Pub Date : 2025-04-09DOI: 10.1016/j.jmsy.2025.03.025
Steve Yuwono , Ahmar Kamal Hussain , Dorothea Schwung , Andreas Schwung
In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders’ decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial–parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5%–13% while satisfying the production demand, which significantly improves potential (global objective) values.
{"title":"Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg games","authors":"Steve Yuwono , Ahmar Kamal Hussain , Dorothea Schwung , Andreas Schwung","doi":"10.1016/j.jmsy.2025.03.025","DOIUrl":"10.1016/j.jmsy.2025.03.025","url":null,"abstract":"<div><div>In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders’ decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial–parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5%–13% while satisfying the production demand, which significantly improves potential (global objective) values.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 578-594"},"PeriodicalIF":12.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-09DOI: 10.1016/j.jmsy.2025.03.017
Hamood Ur Rehman , Fan Mo , Jack C. Chaplin , Leszek Zarzycki , Mark Jones , Antonio Maffei , Svetan Ratchev
This paper presents an innovative approach to integrating data-driven strategies into intelligent manufacturing systems, specifically targeting the challenges of configuration management in modular production environments. To address the distinct and evolving requirements of customized products, we propose a dynamic configuration management methodology that automatically adjusts system settings in real-time. This approach utilizes operational semantics to formalize the interactions between production modules, capturing essential operational information for intelligent decision-making. A novel control mechanism is developed, using knowledge graphs to semantically represent and manage the relationships between production system components and settings. By mapping these, the system can determine optimal configurations based on real-time data and specific operational requirements. The interaction between the control mechanism and the knowledge graph ensures continuous adaptability, enabling the system to reconfigure dynamically in response to changes. This method was validated in an industrial dry-air leak testing scenario, demonstrating its effectiveness in adaptability.
{"title":"Intelligent configuration management in modular production systems: Integrating operational semantics with knowledge graphs","authors":"Hamood Ur Rehman , Fan Mo , Jack C. Chaplin , Leszek Zarzycki , Mark Jones , Antonio Maffei , Svetan Ratchev","doi":"10.1016/j.jmsy.2025.03.017","DOIUrl":"10.1016/j.jmsy.2025.03.017","url":null,"abstract":"<div><div>This paper presents an innovative approach to integrating data-driven strategies into intelligent manufacturing systems, specifically targeting the challenges of configuration management in modular production environments. To address the distinct and evolving requirements of customized products, we propose a dynamic configuration management methodology that automatically adjusts system settings in real-time. This approach utilizes operational semantics to formalize the interactions between production modules, capturing essential operational information for intelligent decision-making. A novel control mechanism is developed, using knowledge graphs to semantically represent and manage the relationships between production system components and settings. By mapping these, the system can determine optimal configurations based on real-time data and specific operational requirements. The interaction between the control mechanism and the knowledge graph ensures continuous adaptability, enabling the system to reconfigure dynamically in response to changes. This method was validated in an industrial dry-air leak testing scenario, demonstrating its effectiveness in adaptability.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 610-625"},"PeriodicalIF":12.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-07DOI: 10.1016/j.jmsy.2025.03.021
Yadong Xu , Sheng Li , Ke Feng , Ruyi Huang , Beibei Sun , Xiaolong Yang , Zhiheng Zhao , George Q. Huang
Precise condition monitoring of manufacturing systems is crucial for maintaining efficient industrial production. In practical manufacturing applications, typical components of manufacturing system such as gearboxes and bearings mainly operate under fluctuating conditions, resulting in obvious nonlinear characteristics in the monitored vibration signals. Nonetheless, numerous extant algorithms are crafted based on the stationary presumption that the signal’s amplitude and frequency remain constant, failing to reflect the real-world scenarios prevalent in industrial environments. In this research, we propose a domain constrained cascadic multirepetive learning network as a response to this challenge. Initially, we leverage cascadic multireceptive learning modules, multiscale feature aggregation modules, and an adaptive filtering module to establish the feature extractor for acquiring multireceptive and multilevel features from monitored signals. Next, a conditional label regulation loss is devised as the loss function to enhance the model’s robustness in complex scenarios. Finally, a domain constrained label adjuster is designed to align the actual labels based on the input data, thereby guiding the feature extractor in learning the domain-invariant feature. Three case studies demonstrate that the DC-CMLN model outperforms seven state-of-the-art algorithms, particularly when applied to mechanical datasets collected under nonstationary conditions.
