufacturing (HSM) takes full advantage of human flexibility, machine precision, and new-generation information technologies to construct a super smart, sustainable, and resilient manufacturing system. Nowadays, extensive research on the concept, architecture, enabling technologies, and applications of smart manufacturing have been conducted. However, research on HSM is relatively lacking, yet starting to gain traction rapidly. Hence, this paper primarily carries out a systematic literature review on HSM to identify promising research topics with high potential for further investigations. Firstly, this paper reveals the concept and connotation of HSM, on which the state-of-the-art architecture of HSM is introduced. Then, several promising Industry 4.0 technologies are identified as the key enablers that can continue to support the construction and operation of HSM, where a comprehensive survey and discussion on these technologies are conducted. Thirdly, what’s new in HSM applications and how HSM will reshape the entire lifecycle of industrial products are explored. Finally, this paper identifies limitations, barriers, and challenges that will be encountered during the developing, operating, and maintaining HSM, and provides valuable research directions to continuously improve HSM. 

Towards new-generation human-centric smart manufacturing in Industry 5.0: A systematic review

Digital twin-enabled dynamic spatial-temporal knowledge graph for production logistics resource allocation

Industry 4.0: a tertiary literature review

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control. 

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Computer-aided process planning is the bridge between computer-aided design and computer-aided manufacturing. With the advent of the intelligent manufacturing era, process knowledge is important for process planning. Knowledge graph is a semantic representation method of knowledge that has attracted extensive attention from the industry and academia. Process planning using the process knowledge graph has become an important development direction for computer-aided process planning. From the analysis of the published reviews, there have been many computer-aided process planning reviews with different focuses. We focus on the techniques and applications of knowledge graph in manufacturing process planning. Therefore, this paper comprehensively reviews knowledge graphs in manufacturing process planning. We analyze the key technologies of process knowledge graph, including process knowledge representation, process knowledge extraction, process knowledge graph construction, process knowledge graph refinement, process knowledge graph validation, and process generation. We also explore the combination of process knowledge graphs and large language models. Finally, potential future research directions are proposed. 

Knowledge graph-based manufacturing process planning: A state-of-the-art review

The process knowledge base is the key module in intelligent process design, it determines the intelligence degree of the design system and affects the quality of product design. However, traditional process knowledge base construction is non-automated, time consuming and requires much manual work, which is not sufficient to meet the demands of the modern manufacturing mode. Moreover, the knowledge base often adopts a single knowledge representation, and this may lead to ambiguity in the meaning of some knowledge, which will affect the quality of the process knowledge base. To overcome the above problems, an automatic construction framework for the process knowledge base in the field of machining based on knowledge graph (KG) is introduced. First, the knowledge is classified and annotated based on the function-behavior-states (FBS) design method. Second, a knowledge extraction framework based on BERT-BiLSTM-CRF is established to perform the automatic knowledge extraction of process text. Third, a knowledge representation method based on fuzzy comprehensive evaluation is established, forming three types of knowledge representation with the KG as the main, production rules and two-dimensional data linked list as a supplement. In addition, to overcome the redundancy in the knowledge fusion stage, a hybrid algorithm based on an improved edit distance and attribute weighting is built. Finally, a prototype system is developed, and quality analysis is carried out. Compared with the F values of BiLSTM-CRF and CNN-BiLSTM-CRF, that of the proposed extraction method in the machining domain is increased by 7.35% and 3.87%, respectively.

An automatic method for constructing machining process knowledge base from knowledge graph

With the integration of new-generation information and communication technology (ICT) in the manufacturing industry, many advanced manufacturing modes have been proposed. Cloud manufacturing (CMfg)...

Cloud manufacturing in China: a review

Automatic process decision-making is a key module in intelligent process design(IPD), which determines the intelligence degree of IPD and affects the quality of product design. The traditional proc...

An automatic machining process decision-making system based on knowledge graph

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An automatic method for constructing machining process knowledge base from knowledge graph

Cloud manufacturing in China: a review

An automatic machining process decision-making system based on knowledge graph