Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents.This information encompasses various levels of elements and complex semantic relati...Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents.This information encompasses various levels of elements and complex semantic relationships.However,there is currently a scarcity of effective modeling techniques to express these documents'inherent assembly process knowledge.This study introduces a method for constructing an Assembly Process Knowledge Graph of Complex Products(APKG-CP)utilizing text mining techniques to tackle the challenges of high costs,low efficiency,and difficulty reusing process knowledge.Developing the assembly process knowledge graph involves categorizing entity and relationship classes from multiple levels.The Bert-BiLSTM-CRF model integrates BERT(bidirectional encoder representations from transformers),BiLSTM(bidirectional long short-term memory),and CRF(conditional random field)to extract knowledge entities and relationships in assembly process documents automatically.Furthermore,the knowledge fusion method automatically instantiates the assembly process knowledge graph.The proposed construction method is validated by constructing and visualizing an assembly process knowledge graph using data from an aerospace enterprise as an example.Integrating the knowledge graph with the assembly process preparation system demonstrates its effectiveness for process design.展开更多
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a...Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.展开更多
When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturin...When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.展开更多
With the nature of the high wind and sand in western China,the Chinese wolfberry recognition shows a strong relationship with the sandy noise and needs a high-accuracy algorithm.To address this issue,this study aimed ...With the nature of the high wind and sand in western China,the Chinese wolfberry recognition shows a strong relationship with the sandy noise and needs a high-accuracy algorithm.To address this issue,this study aimed to develop an algorithm for accurately detecting and recognizing wolfberries.YOLOv8,an algorithm promoted by Ultralytics,supports image classification,object detection,and instance segmentation tasks.To enhance the performance of the original YOLOv8 model,a novel YOLOv8 algorithm incorporating FasterNet,RepBiFPN,and Lightweight Asymmetric Dual-Head was proposed.Firstly,thousands of Chinese wolfberry images were collected from the Ningxia Academy of Agriculture and Forestry Science,China,and random noises were added to simulate the wind and sand conditions typical of spring.Secondly,leveraging the advantages of YOLOv8n,such as its high speed and accuracy,this research innovatively integrated the FasterNet block into the C2f module of YOLOv8 to improve the effective handling of data uncertainty and noise.Additionally,an innovative RepViT+BiFPN,a new detective head,and a Lightweight Asymmetric Dual-Head were introduced to improve the training efficiency of the YOLOv8 network.Finally,to evaluate the effectiveness of improved YOLOv8 for the recognition of wolfberry,the dataset of wolfberry images was divided into a training set,a validation set,and a testing set to assess the performances of different models.Experiment results demonstrate that the YOLOv8-FasterNet+LADH+RepBiFPN model outperforms other models in terms of mAP@0.50-0.95,achieving a 4.5%improvement on the validation set compared to the original YOLOv8n.This research addresses the high-speed and accurate recognition of the Chinese wolfberry under strong winds and sand noise through algorithmic improvements and integration,which can facilitate the automation and intelligence of Chinese wolfberry harvesting and contribute to the advancement of agricultural mechanization.展开更多
Despite advancements in optimisation techniques,existing flexible job shop problem(FJSP)models are reactive and struggle with dynamic scheduling.Digital twin(DT)technology offers a solution.This study integrates DT wi...Despite advancements in optimisation techniques,existing flexible job shop problem(FJSP)models are reactive and struggle with dynamic scheduling.Digital twin(DT)technology offers a solution.This study integrates DT with deep reinforcement learning(DRL)for proactive dynamic scheduling.A digital twin-based framework with multi-agent proximal policy optimisation(PPO)was used to adapt scheduling strategies in real-time.The virtual environment simulates production,predicts disruptions,and enables proactive adjustment.The dynamic flexible job shop problem(DFJSP)is modelled as a Markov decision process(MDP)with agents introduced to optimise decisions using DRL.The state and action spaces for the machine and job agents were designed to capture the real-time states.The reward function combines global(makespan)and local(machine utilisation)rewards.Multi-agent PPO trains agents in a virtual environment based on DT interactions.Experiments show that the method outperforms traditional rules and genetic algorithms,particularly in large-scale problems.Additionally,a real-world case study proved its effectiveness in managing machine failures and ensuring on-time completion with minimal deviation in dynamic and uncertain environments.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.52375479)。
文摘Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents.This information encompasses various levels of elements and complex semantic relationships.However,there is currently a scarcity of effective modeling techniques to express these documents'inherent assembly process knowledge.This study introduces a method for constructing an Assembly Process Knowledge Graph of Complex Products(APKG-CP)utilizing text mining techniques to tackle the challenges of high costs,low efficiency,and difficulty reusing process knowledge.Developing the assembly process knowledge graph involves categorizing entity and relationship classes from multiple levels.The Bert-BiLSTM-CRF model integrates BERT(bidirectional encoder representations from transformers),BiLSTM(bidirectional long short-term memory),and CRF(conditional random field)to extract knowledge entities and relationships in assembly process documents automatically.Furthermore,the knowledge fusion method automatically instantiates the assembly process knowledge graph.The proposed construction method is validated by constructing and visualizing an assembly process knowledge graph using data from an aerospace enterprise as an example.Integrating the knowledge graph with the assembly process preparation system demonstrates its effectiveness for process design.