{"title":"Domain constrained cascadic multireceptive learning networks for machine health monitoring in complex manufacturing systems","authors":"Yadong Xu , Sheng Li , Ke Feng , Ruyi Huang , Beibei Sun , Xiaolong Yang , Zhiheng Zhao , George Q. Huang","doi":"10.1016/j.jmsy.2025.03.021","DOIUrl":"10.1016/j.jmsy.2025.03.021","url":null,"abstract":"<div><div>Precise condition monitoring of manufacturing systems is crucial for maintaining efficient industrial production. In practical manufacturing applications, typical components of manufacturing system such as gearboxes and bearings mainly operate under fluctuating conditions, resulting in obvious nonlinear characteristics in the monitored vibration signals. Nonetheless, numerous extant algorithms are crafted based on the stationary presumption that the signal’s amplitude and frequency remain constant, failing to reflect the real-world scenarios prevalent in industrial environments. In this research, we propose a domain constrained cascadic multirepetive learning network as a response to this challenge. Initially, we leverage cascadic multireceptive learning modules, multiscale feature aggregation modules, and an adaptive filtering module to establish the feature extractor for acquiring multireceptive and multilevel features from monitored signals. Next, a conditional label regulation loss is devised as the loss function to enhance the model’s robustness in complex scenarios. Finally, a domain constrained label adjuster is designed to align the actual labels based on the input data, thereby guiding the feature extractor in learning the domain-invariant feature. Three case studies demonstrate that the DC-CMLN model outperforms seven state-of-the-art algorithms, particularly when applied to mechanical datasets collected under nonstationary conditions.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 563-577"},"PeriodicalIF":12.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-05DOI: 10.1016/j.jmsy.2025.03.016
Duidi Wu , Qianyou Zhao , Junming Fan , Jin Qi , Pai Zheng , Jie Hu
Human–robot collaboration enhances efficiency by enabling robots to work alongside human operators in shared tasks. Accurately understanding human intentions is critical for achieving a high level of collaboration. Existing methods heavily rely on case-specific data and face challenges with new tasks and unseen categories, while often limited data is available under real-world conditions. To bolster the proactive cognitive abilities of collaborative robots, this work introduces a Visual-Language-Temporal approach, conceptualizing intent recognition as a multimodal learning problem with HRC-oriented prompts. A large model with prior knowledge is fine-tuned to acquire industrial domain expertise, then enables efficient rapid transfer through few-shot learning in data-scarce scenarios. Comparisons with state-of-the-art methods across various datasets demonstrate the proposed approach achieves new benchmarks. Ablation studies confirm the efficacy of the multimodal framework, and few-shot experiments further underscore meta-perceptual potential. This work addresses the challenges of perceptual data and training costs, building a human–robot bridge (H2R Bridge) for semantic communication, and is expected to facilitate proactive HRC and further integration of large models in industrial applications.
{"title":"H2R Bridge: Transferring vision-language models to few-shot intention meta-perception in human robot collaboration","authors":"Duidi Wu , Qianyou Zhao , Junming Fan , Jin Qi , Pai Zheng , Jie Hu","doi":"10.1016/j.jmsy.2025.03.016","DOIUrl":"10.1016/j.jmsy.2025.03.016","url":null,"abstract":"<div><div>Human–robot collaboration enhances efficiency by enabling robots to work alongside human operators in shared tasks. Accurately understanding human intentions is critical for achieving a high level of collaboration. Existing methods heavily rely on case-specific data and face challenges with new tasks and unseen categories, while often limited data is available under real-world conditions. To bolster the proactive cognitive abilities of collaborative robots, this work introduces a Visual-Language-Temporal approach, conceptualizing intent recognition as a multimodal learning problem with HRC-oriented prompts. A large model with prior knowledge is fine-tuned to acquire industrial domain expertise, then enables efficient rapid transfer through few-shot learning in data-scarce scenarios. Comparisons with state-of-the-art methods across various datasets demonstrate the proposed approach achieves new benchmarks. Ablation studies confirm the efficacy of the multimodal framework, and few-shot experiments further underscore meta-perceptual potential. This work addresses the challenges of perceptual data and training costs, building a human–robot bridge (H2R Bridge) for semantic communication, and is expected to facilitate proactive HRC and further integration of large models in industrial applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":""},"PeriodicalIF":12.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-04DOI: 10.1016/j.jmsy.2025.03.019
Linshan Ding , Zailin Guan , Dan Luo , Lei Yue
