Pub Date : 2025-11-04DOI: 10.1016/j.jmsy.2025.10.007
Karl Lossie , Jan Hendrik Hellmich , Junjie Liang , Jonas Baum , Amon Göppert , Dennis Grunert , Robert H. Schmitt
The increasing diversity and shorter life cycles of technical products pose significant challenges for manufacturing companies, particularly in the context of providing specific and context-sensitive instructions to employees, especially in domains including maintenance, assembly and disassembly. This challenge holds significant importance in the context of the current skilled worker shortage. This paper proposes a solution by leveraging digital twin technology and smart services to automate the generation of context-sensitive instructions. The research outlines the development of a smart service system that uses real-time data from digital twins to create and deliver adaptive and user-specific instructions via smart devices. A conceptual design of the smart service system, a prototypical implementation using a rolling mill maintenance task, and the verification and validation of the developed system were carried out. The results indicate that the proposed system effectively addresses the challenges of traditional manual instructions, enhancing efficiency, accuracy, and user satisfaction.
{"title":"Using a digital twin and smart services to enable automatic generation of context-sensitive instructions","authors":"Karl Lossie , Jan Hendrik Hellmich , Junjie Liang , Jonas Baum , Amon Göppert , Dennis Grunert , Robert H. Schmitt","doi":"10.1016/j.jmsy.2025.10.007","DOIUrl":"10.1016/j.jmsy.2025.10.007","url":null,"abstract":"<div><div>The increasing diversity and shorter life cycles of technical products pose significant challenges for manufacturing companies, particularly in the context of providing specific and context-sensitive instructions to employees, especially in domains including maintenance, assembly and disassembly. This challenge holds significant importance in the context of the current skilled worker shortage. This paper proposes a solution by leveraging digital twin technology and smart services to automate the generation of context-sensitive instructions. The research outlines the development of a smart service system that uses real-time data from digital twins to create and deliver adaptive and user-specific instructions via smart devices. A conceptual design of the smart service system, a prototypical implementation using a rolling mill maintenance task, and the verification and validation of the developed system were carried out. The results indicate that the proposed system effectively addresses the challenges of traditional manual instructions, enhancing efficiency, accuracy, and user satisfaction.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 652-674"},"PeriodicalIF":14.2,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465405","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 : 2025-10-31DOI: 10.1016/j.jmsy.2025.10.009
Jeremy Cleeman , Adrian Jackson , Anandkumar Patel , Zihan Wang , Thomas Feldhausen , Chenhui Shao , Hongyi Xu , Rajiv Malhotra
Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.
{"title":"Operational resilience of additively manufactured parts to stealthy cyberphysical attacks using geometric and process digital twins","authors":"Jeremy Cleeman , Adrian Jackson , Anandkumar Patel , Zihan Wang , Thomas Feldhausen , Chenhui Shao , Hongyi Xu , Rajiv Malhotra","doi":"10.1016/j.jmsy.2025.10.009","DOIUrl":"10.1016/j.jmsy.2025.10.009","url":null,"abstract":"<div><div>Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 626-651"},"PeriodicalIF":14.2,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416648","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}
Machine tools are critical to modern manufacturing, yet their high energy consumption and vulnerability to faults present significant operational challenges. While predictive models can enhance energy optimization and fault diagnosis, their performance is often constrained by the scarcity of high-quality training data. To address this gap, this study presents a real-time digital twin (DT) framework that integrates OPAL-RT HIL simulation with OPC-UA-based cloud communication. The system enables both energy monitoring and synthetic fault data generation under diverse machining conditions. The DT operates in a bidirectional loop with a cloud-based data acquisition layer, allowing real-time parameter input and retrieval of simulated outputs. Model fidelity is verified by aligning simulation results with real-world CNC machine measurements and further confirmed through pattern-based external validation. The framework is applied to analyze energy consumption across varying machining parameters — such as electrospindle speed, feed rate, tool length, and depth of cut — and to simulate bearing fault scenarios for evaluating their impact on power consumption. These simulations produce labeled datasets suitable for future diagnostic and predictive maintenance applications. This work delivers a validated, closed-loop DT framework that unites high-fidelity OPAL-RT simulation, real-time OPC-UA data exchange, and synthetic data generation, extending predictive maintenance capabilities beyond those of prior modeling or diagnostic approaches. The proposed methodology offers a scalable foundation for energy-aware machining and real-time fault detection, contributing to sustainable manufacturing practices and operational resilience in smart industrial systems.
