Journal of Intelligent Manufacturing最新文献

Modular scheduling of tightly coupled production lines. 紧耦合生产线的模块化调度。

IF 7.7 2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2026-01-01 Epub Date: 2026-05-08 DOI: 10.1007/s10845-025-02766-3

Joan Marcè I Igual, Marc Geilen, Mitra Nasri, Twan Basten

{"title":"Modular scheduling of tightly coupled production lines.","authors":"Joan Marcè I Igual, Marc Geilen, Mitra Nasri, Twan Basten","doi":"10.1007/s10845-025-02766-3","DOIUrl":"https://doi.org/10.1007/s10845-025-02766-3","url":null,"abstract":"Optimising productivity of tightly coupled production lines in, for instance, the production printing or semiconductor industry is difficult due to the diversity of products resulting in different product flows, the variety of constraints, and the precise timing required to coordinate multiple tightly coupled machines. A modular setup provides flexibility and cost reduction through reuse of machinery and schedulers. However, the scheduling of a modular production line is challenging as it leads to a distributed decision process where each module has a limited view of the entire system, while local scheduling decisions have a global impact on schedule feasibility. This work proposes a distributed scheduling method for tightly coupled modular sequential production lines where products cannot overtake each other during production. We develop a multi-agent framework in which a system agent propagates timing constraints of the schedulers of different modules. The system agent aims to reach consensus about the handover times of jobs between modules to converge to a globally feasible schedule. The system agent interacts with local agents that interface with local schedulers, allowing the use of existing local schedulers without modification. We illustrate the approach on production lines with multiple re-entrant flow-shop instances with setup times and due dates, which results in a challenging scheduling problem. The performance of the proposed distributed scheduling method is assessed using a monolithic exact scheduler (implemented as a constraint program, not applicable to modular problems) as a reference. It is found to deliver good quality schedules, having only a 1.15 larger makespan on average than the hypothetical optimum provided by the exact scheduler.","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"37 7","pages":"2635-2668"},"PeriodicalIF":7.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148345535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-layer process control in selective laser melting: a reinforcement learning approach. 选择性激光熔化的多层过程控制：一种强化学习方法。

IF 7.4 2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2026-01-01 Epub Date: 2024-12-18 DOI: 10.1007/s10845-024-02548-3

Stylianos Vagenas, Taha Al-Saadi, George Panoutsos

{"title":"Multi-layer process control in selective laser melting: a reinforcement learning approach.","authors":"Stylianos Vagenas, Taha Al-Saadi, George Panoutsos","doi":"10.1007/s10845-024-02548-3","DOIUrl":"https://doi.org/10.1007/s10845-024-02548-3","url":null,"abstract":"Powder bed fusion (PBF) is an original additive manufacturing technique for creating 3D parts layer-by-layer. While there are numerous benefits to this process, the complex undergoing physical phenomena are challenging to analytically model and interpret. Hence, integrated and control-oriented 3D models are lacking in the current literature. As a result, the state of the art in process control for the powder bed fusion (PBF) process is not as advanced as in other manufacturing processes. Reinforcement learning is a machine learning, data-driven mathematical and computational framework that can be used for process control while addressing this challenge (lack of control-oriented models) effectively. Its flexible formulation and its trial-and-error nature make reinforcement learning suitable for processes where the model is intricate or even unknown. The focus of this research work is selective laser melting, which is a laser-based PBF process. For the first time in the literature we demonstrate the benefits of a reinforcement learning process control framework for multiple layers (complete 3D parts) and we highlight the importance of stability during training. The presented case studies confirm the effectiveness of the proposed control framework, directly addressing heat accumulation issues while demonstrating effective overall process control, hence opening up opportunities for further research and impact in this area.","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"37 1","pages":"281-298"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146104969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning nested in multiphysics finite element analysis: application to flash sintering. 嵌套在多物理场有限元分析中的机器学习：在闪烧中的应用。

IF 7.7 2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2026-01-01 Epub Date: 2025-12-05 DOI: 10.1007/s10845-025-02725-y