In the context of Industry 4.0, manufacturers face pressure to personalize products and accelerate the supply chain. This requires rapid response to volatile production schedules, ensuring a balance between operational efficiency and product quality. Moreover, the rapid development and convergence of the cloud computing, Internet of Things (IoT), and big data have expanded the need for real-time tracking and adaptive scheduling to address uncertainties, such as equipment downtime, supply variation, and ongoing product revisions. The capability of IoT has significantly improved the continuous monitoring and data analysis, emphasizing the importance of developing effective real-time scheduling solutions in the manufacturing system. In response to these evolving industrial requirements, and driven by objectives to reduce the makespan, total tardiness, and energy consumption, we study the multi-objective multiplicity dynamic flexible job shop scheduling problem (MOMDFJSP), to cope with the challenges of new order arrivals and machine breakdowns in the IoT-enabled manufacturing system. This study proposes a novel hierarchical multi-policy deep reinforcement learning framework for IoT-infused manufacturing environments, aiming to integrate these diverse requirements and uncertainties into a coherent and responsive scheduling framework. The proposed framework comprises an upper-level control policy network and three lower-level objective policy networks. The upper-level and lower-level networks are respectively responsible for selecting temporary optimization objectives and specific dispatching rules. Based on the proposed framework, we design a two-stage training approach named the hierarchical multi-policy soft actor-critic (HMPSAC) algorithm to train multiple policy networks. In addition, we develop a fluid model to design the state features and dispatching rules that act as inputs and outputs, respectively, for the deep reinforcement learning (DRL) policy network. The comparative analysis with well-known dispatching rules and DRL-based methods reveals the superior performance of HMPSAC algorithm.
{"title":"Data-driven hierarchical multi-policy deep reinforcement learning framework for multi-objective multiplicity dynamic flexible job shop scheduling","authors":"Linshan Ding , Zailin Guan , Dan Luo , Lei Yue","doi":"10.1016/j.jmsy.2025.03.019","DOIUrl":"10.1016/j.jmsy.2025.03.019","url":null,"abstract":"<div><div>In the context of Industry 4.0, manufacturers face pressure to personalize products and accelerate the supply chain. This requires rapid response to volatile production schedules, ensuring a balance between operational efficiency and product quality. Moreover, the rapid development and convergence of the cloud computing, Internet of Things (IoT), and big data have expanded the need for real-time tracking and adaptive scheduling to address uncertainties, such as equipment downtime, supply variation, and ongoing product revisions. The capability of IoT has significantly improved the continuous monitoring and data analysis, emphasizing the importance of developing effective real-time scheduling solutions in the manufacturing system. In response to these evolving industrial requirements, and driven by objectives to reduce the makespan, total tardiness, and energy consumption, we study the multi-objective multiplicity dynamic flexible job shop scheduling problem (MOMDFJSP), to cope with the challenges of new order arrivals and machine breakdowns in the IoT-enabled manufacturing system. This study proposes a novel hierarchical multi-policy deep reinforcement learning framework for IoT-infused manufacturing environments, aiming to integrate these diverse requirements and uncertainties into a coherent and responsive scheduling framework. The proposed framework comprises an upper-level control policy network and three lower-level objective policy networks. The upper-level and lower-level networks are respectively responsible for selecting temporary optimization objectives and specific dispatching rules. Based on the proposed framework, we design a two-stage training approach named the hierarchical multi-policy soft actor-critic (HMPSAC) algorithm to train multiple policy networks. In addition, we develop a fluid model to design the state features and dispatching rules that act as inputs and outputs, respectively, for the deep reinforcement learning (DRL) policy network. The comparative analysis with well-known dispatching rules and DRL-based methods reveals the superior performance of HMPSAC algorithm.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 536-562"},"PeriodicalIF":12.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advancements in Large Language Models (LLMs) have significantly transformed the field of natural data interpretation, translation, and user training. However, a notable gap exists when LLMs are tasked to assist with real-time context-sensitive machine data. The paper presents a multi-agent LLM framework capable of accessing and interpreting real-time and historical data through an Industrial Internet of Things (IIoT) platform for evidence-based inferences. Real-time data is acquired from several legacy machine artifacts (such as seven-segment displays, toggle switches, and knobs), smart machines (such as 3D printers), and building data (such as sound sensors and temperature measurement devices) through MTConnect data streaming protocol. Further, a multi-agent LLM framework that consists of four specialized agents – a supervisor agent, a machine-expertise agent, a data visualization agent, and a fault-diagnostic agent is developed for context-specific manufacturing tasks. This LLM framework is then integrated into a digital twin to visualize the unstructured data in real time. The paper also explores how LLM-based digital twins can serve as real time virtual experts through an avatar, minimizing reliance on traditional manuals or supervisor-based expertise. To demonstrate the functionality and effectiveness of this framework, we present a case study consisting of legacy machine artifacts and modern machines. The results highlight the practical application of LLM to assist and infer real-time machine data in a digital twin environment.