{"title":"A novel approach to digital twin-based energy efficiency monitoring and failure analysis in industrial applications","authors":"Mohsen Zeynivand, Parisa Esmaili, Loredana Cristaldi, Giambattista Gruosso","doi":"10.1016/j.jmsy.2025.10.011","DOIUrl":"10.1016/j.jmsy.2025.10.011","url":null,"abstract":"<div><div>Machine tools are critical to modern manufacturing, yet their high energy consumption and vulnerability to faults present significant operational challenges. While predictive models can enhance energy optimization and fault diagnosis, their performance is often constrained by the scarcity of high-quality training data. To address this gap, this study presents a real-time digital twin (DT) framework that integrates OPAL-RT HIL simulation with OPC-UA-based cloud communication. The system enables both energy monitoring and synthetic fault data generation under diverse machining conditions. The DT operates in a bidirectional loop with a cloud-based data acquisition layer, allowing real-time parameter input and retrieval of simulated outputs. Model fidelity is verified by aligning simulation results with real-world CNC machine measurements and further confirmed through pattern-based external validation. The framework is applied to analyze energy consumption across varying machining parameters — such as electrospindle speed, feed rate, tool length, and depth of cut — and to simulate bearing fault scenarios for evaluating their impact on power consumption. These simulations produce labeled datasets suitable for future diagnostic and predictive maintenance applications. This work delivers a validated, closed-loop DT framework that unites high-fidelity OPAL-RT simulation, real-time OPC-UA data exchange, and synthetic data generation, extending predictive maintenance capabilities beyond those of prior modeling or diagnostic approaches. The proposed methodology offers a scalable foundation for energy-aware machining and real-time fault detection, contributing to sustainable manufacturing practices and operational resilience in smart industrial systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 612-625"},"PeriodicalIF":14.2,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416649","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 : 2025-10-31DOI: 10.1016/j.jmsy.2025.10.004
Yangyang Liu , Tang Ji , Xiangyu Guo , Xun Xu , Jan Polzer
Cognitive Digital Twin (CDT) represents an advanced evolution of traditional Digital Twin (DT) technology, overcoming constraints in perception, reasoning, learning, and self-evolution to meet the growing demands of complex and dynamic industrial systems. This study first analyses the conceptual evolution of CDT and categorises it into three categories based on differing research trends. Through a comparative analysis of the definitions across these categories, we summarise the core features of CDT. Based on these characteristics, this study proposes a novel evaluation criteria for CDT, which systematically assesses its performance in cognitive functions such as perception, reasoning, and memory. Finally, building upon the preceding analysis, we identify the key challenges currently facing the field and envision potential future research directions to provide theoretical insights and practical guidance for developing next-generation DT technology.
