A novel cutting tool selection approach based on a metal cutting process knowledge graph

Duan, Yang; Hou, Long; Leng, Shuai

doi:10.1007/s00170-021-06606-5

Cited by 19 publications

(3 citation statements)

References 24 publications

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“…Modeling automation. Based on the process documents, it is possible to automate the process identification and process modeling [12]. The challenge is to identify the input and output parameters of each stage and to match with the corresponding branch network in the SNN model.…”

Section: Discussionmentioning

confidence: 99%

Design of a quality risk early warning system for discrete manufacturing based on stepped neural network

Shen,

Chu,

Duan

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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The complexity of mechanical part manufacturing process calls for quality control from the production line level. Regarding the fuzzy mechanism, low efficiency and high cost of error diagnosis and resolution, AI methods are used to carry out prognostics that avoid the failure occurs. This paper proposes an error prediction model based on a new form of neural network constructed by serially connected branch networks, in which each branch network corresponds to a manufacturing stage. Then an early warning system for the production line concentrating on discrete manufacturing industry is developed based on the proposed model. Application research is carried out in a mechanical parts manufacturing factory where the prototype system is testified to be effective and meaningful. The study contributes to reduce the production shutdown and improve quality control capability for discrete manufacturing factories.

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Section: Discussionmentioning

confidence: 99%

Design of a quality risk early warning system for discrete manufacturing based on stepped neural network

Shen,

Chu,

Duan

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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“…Consequently, numerous scholars have employed KG methodologies in process planning. For example, Yang Duan and colleagues developed a KG known as the metal cutting process knowledge graph (MCPKG) and then used a personalised PageRank (PPR) algorithm to recommend tools [14]; Hongshen Wang et al constructed an intelligent Q&A system with a KG of impeller CNC machining to achieve rapid search and reuse of CNC machining knowledge and experience [15].…”

Section: Process Kg-based Knowledge Reusementioning

confidence: 99%

A Knowledge-Guided Process Planning Approach with Reinforcement Learning

Zhang,

Wu,

Chen

et al. 2024

Preprint

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With the wide application of computer-aided technologies such as CAD and CAM in the manufacturing industry, more and more process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, more effective methods are needed to mine a large amount of multi-source information and the explicit and implicit relationships between knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning problem as a sequential recommendation of process knowledge. Secondly, this paper designs in detail the state representation that combines process sequences and potential relationships between processes. This paper also creates a composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure proposed in this paper has more accurate recommendation results than other methods. Finally, this paper takes flange as an example to verify the feasibility and effectiveness of the proposed method.

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“…They constructed a knowledge graph of aircraft manufacturing processes, and proposed a process route recommendation method based on it. Duan Yang et al 11 proposed the construction of a knowledge graph for metal cutting processes, which links factual knowledge and process knowledge in metal cutting on a semantic level, providing a new approach for creating an intelligent control system for metal cutting processes. Addressing the difficulty of representing complex and diverse process knowledge, Li Xiuling et al 12 proposed a process knowledge graph construction method for process reuse.…”

Section: Related Workmentioning

confidence: 99%

A method of knowledge graph construction for the field of mechanical manufacturing processes

Zhou,

Wang,

Jin

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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A novel cutting tool selection approach based on a metal cutting process knowledge graph

Cited by 19 publications

References 24 publications

Design of a quality risk early warning system for discrete manufacturing based on stepped neural network

Design of a quality risk early warning system for discrete manufacturing based on stepped neural network

A Knowledge-Guided Process Planning Approach with Reinforcement Learning

A method of knowledge graph construction for the field of mechanical manufacturing processes

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