Peer-review under responsibility of the scientific committee of the NAMRI/SME.
{"title":"IIoT-enabled digital twin for legacy and smart factory machines with LLM integration","authors":"Anuj Gautam , Manish Raj Aryal , Sourabh Deshpande , Shailesh Padalkar , Mikhail Nikolaenko , Ming Tang , Sam Anand","doi":"10.1016/j.jmsy.2025.03.022","DOIUrl":"10.1016/j.jmsy.2025.03.022","url":null,"abstract":"<div><div>Recent advancements in Large Language Models (LLMs) have significantly transformed the field of natural data interpretation, translation, and user training. However, a notable gap exists when LLMs are tasked to assist with real-time context-sensitive machine data. The paper presents a multi-agent LLM framework capable of accessing and interpreting real-time and historical data through an Industrial Internet of Things (IIoT) platform for evidence-based inferences. Real-time data is acquired from several legacy machine artifacts (such as seven-segment displays, toggle switches, and knobs), smart machines (such as 3D printers), and building data (such as sound sensors and temperature measurement devices) through MTConnect data streaming protocol. Further, a multi-agent LLM framework that consists of four specialized agents – a supervisor agent, a machine-expertise agent, a data visualization agent, and a fault-diagnostic agent is developed for context-specific manufacturing tasks. This LLM framework is then integrated into a digital twin to visualize the unstructured data in real time. The paper also explores how LLM-based digital twins can serve as real time virtual experts through an avatar, minimizing reliance on traditional manuals or supervisor-based expertise. To demonstrate the functionality and effectiveness of this framework, we present a case study consisting of legacy machine artifacts and modern machines. The results highlight the practical application of LLM to assist and infer real-time machine data in a digital twin environment.</div><div>Peer-review under responsibility of the scientific committee of the NAMRI/SME.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 511-523"},"PeriodicalIF":12.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-03DOI: 10.1016/j.jmsy.2025.03.014
Clayton Cooper , Jianjing Zhang , Y.B. Guo , Robert X. Gao
Accurate quantification of machined surface roughness is crucial to the characterization of part performance measures, including aerodynamics and biocompatibility. Given this cruciality, there exists a need for predictive metrology models which can predict the surface roughness before a part leaves a machine to reduce metrology-induced bottlenecks and improve production planning efficiency under emergent production paradigms, e.g., Industry 4.0. Current predictive metrology approaches in machining generally train machine learning models on all available input features at once. However, this approach yields a high number of free parameters during all states of training, possibly leading to suboptimal prediction results because of the complexity of simultaneously optimizing all parameters at once. In addition, previous machine learning-enabled surface roughness prediction studies have used limited test dataset sizes, which reduces the reliability and robustness of the reported results. To address these limitations, this study proposes a two-stage model training approach based on domain-incremental learning, wherein a second stage of training is performed using an expanded input domain. The proposed method is evaluated on a 3,000-element experimentally collected testing dataset of machined H13 tool steel surfaces, where it achieves 16.3 % roughness prediction error compared to the 29.5 % error of the conventional single-stage training approach, indicating the suitability of the two-stage training method for reducing surface roughness prediction error.