{"title":"Cognitive Digital Twin frameworks in manufacturing—A critical survey, evaluation criteria, and future directions","authors":"Yangyang Liu , Tang Ji , Xiangyu Guo , Xun Xu , Jan Polzer","doi":"10.1016/j.jmsy.2025.10.004","DOIUrl":"10.1016/j.jmsy.2025.10.004","url":null,"abstract":"<div><div>Cognitive Digital Twin (CDT) represents an advanced evolution of traditional Digital Twin (DT) technology, overcoming constraints in perception, reasoning, learning, and self-evolution to meet the growing demands of complex and dynamic industrial systems. This study first analyses the conceptual evolution of CDT and categorises it into three categories based on differing research trends. Through a comparative analysis of the definitions across these categories, we summarise the core features of CDT. Based on these characteristics, this study proposes a novel evaluation criteria for CDT, which systematically assesses its performance in cognitive functions such as perception, reasoning, and memory. Finally, building upon the preceding analysis, we identify the key challenges currently facing the field and envision potential future research directions to provide theoretical insights and practical guidance for developing next-generation DT technology.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 597-611"},"PeriodicalIF":14.2,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416647","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 : 2025-10-30DOI: 10.1016/j.jmsy.2025.10.008
Pablo Malvido Fresnillo , Saigopal Vasudevan , Wael M. Mohammed , Jose A. Perez Garcia , Jose L. Martinez Lastra
Wire harnesses are critical components in modern vehicles, responsible for transmitting electrical signals and power to sensors and actuators. Despite the high level of automation in the automotive industry, wire harness manufacturing still relies heavilylargely depends on manual assembly. This is due to the significant challenges posed by the process, such as the complexity of perceiving and manipulating flexible materials and the high degree of customization required. As a result, existing solutions only address specific assembly tasks, rather than the entire processare fragmented, unable to scale to full production, and remain economically unviable for high-mix scenarios. To bridge this gap, this paper presents a novel robotic system for fully automating wire harness assembly. The system adopts a task-level programming methodology that leverages process knowledge to enable fast and easy reconfiguration. Additionally, it incorporates specific solutions to address key challenges in multi-branch wire harness manipulation, such as cable separation and entanglement prevention. The system’s performance was evaluated in two real-world assembly scenarios using a dual-arm robot. Experimental results demonstrate the system’s effectiveness and ease of reconfiguration, achieving success rates of 55% and 73% in two complex multi-branch wire harness assembly processes, and highlight areas of improvement, which will be further investigated in future works. The system repository is openly available allowing other researchers to build their solutions upon the proposed methodology.
{"title":"A dual-arm robotic system for automated multi-branch wire harness assembly in automotive industry","authors":"Pablo Malvido Fresnillo , Saigopal Vasudevan , Wael M. Mohammed , Jose A. Perez Garcia , Jose L. Martinez Lastra","doi":"10.1016/j.jmsy.2025.10.008","DOIUrl":"10.1016/j.jmsy.2025.10.008","url":null,"abstract":"<div><div>Wire harnesses are critical components in modern vehicles, responsible for transmitting electrical signals and power to sensors and actuators. Despite the high level of automation in the automotive industry, wire harness manufacturing still relies heavilylargely depends on manual assembly. This is due to the significant challenges posed by the process, such as the complexity of perceiving and manipulating flexible materials and the high degree of customization required. As a result, existing solutions only address specific assembly tasks, rather than the entire processare fragmented, unable to scale to full production, and remain economically unviable for high-mix scenarios. To bridge this gap, this paper presents a novel robotic system for fully automating wire harness assembly. The system adopts a task-level programming methodology that leverages process knowledge to enable fast and easy reconfiguration. Additionally, it incorporates specific solutions to address key challenges in multi-branch wire harness manipulation, such as cable separation and entanglement prevention. The system’s performance was evaluated in two real-world assembly scenarios using a dual-arm robot. Experimental results demonstrate the system’s effectiveness and ease of reconfiguration, achieving success rates of 55% and 73% in two complex multi-branch wire harness assembly processes, and highlight areas of improvement, which will be further investigated in future works. The system repository is openly available allowing other researchers to build their solutions upon the proposed methodology.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 577-596"},"PeriodicalIF":14.2,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416704","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}
Detailed machine-specific data are critical for accurate sustainability assessment and for supporting design decisions to reduce environmental impacts from manufacturing. However, obtaining, analyzing, and interpreting such fine-grained measurements can be challenging and inefficient. Existing methods for the above are time-consuming, do not fully capture process variability over time, and do not relate primary manufacturing data back to design decision-making. In this work, we propose a methodology to programmatically disaggregate process-level life cycle inventory data measurements, and relate it to both operations (i.e., activities or sub-processes) within the process and the geometric features created or affected by the process. We do this by leveraging the underlying machine code used to manufacture the part, in this case G-code, and by providing a scalable definition scheme for the corresponding operations, geometric features, and the relationship between them. Results can be used to generate targeted, actionable insights into process setup and product design improvements to address environmental impacts of manufacturing processes.