Ran He, Mingxuan Xia, Peter Polak, Baber Saleem, Xiaoxia Yu, Jingzhe Pan

{"title":"Machine learning nested in multiphysics finite element analysis: application to flash sintering.","authors":"Ran He, Mingxuan Xia, Peter Polak, Baber Saleem, Xiaoxia Yu, Jingzhe Pan","doi":"10.1007/s10845-025-02725-y","DOIUrl":"10.1007/s10845-025-02725-y","url":null,"abstract":"Finite element modelling is a powerful tool for predicting temperature field and deformation in flash sintering. However, conventional finite element modelling approaches often rely on empirical constitutive models, which struggle to capture the highly complex, nonlinear behaviour of materials under flash sintering conditions. To overcome this limitation, this study develops a framework that can train artificial neural networks (ANNs) when nested in multiphysics finite element analysis. By replacing empirical rules with ANNs, the finite element model can learn directly from data and, thus, operate as a digital twin of the flash sintering process. A two-step training strategy is adopted: pretraining of ANNs using synthetic data generated from empirical rules, followed by nested retraining within a commercial finite element package using experimental data. To enable the second-step training, a novel backpropagation algorithm integrated with the Adam optimiser is developed. The results demonstrate a significant improvement in predictive accuracy: the mean squared error between predicted and measured strains decreased from 0.0316 (modified Olevsky's constitutive law) to 0.0034 (ANNs). Beyond improving model fidelity, the approach enables continual adaptation as new data become available and is extensible to all engineering problems governed by differential equations.","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"37 10","pages":"4323-4343"},"PeriodicalIF":7.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13585878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149008423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of the performance of LSTM-DNN models with the consideration of signal complexity in milling processes. 铣削过程中考虑信号复杂度的LSTM-DNN模型性能分析。

IF 7.7 2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2026-01-01 Epub Date: 2025-07-28 DOI: 10.1007/s10845-025-02646-w

Hui Xie, Meng Liu, Ashley Cusack, Guangxian Li, Andrew P Longstaff, Songling Ding, Wencheng Pan

{"title":"Analysis of the performance of LSTM-DNN models with the consideration of signal complexity in milling processes.","authors":"Hui Xie, Meng Liu, Ashley Cusack, Guangxian Li, Andrew P Longstaff, Songling Ding, Wencheng Pan","doi":"10.1007/s10845-025-02646-w","DOIUrl":"10.1007/s10845-025-02646-w","url":null,"abstract":"This paper investigates the impacts of signal complexity on the performance of Deep Learning (DL) models in milling processes, with a particular focus on cutting force due to its critical role in system diagnostics and process monitoring. To compare the Cutter-Workpiece-Engagement (CWE) time-dependent and time-independent processes, signal characterisation was firstly conducted using various feature indicators to quantify signal complexity under different conditions. Multiple DL models were then evaluated through controlled cutting trials to assess how variations in signal complexity affect model performance. The signal analysis revealed that the 14 selected indicators, along with the Recurrent Neural Network-Deep Neural Network (RNN-DNN) models, demonstrated monotonicity as the number of embedded 'active frequencies' increased during the \"Partial Engagement\" or CWE time-dependent process. However, as the depth of the DL models increased, this monotonicity effect diminished. Furthermore, clearer dependencies were observed in the analysis of harmonic prediction performance, particularly with a more pronounced impact on accuracy and uncertainty in the 'Partial Engagement' stage compared to the 'Full Engagement' stage. In conclusion, the complex performance testing of DL models reveals a significant relationship between signal complexity and model performance. This study underscores the importance of incorporating signal complexity analysis as a critical component in applying DL technology within machining processes, as it provides valuable insights into model performance.Graphical abstract: ","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"37 6","pages":"2337-2380"},"PeriodicalIF":7.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13176272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147963961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital twin-enabled real-time control for robot arm-based manufacturing via reinforcement learning 基于强化学习的机械臂制造的数字孪生实时控制

2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2025-11-11 DOI: 10.1007/s10845-025-02728-9

Matsive Ali, Sandesh Giri, Qin Yang, Sen Liu

引用次数: 5

Industrial vision inspection using digital twins: bridging CAD models and realistic scenarios 利用数字双胞胎进行工业视觉检测：连接 CAD 模型与现实场景

IF 8.3 2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2024-09-16 DOI: 10.1007/s10845-024-02485-1