基金Supported by National Key Research and Development Program(Grant No.2024YFB3312700)National Natural Science Foundation of China(Grant No.52405541)the Changzhou Municipal Sci&Tech Program(Grant No.CJ20241131)。
文摘Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.
基金Supported by Jiangsu Provincial Agriculture Science and Technology Innovation Fund(Grant No.CX(23)3036)National Natural Science Foundation of China(Grant No.52375479)+1 种基金Jiangsu Provincal Graduate Research and Practical Innovation Program(Grant No.KYCX24_0825)Changzhou Municipal Sci&Tech Program(Grant No.CM20223014).
文摘When several traditional flow-shop lines operate in parallel,the operation mode with no communication between production lines will no longer be the optimal production paradigm.This paper describes matrix manufacturing systems(MMS)in a general manner from the perspective of related works,comparing different manufacturing organizational forms and their characteristics.Subsequently,MMS are extracted during the parallel production of multiple surface mount technology(SMT)lines.An overall equipment effectiveness(OEE)online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS.The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS.The efficiency of each matrix unit(MU)was calculated,and a collaborative optimization method was proposed based on an indicator(OEE).In this paper,an example of eight SMT lines is presented.The partitioning of MUs,OEE calculation of each MU,and the low OEE unit collaborative optimization method are described in detail.Through a case study,the architecture of the collaborative optimization model for the MMS was constructed and discussed.Finally,the improvement in the OEE proved the effectiveness and usability of the proposed architecture.
基金supported by the National Natural Science Foundation of China(Grant No.32201681)the Ningxia Hui Autonomous Region Science and Technology Program(Grant 2021BEF02001)+2 种基金the Fruit,Vegetable,and Tea Harvesting Machinery Innovation Project of the Chinese Academy of Agricultural Sciencesthe Jiangsu Agriculture Science and Technology Innovation Fund(Grant JASTIF,CX(23)3036)the Changzhou Sci&Tech Program(Grant CJ20241131).
文摘With the nature of the high wind and sand in western China,the Chinese wolfberry recognition shows a strong relationship with the sandy noise and needs a high-accuracy algorithm.To address this issue,this study aimed to develop an algorithm for accurately detecting and recognizing wolfberries.YOLOv8,an algorithm promoted by Ultralytics,supports image classification,object detection,and instance segmentation tasks.To enhance the performance of the original YOLOv8 model,a novel YOLOv8 algorithm incorporating FasterNet,RepBiFPN,and Lightweight Asymmetric Dual-Head was proposed.Firstly,thousands of Chinese wolfberry images were collected from the Ningxia Academy of Agriculture and Forestry Science,China,and random noises were added to simulate the wind and sand conditions typical of spring.Secondly,leveraging the advantages of YOLOv8n,such as its high speed and accuracy,this research innovatively integrated the FasterNet block into the C2f module of YOLOv8 to improve the effective handling of data uncertainty and noise.Additionally,an innovative RepViT+BiFPN,a new detective head,and a Lightweight Asymmetric Dual-Head were introduced to improve the training efficiency of the YOLOv8 network.Finally,to evaluate the effectiveness of improved YOLOv8 for the recognition of wolfberry,the dataset of wolfberry images was divided into a training set,a validation set,and a testing set to assess the performances of different models.Experiment results demonstrate that the YOLOv8-FasterNet+LADH+RepBiFPN model outperforms other models in terms of mAP@0.50-0.95,achieving a 4.5%improvement on the validation set compared to the original YOLOv8n.This research addresses the high-speed and accurate recognition of the Chinese wolfberry under strong winds and sand noise through algorithmic improvements and integration,which can facilitate the automation and intelligence of Chinese wolfberry harvesting and contribute to the advancement of agricultural mechanization.
基金supported in part by the Natural Science Foundation of Jiangsu Province of China(BK20241780)Changzhou Science and Technology Program Project(CM20223014 and CJ20220207)Changzhou Science and Technology Support Plan(Social Development)Project(CE20205045).
文摘Despite advancements in optimisation techniques,existing flexible job shop problem(FJSP)models are reactive and struggle with dynamic scheduling.Digital twin(DT)technology offers a solution.This study integrates DT with deep reinforcement learning(DRL)for proactive dynamic scheduling.A digital twin-based framework with multi-agent proximal policy optimisation(PPO)was used to adapt scheduling strategies in real-time.The virtual environment simulates production,predicts disruptions,and enables proactive adjustment.The dynamic flexible job shop problem(DFJSP)is modelled as a Markov decision process(MDP)with agents introduced to optimise decisions using DRL.The state and action spaces for the machine and job agents were designed to capture the real-time states.The reward function combines global(makespan)and local(machine utilisation)rewards.Multi-agent PPO trains agents in a virtual environment based on DT interactions.Experiments show that the method outperforms traditional rules and genetic algorithms,particularly in large-scale problems.Additionally,a real-world case study proved its effectiveness in managing machine failures and ensuring on-time completion with minimal deviation in dynamic and uncertain environments.