{"title":"Feature-centric allocation and visualization of primary manufacturing process life cycle inventory data","authors":"Teodor Vernica , Badrinath Veluri , Devarajan Ramanujan","doi":"10.1016/j.jmsy.2025.10.006","DOIUrl":"10.1016/j.jmsy.2025.10.006","url":null,"abstract":"<div><div>Detailed machine-specific data are critical for accurate sustainability assessment and for supporting design decisions to reduce environmental impacts from manufacturing. However, obtaining, analyzing, and interpreting such fine-grained measurements can be challenging and inefficient. Existing methods for the above are time-consuming, do not fully capture process variability over time, and do not relate primary manufacturing data back to design decision-making. In this work, we propose a methodology to programmatically disaggregate process-level life cycle inventory data measurements, and relate it to both operations (i.e., activities or sub-processes) within the process and the geometric features created or affected by the process. We do this by leveraging the underlying machine code used to manufacture the part, in this case G-code, and by providing a scalable definition scheme for the corresponding operations, geometric features, and the relationship between them. Results can be used to generate targeted, actionable insights into process setup and product design improvements to address environmental impacts of manufacturing processes.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 557-576"},"PeriodicalIF":14.2,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416703","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 : 2025-10-21DOI: 10.1016/j.jmsy.2025.10.005
Jianhao Lv, Jiahui Si, Ding Gao, Jinsong Bao
Existing AR-assisted Human–Robot Collaboration (HRC) systems passively respond to real-time information, lacking the ability to model, store, and leverage historical task knowledge in HRC scenarios, thus relying on replacing pre-programmed fixed-content modules for upgrades. To address this constraint, a historical visual question answering (VQA) framework with large language models is proposed. The unstructured visual frame is converted into structured information via structured visual representation, supported by a cross-modal interaction module and multi-component loss function to lay a structured foundation for storing historical experiences and subsequent reasoning. A temporally structured Memory Graph (MG) is constructed. Combined with large language models, historical VQA solves traditional VQA’s reliance on static images and lack of temporal continuity; An AR-assisted Human–Robot Interaction pipeline is designed for bidirectional transmission and visualization, integrating perception and reasoning results with AR to enable Human–Robot bidirectional communication. Quantitative and qualitative results show the method significantly outperforms in integrating historical and real-time information with supporting HRC VQA.
{"title":"Historical visual question answering with large language model for Augmented Reality-assisted Human–Robot Collaboration","authors":"Jianhao Lv, Jiahui Si, Ding Gao, Jinsong Bao","doi":"10.1016/j.jmsy.2025.10.005","DOIUrl":"10.1016/j.jmsy.2025.10.005","url":null,"abstract":"<div><div>Existing AR-assisted Human–Robot Collaboration (HRC) systems passively respond to real-time information, lacking the ability to model, store, and leverage historical task knowledge in HRC scenarios, thus relying on replacing pre-programmed fixed-content modules for upgrades. To address this constraint, a historical visual question answering (VQA) framework with large language models is proposed. The unstructured visual frame is converted into structured information via structured visual representation, supported by a cross-modal interaction module and multi-component loss function to lay a structured foundation for storing historical experiences and subsequent reasoning. A temporally structured Memory Graph (MG) is constructed. Combined with large language models, historical VQA solves traditional VQA’s reliance on static images and lack of temporal continuity; An AR-assisted Human–Robot Interaction pipeline is designed for bidirectional transmission and visualization, integrating perception and reasoning results with AR to enable Human–Robot bidirectional communication. Quantitative and qualitative results show the method significantly outperforms in integrating historical and real-time information with supporting HRC VQA.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 546-556"},"PeriodicalIF":14.2,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362159","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 : 2025-10-17DOI: 10.1016/j.jmsy.2025.10.003
Xuanhao Wen , Huajun Cao , Hongcheng Li , Weiwei Ge , Na Yang , Xiaohui Huang , Jin Zhou , Qiongzhi Zhang