Fangjun Wang, Jianhao Wu, Zhouwang Yang, Yanzhi Song

{"title":"Industrial vision inspection using digital twins: bridging CAD models and realistic scenarios","authors":"Fangjun Wang, Jianhao Wu, Zhouwang Yang, Yanzhi Song","doi":"10.1007/s10845-024-02485-1","DOIUrl":"https://doi.org/10.1007/s10845-024-02485-1","url":null,"abstract":"This study introduces a new industrial visual inspection method that emphasizes the application of computer-aided design (CAD) models. This method significantly reduces the dependence on acquiring and annotating extensive real-scene data, subsequently expediting the development of visual inspection models. The paper highlights two pivotal contributions. Firstly, we introduce a configurable 3D rendering technology that digitally simulates different states of the product, achieving automatic batch generation and labeling of training data. This feature distinguishes our work from existing methods. Secondly, we designed a domain generalization method based on second-order statistics. This approach effectively addresses the domain shift challenge between synthetic and actual production data, enhancing the model’s generalization capabilities. This represents a noteworthy advancement in the field as it boosts the model’s adaptability to real-world scenarios. Our method has demonstrated impressive performance, achieving accuracy rates of 94.30(%), 96.75(%), and 97.35(%) on component model classification, motor defect recognition, and rotating motor brush holder datasets, respectively. These results not only validate the efficacy of our domain generalization method but also underscore the potential of using CAD model data for industrial visual inspection. In summary, our research has created a new method for integrating industrial visual inspection into digital twin ecosystems, highlighting the potential for significant improvements in this field.","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"186 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142247392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability-improved machine learning model using knowledge-embedded learning approach for smart manufacturing 利用知识嵌入式学习方法改进用于智能制造的机器学习模型的可靠性

IF 8.3 2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2024-09-09 DOI: 10.1007/s10845-024-02482-4

Farzam Farbiz, Saurabh Aggarwal, Tomasz Karol Maszczyk, Mohamed Salahuddin Habibullah, Brahim Hamadicharef

{"title":"Reliability-improved machine learning model using knowledge-embedded learning approach for smart manufacturing","authors":"Farzam Farbiz, Saurabh Aggarwal, Tomasz Karol Maszczyk, Mohamed Salahuddin Habibullah, Brahim Hamadicharef","doi":"10.1007/s10845-024-02482-4","DOIUrl":"https://doi.org/10.1007/s10845-024-02482-4","url":null,"abstract":"Machine learning models play a crucial role in smart manufacturing by revolutionizing industrial automation so as to boost productivity and product quality. However, the reliability of these models often faces challenges from factors such as data drift, concept drift, adversarial attacks, and increasing model complexity. In addressing these challenges, this paper proposes a novel approach called Reliability Improved Machine Learning (RIML), which leverages on prior knowledge by incorporating it into the machine learning pipeline through a secondary output that is easily verifiable and assessable within the application domain. Built upon the Knowledge-embedded Machine Learning (KML) framework, RIML differs from conventional strategies by modifying the model’s architecture. In its implementation, additional layers were introduced, specifically designed to identify and discard misclassified cases to improve the model’s reliability. RIML’s efficacy was successfully demonstrated through a simulated dataset and three real use-case studies, namely, a general walk/run scenario, an industry-related case using metro railway dataset, and a smart manufacturing application on gas detection. The promising results highlighted RIML’s ability to significantly reduce misclassifications, thereby enhancing model reliability in diverse real-world scenarios.","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"283 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart scheduling for next generation manufacturing systems: a systematic literature review 下一代制造系统的智能调度：系统文献综述

IF 8.3 2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2024-09-09 DOI: 10.1007/s10845-024-02484-2

Shriprasad Chorghe, Rishi Kumar, Makarand S. Kulkarni, Vibhor Pandhare, Bhupesh Kumar Lad

