Journal of Intelligent Manufacturing最新文献

{"title":"Shapley-based explainable AI for clustering applications in fault diagnosis and prognosis","authors":"Joseph Cohen, Xun Huan, Jun Ni","doi":"10.1007/s10845-024-02468-2","DOIUrl":"https://doi.org/10.1007/s10845-024-02468-2","url":null,"abstract":"<p>Data-driven artificial intelligence models require explainability in intelligent manufacturing to streamline adoption and trust in modern industry. However, recently developed explainable artificial intelligence (XAI) techniques that estimate feature contributions on a model-agnostic level such as SHapley Additive exPlanations (SHAP) have not yet been evaluated for semi-supervised fault diagnosis and prognosis problems characterized by class imbalance and weakly labeled datasets. This paper explores the potential of utilizing Shapley values for a new clustering framework compatible with semi-supervised learning problems, loosening the strict supervision requirement of current XAI techniques. This broad methodology is validated on two case studies: a heatmap image dataset obtained from a semiconductor manufacturing process featuring class imbalance, and the benchmark N-CMAPSS dataset. Semi-supervised clustering based on Shapley values significantly improves upon clustering quality compared to the fully unsupervised case, deriving information-dense and meaningful clusters that relate to underlying fault diagnosis model predictions. These clusters can also be characterized by high-precision decision rules in terms of original feature values, as demonstrated in the second case study. The rules, limited to 2 terms utilizing original feature scales, describe 14 out of the 19 derived equipment failure clusters with average precision exceeding 0.85, showcasing the promising utility of the explainable clustering framework for intelligent manufacturing applications.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"21 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870825","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}

{"title":"Edge-fog-cloud hybrid collaborative computing solution with an improved parallel evolutionary strategy for enhancing tasks offloading efficiency in intelligent manufacturing workshops","authors":"Zhiwen Lin, Zhifeng Liu, Yueze Zhang, Jun Yan, Shimin Liu, Baobao Qi, Kaien Wei","doi":"10.1007/s10845-024-02463-7","DOIUrl":"https://doi.org/10.1007/s10845-024-02463-7","url":null,"abstract":"<p>In intelligent manufacturing workshops, the lack of an efficient collaborative mechanism among the various computational resources leads to higher latency, increased costs, and uneven computational load distribution, compromising the response efficacy of intelligent manufacturing services. To address these challenges, this paper introduces an edge-fog-cloud hybrid collaborative computing architecture (EFCHC) that enhances the interaction among multi-layer computational resources. Furthermore, the computational tasks offloading model under EFCHC is formulated to minimize objectives such as latency and cost. To refine the offloading solution, a novel multi-group parallel evolutionary strategy is proposed, which includes a two-stage pre-allocation scheme and a hyper-heuristic evolutionary operator for effective solution identification. In multi-objective benchmark testing experiments, the proposed algorithm substantially outperforms other comparative algorithms in terms of accuracy, convergence, and stability. In simulated workshop scenarios, the proposed offloading strategy reduces the total computational latency and cost by 17.81% and 21.89%, and enhances the load balancing efficiency by up to 52.50%, compared to six typical benchmark algorithms and architectures.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"39 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774144","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}

{"title":"Deformation prediction model for hollow thin-walled aluminum alloy structural parts under multiple load-sequence coupling conditions","authors":"Jiaheng Ma, Shengfang Zhang, Fujian Ma, Xiuying Song, Ziguang Wang, Zhihua Sha","doi":"10.1007/s10845-024-02464-6","DOIUrl":"https://doi.org/10.1007/s10845-024-02464-6","url":null,"abstract":"<p>Hollow thin-walled aluminum alloy structural parts (HTWASP) must be cut after welding during the actual processing process, and a welding stress field is inevitably generated owing to the welding heat effect in the process of welding the workpiece, leading to distortion of workpiece. To accurately and efficiently predict the deformation of HTWASP after multiprocess processing, equivalent load caused by process of welding was computed through connection inherent strain theory with welding thermal parameters. A dynamic simulation model of the milling process of HTWASP was established by welding equivalent load. The influences of spindle speed and tool diameter on the deformation remain stress of structural workpieces were analyzed. Additionally, the simulated values for the deformation of the workpiece were compared and analyzed through milling tests on these structural parts. The results showed that the range of stress values and stress effects was smaller when the spindle speed was higher. The distance of the stress effect was the smallest when machine speed was 2500 rpm and tool diameter was 20 mm. Milling stress value was the smallest when machine speed was 2500 rpm and tool diameter was 6 mm. Most of the deformation occurred in the hollow position of the upper and diagonal plate; in contrast, the distortion of vertical plate and weld seam was not significant. The minimum deformation was 0.501 mm at machine speed is 2500 rpm and tool diameter is 6 mm. In the non-high-speed cutting state, high speed reduced workpiece quality of aluminum alloy workpiece, and slot milling quality was the best when machine speed was 1000 r/min and tool diameter was 6 mm. The proposed model sequentially couples the welding and milling process loads, and a multiprocess deformation prediction model that increasingly conforms to the actual processing sequence is constructed, providing a reference for the high-precision and efficient prediction of the multiprocess deformation in hollow thin-walled structural parts.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"67 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774145","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}