The obligatory carbon neutrality targets drive manufacturers to improve energy performance for emission reduction. In this context, energy waste diagnosis in production has become increasingly critical. However, energy waste in manufacturing systems exhibits complex characteristics such as multi-source distributions, multi-form mediums and multi-variant influencers, resulting in a lack of comprehensive diagnosis methods. Inspired by effectiveness metrics for productivity waste diagnosis, this paper extends the concept of energy efficiency to energy effectiveness and proposes a novel energy waste diagnosis method. Firstly, it transcends the binary classification of energy consumption to establish a novel energy waste taxonomy. Next, a hierarchical framework of energy effectiveness metrics (indicators and dynamic benchmarks) is developed. These metrics are then quantified using data-driven approaches, such as meta-energy-blocks, to pinpoint the root-causes of waste. Finally, the method facilitates practical applications such as energy-saving potential estimation and waste visualization. An industrial case study on a die-casting unit demonstrates the method's effectiveness and practicality. The results revealed that actual energy consumption exceeded the ideal minimum by 11.5 times, indicating significant saving potential. Moreover, 37.2 % of energy was wasted due to managerial issues, with the method successfully identifying their specific root-causes for targeted improvements. The main novelty of the proposed method lies in its transferable effectiveness metric framework, which enables a comprehensive and in-depth diagnosis of diverse energy waste types, thereby bridging a critical gap in manufacturing energy management.
{"title":"From efficiency to effectiveness: A new method for diagnosing energy waste in manufacturing systems","authors":"Xuanhao Wen , Huajun Cao , Hongcheng Li , Weiwei Ge , Na Yang , Xiaohui Huang , Jin Zhou , Qiongzhi Zhang","doi":"10.1016/j.jmsy.2025.10.003","DOIUrl":"10.1016/j.jmsy.2025.10.003","url":null,"abstract":"<div><div>The obligatory carbon neutrality targets drive manufacturers to improve energy performance for emission reduction. In this context, energy waste diagnosis in production has become increasingly critical. However, energy waste in manufacturing systems exhibits complex characteristics such as multi-source distributions, multi-form mediums and multi-variant influencers, resulting in a lack of comprehensive diagnosis methods. Inspired by effectiveness metrics for productivity waste diagnosis, this paper extends the concept of energy efficiency to energy effectiveness and proposes a novel energy waste diagnosis method. Firstly, it transcends the binary classification of energy consumption to establish a novel energy waste taxonomy. Next, a hierarchical framework of energy effectiveness metrics (indicators and dynamic benchmarks) is developed. These metrics are then quantified using data-driven approaches, such as meta-energy-blocks, to pinpoint the root-causes of waste. Finally, the method facilitates practical applications such as energy-saving potential estimation and waste visualization. An industrial case study on a die-casting unit demonstrates the method's effectiveness and practicality. The results revealed that actual energy consumption exceeded the ideal minimum by 11.5 times, indicating significant saving potential. Moreover, 37.2 % of energy was wasted due to managerial issues, with the method successfully identifying their specific root-causes for targeted improvements. The main novelty of the proposed method lies in its transferable effectiveness metric framework, which enables a comprehensive and in-depth diagnosis of diverse energy waste types, thereby bridging a critical gap in manufacturing energy management.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 517-545"},"PeriodicalIF":14.2,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324716","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 : 2025-10-15DOI: 10.1016/j.jmsy.2025.09.016
Victor Vantilborgh, Tom Lefebvre, Guillaume Crevecoeur
This paper proposes a generic low-cost method for probabilistic condition monitoring of industrial equipment. A computationally efficient recursive data-driven model is constructed that correlates real-time machine measurements with a quantity or several quantities of interest (QoIs), including artificial metrics such as the Remaining Useful Lifetime (RUL). To that end, a probabilistic state–space model (PSSM) is identified, based on a fully instrumented measurement set obtained from a limited set of experiments that can only be obtained in a specialized testing environment. The dataset contains both cheaply available sensory information as well as prohibitively expensive, invasive or artificially constructed sensory signals. To identify a Maximum Likelihood Estimate of the PSSM, we rely on the Expectation–Maximization (EM) algorithm and Sequential Monte Carlo (SMC) estimation techniques. During operation, only the vital, non-intrusive and cheap sensors are used. The PSSM is then repurposed to reconstruct the costly sensory signals, realizing an effective and general purpose virtual sensor. Our methodology demonstrates the capacity to robustly estimate unmeasured physical variables in real-time and artificially constructed prognostic QoIs, such as the RUL, even when working with an incomplete measurement array. We validate the presented methodology for condition monitoring on the C-MAPSS dataset and a solenoid valve (SV) use case. The presented tool has similar predictive capabilities as compared with other state-of-the-art RUL prognostic methods and furthermore provides uncertainty quantification and contextual information with respect to equipment health.