{"title":"Surface roughness prediction in machining using two-stage domain-incremental learning with input dimensionality expansion","authors":"Clayton Cooper , Jianjing Zhang , Y.B. Guo , Robert X. Gao","doi":"10.1016/j.jmsy.2025.03.014","DOIUrl":"10.1016/j.jmsy.2025.03.014","url":null,"abstract":"<div><div>Accurate quantification of machined surface roughness is crucial to the characterization of part performance measures, including aerodynamics and biocompatibility. Given this cruciality, there exists a need for predictive metrology models which can predict the surface roughness before a part leaves a machine to reduce metrology-induced bottlenecks and improve production planning efficiency under emergent production paradigms, e.g., Industry 4.0. Current predictive metrology approaches in machining generally train machine learning models on all available input features at once. However, this approach yields a high number of free parameters during all states of training, possibly leading to suboptimal prediction results because of the complexity of simultaneously optimizing all parameters at once. In addition, previous machine learning-enabled surface roughness prediction studies have used limited test dataset sizes, which reduces the reliability and robustness of the reported results. To address these limitations, this study proposes a two-stage model training approach based on domain-incremental learning, wherein a second stage of training is performed using an expanded input domain. The proposed method is evaluated on a 3,000-element experimentally collected testing dataset of machined H13 tool steel surfaces, where it achieves 16.3 % roughness prediction error compared to the 29.5 % error of the conventional single-stage training approach, indicating the suitability of the two-stage training method for reducing surface roughness prediction error.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 503-510"},"PeriodicalIF":12.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.jmsy.2025.03.010
Kaiyao Zhang, Wenlei Xiao, Xiangming Fan, Gang Zhao
The next-generation STEP-NC technology requires automatic generation of machining strategies within manufacturing systems to implement flexible manufacturing in the future. Currently, the machining feature modeling based on STEP-NC is in its infancy, facing challenges such as cumbersome modeling processes, ineffective utilization of the STEP-NC standard, and low development efficiency. A low-code modular solution based on the STEP-NC data kernel for machining feature-oriented modeling is important to achieve more intelligent flexible manufacturing. This paper presents a low-code modular modeling method for machining features, based on the STEP-NC data structure and incorporating geometric, process, and machining information, aimed at part milling. A low-code modular CAD modeling platform based on STEP-NC was built using Rhino Grasshopper. Additionally, a toolpath generation algorithm was designed for milling feature models to enable the automatic generation of milling strategies. Finally, the feasibility of a low-code modular CAD system based on machining features for STEP-NC compliant manufacturing in engineering applications is validated through a case study involving part design, milling, and optimization.
{"title":"A low-code modular CAD system oriented towards machining features for STEP-NC compliant manufacturing","authors":"Kaiyao Zhang, Wenlei Xiao, Xiangming Fan, Gang Zhao","doi":"10.1016/j.jmsy.2025.03.010","DOIUrl":"10.1016/j.jmsy.2025.03.010","url":null,"abstract":"<div><div>The next-generation STEP-NC technology requires automatic generation of machining strategies within manufacturing systems to implement flexible manufacturing in the future. Currently, the machining feature modeling based on STEP-NC is in its infancy, facing challenges such as cumbersome modeling processes, ineffective utilization of the STEP-NC standard, and low development efficiency. A low-code modular solution based on the STEP-NC data kernel for machining feature-oriented modeling is important to achieve more intelligent flexible manufacturing. This paper presents a low-code modular modeling method for machining features, based on the STEP-NC data structure and incorporating geometric, process, and machining information, aimed at part milling. A low-code modular CAD modeling platform based on STEP-NC was built using Rhino Grasshopper. Additionally, a toolpath generation algorithm was designed for milling feature models to enable the automatic generation of milling strategies. Finally, the feasibility of a low-code modular CAD system based on machining features for STEP-NC compliant manufacturing in engineering applications is validated through a case study involving part design, milling, and optimization.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 487-502"},"PeriodicalIF":12.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.jmsy.2025.03.015
Dolor R. Enarevba , Nebojsa I. Jaksic , Karl R. Haapala
This research is motivated by the increasing demand for bio-based materials, the recent growth of the U.S. hemp industry, and the broader trend of using sustainable filaments in additive manufacturing. This study presents a comparative life cycle assessment (LCA) of untreated hemp straw and kraft lignin as fillers for polylactic acid (PLA) 3D-printed tensile specimens to evaluate their environmental impacts. Both materials, being low-cost renewable bio-based fillers, can enhance elongation of PLA while reducing the environmental footprint of 3D-printed components. The environmental impacts of hemp straw-PLA and kraft lignin-PLA were assessed using several life cycle impact assessment (LCIA) methods, including ReCiPe 2016 Endpoint (H), Cumulative Energy Demand (CED), and IPCC GWP100. Hemp straw showed lower environmental impacts than kraft lignin across most categories, making it a more favorable option for eco-conscious prosumers. The ReCiPe 2016 results indicated that major impact categories for kraft lignin-PLA were fine particulate matter formation, global warming potential, and human toxicity, with filament production being the major contributor. For hemp straw-PLA, hemp straw pre-processing was the major contributor. The CED method revealed that nonrenewable fossil resources had the highest impact on both materials. IPCC GWP100 results aligned with CED, showing higher greenhouse gas emissions for kraft lignin-PLA, mainly due to fossil fuel use. Sensitivity analysis of transportation distances showed no significant differences in impact results, while alternative LCIA methods (TRACI and IMPACT World+) confirmed the consistency of the findings. To build upon this study, future work will explore the environmental performance of treated hemp materials as alternative fillers for 3D-printed components.