{"title":"Smart scheduling for next generation manufacturing systems: a systematic literature review","authors":"Shriprasad Chorghe, Rishi Kumar, Makarand S. Kulkarni, Vibhor Pandhare, Bhupesh Kumar Lad","doi":"10.1007/s10845-024-02484-2","DOIUrl":"https://doi.org/10.1007/s10845-024-02484-2","url":null,"abstract":"In the current scenario, smart scheduling has become an essential requirement to generate dynamic schedules, prescribe, and adjust scheduling plans in response to dynamic events such as machine failures, unpredictable demand, customer order cancellations, worker unavailability, and mass customization. Such scheduling techniques must also take advantage of intelligence continuously being built for next-generation manufacturing systems. This study presents a systematic literature review on smart scheduling, analysing 123 identified literature from 2010 to May 2024 using the PRISMA technique. The analysis includes scientometric and content analysis to identify paradigm shifts in development (concepts, methodologies, practices) along with their maturity levels, and provides recommendations for the next generation of smart scheduling. This study is significant for advancing knowledge and addressing current and future needs/requirements in smart scheduling. This would serve as a reference in understanding the maturity status of various developments, assist researchers and practitioners in identifying research gaps, and direct future advancements in the smart scheduling domain.","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"59 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An overview of traditional and advanced methods to detect part defects in additive manufacturing processes 快速成型制造工艺中检测零件缺陷的传统和先进方法概述

IF 8.3 2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2024-09-02 DOI: 10.1007/s10845-024-02483-3

Vivek V. Bhandarkar, Harshal Y. Shahare, Anand Prakash Mall, Puneet Tandon

{"title":"An overview of traditional and advanced methods to detect part defects in additive manufacturing processes","authors":"Vivek V. Bhandarkar, Harshal Y. Shahare, Anand Prakash Mall, Puneet Tandon","doi":"10.1007/s10845-024-02483-3","DOIUrl":"https://doi.org/10.1007/s10845-024-02483-3","url":null,"abstract":"Additive manufacturing (AM) or 3-dimensional (3D) printing processes have been adopted in several industrial sectors including aerospace, automotive, medical, architecture, arts and design, food, and construction for the past few decades due to their numerous advantages over other conventional subtractive manufacturing processes. However, some flaws and defects associated with 3D-printed components hinder its extensive adoption in industries. Therefore, real-time detection and elimination of these defects by analyzing the defects-causing process parameters is very important to obtain a defect-free final component. While global efforts are in progress to develop defect detection techniques with the rise of Industry 4.0, there is still a limited scope of comprehensive research that encapsulates various defect detection techniques in the AM sector on a global scale. Thus, this systematic review explores defects in parts manufactured via metallic and non-metallic AM processes. It covers traditional defect detection methods and extends to recent advanced machine learning (ML) and deep learning (DL) based techniques. The paper also delves into challenges associated with the implementation of ML and DL approaches for defect detection, providing a comprehensive understanding of the current state and future directions in AM research.","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"40 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic multi-layer cognitive model for intelligent machine tool 用于智能机床的系统化多层认知模型

IF 8.3 2区工程技术

Journal of Intelligent Manufacturing Pub Date : 2024-08-30 DOI: 10.1007/s10845-024-02481-5

Tengyuan Jiang, Jingtao Zhou, Xiang Luo, Mingwei Wang, Shusheng Zhang

{"title":"A systematic multi-layer cognitive model for intelligent machine tool","authors":"Tengyuan Jiang, Jingtao Zhou, Xiang Luo, Mingwei Wang, Shusheng Zhang","doi":"10.1007/s10845-024-02481-5","DOIUrl":"https://doi.org/10.1007/s10845-024-02481-5","url":null,"abstract":"As the basic manufacturing capabilities provide unit of the production system, the intelligent level of the CNC machine tool will affect the realization of intelligent manufacturing. Academia has carried out a lot of intelligent research on CNC machine tool from technical perspective, but there still needs a systematic cognitive model to promote the construction of cognitive abilities, to support the intelligent realization and continuous improvement of CNC machine tool. Therefore, this paper proposes a three-part, seven-layer cognitive model based on cognitive informatics to promote the construction of cognitive abilities and the intelligent transformation of CNC machine tool. Firstly, a systematic multi-layer cognitive model is proposed, and each cognitive layer is introduced to promote the different cognitive abilities construction of CNC machine tool. Then, this paper introduces the cognitive analysis loop and the cognitive learning loop contained in the multi-layer cognitive model, which can promote the construction of the adaptive and continuous learning abilities of CNC machine tool. The evaluation indicators of the intelligence machine tool are given, which is used to evaluate machine tool intelligence model. Furthermore, the cognitive enabling technologies of the multi-layer cognitive model for intelligent machine tool is presented, which supports the realization of cognitive abilities such as analysis, decision making, and learning. Finally, the feasibility of the proposed systematic multi-layer cognitive model is verified by the developed computable digital twin platform and comparison before and after implementation for intelligent machine tool.","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0