{"title":"Probabilistic state–space modeling for robust condition monitoring of industrial equipment","authors":"Victor Vantilborgh, Tom Lefebvre, Guillaume Crevecoeur","doi":"10.1016/j.jmsy.2025.09.016","DOIUrl":"10.1016/j.jmsy.2025.09.016","url":null,"abstract":"<div><div>This paper proposes a generic low-cost method for probabilistic condition monitoring of industrial equipment. A computationally efficient recursive data-driven model is constructed that correlates real-time machine measurements with a quantity or several quantities of interest (QoIs), including artificial metrics such as the Remaining Useful Lifetime (RUL). To that end, a probabilistic state–space model (PSSM) is identified, based on a fully instrumented measurement set obtained from a limited set of experiments that can only be obtained in a specialized testing environment. The dataset contains both cheaply available sensory information as well as prohibitively expensive, invasive or artificially constructed sensory signals. To identify a Maximum Likelihood Estimate of the PSSM, we rely on the Expectation–Maximization (EM) algorithm and Sequential Monte Carlo (SMC) estimation techniques. During operation, only the vital, non-intrusive and cheap sensors are used. The PSSM is then repurposed to reconstruct the costly sensory signals, realizing an effective and general purpose virtual sensor. Our methodology demonstrates the capacity to robustly estimate unmeasured physical variables in real-time and artificially constructed prognostic QoIs, such as the RUL, even when working with an incomplete measurement array. We validate the presented methodology for condition monitoring on the C-MAPSS dataset and a solenoid valve (SV) use case. The presented tool has similar predictive capabilities as compared with other state-of-the-art RUL prognostic methods and furthermore provides uncertainty quantification and contextual information with respect to equipment health.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 505-516"},"PeriodicalIF":14.2,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324717","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 : 2025-10-11DOI: 10.1016/j.jmsy.2025.10.002
Zheng Zhou , Yasong Li , Ruqiang Yan
Modeling degradation processes is essential for estimating industrial system performance and guiding maintenance decisions. Machine learning methods, due to the adaptability for diverse data types, show promise to model temporal evolution of degradation in an embedding space known as latent dynamics, especially for neural ordinary differential equations (NODE) with continuous-time property. However, inferring system states from observation data is an inverse problem, and NODEs often inherit ill-posedness further from their complex optimization landscape. We propose an invertible transfer function informed NODE to ensure stable latent dynamics, making the model robust to perturbations in observation data. First, a NODE describes the hidden degradation process, while an invertible Fourier neural operator maps between latent dynamics and observations. Error analysis reveals that stability is governed by data fidelity and the Lipschitz constant of the inverse mapping, forming the basis for our regularization technique. Additionally, we demonstrate that without ground truth degradation data, latent dynamics lack uniqueness, leading to infinite equivalent solutions. Tests on turbofan engine and battery datasets confirm improved robustness and performance in fault diagnosis and prognosis.
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