{"title":"A comparative life cycle assessment of kraft lignin and hemp straw fillers to improve ductility of polylactide (PLA) 3D printed parts","authors":"Dolor R. Enarevba , Nebojsa I. Jaksic , Karl R. Haapala","doi":"10.1016/j.jmsy.2025.03.015","DOIUrl":"10.1016/j.jmsy.2025.03.015","url":null,"abstract":"<div><div>This research is motivated by the increasing demand for bio-based materials, the recent growth of the U.S. hemp industry, and the broader trend of using sustainable filaments in additive manufacturing. This study presents a comparative life cycle assessment (LCA) of untreated hemp straw and kraft lignin as fillers for polylactic acid (PLA) 3D-printed tensile specimens to evaluate their environmental impacts. Both materials, being low-cost renewable bio-based fillers, can enhance elongation of PLA while reducing the environmental footprint of 3D-printed components. The environmental impacts of hemp straw-PLA and kraft lignin-PLA were assessed using several life cycle impact assessment (LCIA) methods, including ReCiPe 2016 Endpoint (H), Cumulative Energy Demand (CED), and IPCC GWP100. Hemp straw showed lower environmental impacts than kraft lignin across most categories, making it a more favorable option for eco-conscious prosumers. The ReCiPe 2016 results indicated that major impact categories for kraft lignin-PLA were fine particulate matter formation, global warming potential, and human toxicity, with filament production being the major contributor. For hemp straw-PLA, hemp straw pre-processing was the major contributor. The CED method revealed that nonrenewable fossil resources had the highest impact on both materials. IPCC GWP100 results aligned with CED, showing higher greenhouse gas emissions for kraft lignin-PLA, mainly due to fossil fuel use. Sensitivity analysis of transportation distances showed no significant differences in impact results, while alternative LCIA methods (TRACI and IMPACT World+) confirmed the consistency of the findings. To build upon this study, future work will explore the environmental performance of treated hemp materials as alternative fillers for 3D-printed components.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 479-486"},"PeriodicalIF":12.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.jmsy.2025.03.013
Wentao Wang, Jing Zhao
The technological advancements of Industry 5.0 place greater emphasis on environmental sustainability and resilience for production scheduling. The flexible job shop scheduling problem (FJSP) effectively adapts to complex production environments and diverse scheduling requirements, which has made it an essential tool for studying modern production scenarios. Against this backdrop, this paper proposes an energy-efficient and low-carbon flexible job shop scheduling problem considering machine restart (ELFJSP-MR), aiming to minimize the makespan and total carbon emissions of the system. To solve ELFJSP-MR, we present an enhanced memetic algorithm (EMA) and design machine restart strategy to balance energy consumption and equipment lifespan. A multi-population hybrid model initialization based on logistic population growth model is used to enhance initial population diversity. Two novel neighborhood search methods are developed to improve convergence speed and explore the solution space more thoroughly. To enhance the flexibility and efficiency of local search, an adaptive operator selection model is designed. Finally, EMA and four well-known algorithms are evaluated on various benchmark problem instances. Experimental results demonstrate that EMA achieves faster convergence and greater stability for ELFJSP-MR. Furthermore, EMA exhibits exceptional performance across eight instances of aerospace composite material processing.
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