{"title":"CNC linear axis condition-based monitoring: a statistics-based framework to establish a baseline dataset and case study","authors":"Andres Hurtado Carreon, Jose Mario DePaiva, Rohan Barooah, Stephen C. Veldhuis","doi":"10.1007/s10845-024-02461-9","DOIUrl":"https://doi.org/10.1007/s10845-024-02461-9","url":null,"abstract":"<p>The linear axis of computer numerical control (CNC) machines is a critical subsystem that provides precise position capabilities. The unexpected failure of its components may lead to part quality issues and machine breakdowns. Therefore, it is crucial to examine and understand its healthy condition when newly commissioned or repaired so that it can be used as a reference when monitoring its operational health. In this paper, a framework to establish a baseline reference dataset is proposed utilizing vibration monitoring and time domain statistical feature analysis. The framework was applied as a case study in a newly commissioned linear axis testbed. The results demonstrated that a linear axis under a known healthy condition exhibits low variability of its time domain features, negligible difference between forward and reverse stroke directions and a robust baseline dataset can be established by collecting data for approximately an hour of operation instead of a full day of operation (6 h of operation).</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"69 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774146","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}

{"title":"Dynamic scenario-enhanced diverse human motion prediction network for proactive human–robot collaboration in customized assembly tasks","authors":"Pengfei Ding, Jie Zhang, Pai Zheng, Peng Zhang, Bo Fei, Ziqi Xu","doi":"10.1007/s10845-024-02462-8","DOIUrl":"https://doi.org/10.1007/s10845-024-02462-8","url":null,"abstract":"<p>Human motion prediction is crucial for facilitating human–robot collaboration in customized assembly tasks. However, existing research primarily focuses on predicting limited human motions using static global information, which fails to address the highly stochastic nature of customized assembly operations in a given region. To address this, we propose a dynamic scenario-enhanced diverse human motion prediction network that extracts dynamic collaborative features to predict highly stochastic customized assembly operations. In this paper, we present a multi-level feature adaptation network that generates information for dynamically manipulating objects. This is accomplished by extracting multi-attribute features at different levels, including multi-channel gaze tracking, multi-scale object affordance detection, and multi-modal object’s 6 degree-of-freedom pose estimation. Notably, we employ gaze tracking to locate the collaborative space accurately. Furthermore, we introduce a multi-step feedback-refined diffusion sampling network specifically designed for predicting highly stochastic customized assembly operations. This network refines the outcomes of our proposed multi-weight diffusion sampling strategy to better align with the target distribution. Additionally, we develop a feedback regulatory mechanism that incorporates ground truth information in each prediction step to ensure the reliability of the results. Finally, the effectiveness of the proposed method was demonstrated through comparative experiments and validation of assembly tasks in a laboratory environment.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"47 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740925","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}

{"title":"Data-driven linear quadratic tracking based temperature control of a big area additive manufacturing system","authors":"Eleni Zavrakli, Andrew Parnell, Andrew Dickson, Subhrakanti Dey","doi":"10.1007/s10845-024-02428-w","DOIUrl":"https://doi.org/10.1007/s10845-024-02428-w","url":null,"abstract":"<p>Designing efficient closed-loop control algorithms is a key issue in Additive Manufacturing (AM), as various aspects of the AM process require continuous monitoring and regulation, with temperature being a particularly significant factor. Here we study closed-loop control for the temperatures in the extruder of a Material Extrusion AM system, specifically a Big Area Additive Manufacturing (BAAM) system. Previous approaches for temperature control in AM either require the knowledge of exact model parameters, or involve discretisation of the state and action spaces to employ traditional data-driven control techniques. On the other hand, modern algorithms that can handle continuous state and action space problems require a large number of hyperparameter tuning to ensure good performance. In this work, we circumvent the above limitations by making use of a state space temperature model while focusing on both model-based and data-driven methods. We adopt the Linear Quadratic Tracking (LQT) framework and utilise the quadratic structure of the value function in the model-based analytical solution to produce a data-driven approximation formula for the optimal controller. We demonstrate these approaches using a simulator of the temperature evolution in the extruder of a BAAM system and perform an in-depth comparison of the performance of these methods. We find that we can learn an effective controller using solely simulated input–output process data. Our approach achieves parity in performance compared to model-based controllers and so lessens the need for estimating a large number of parameters of the often intricate and complicated process model. We believe this result is an important step towards achieving autonomous intelligent manufacturing.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"13 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740923","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}

{"title":"Comparative analysis of different machine vision algorithms for tool wear measurement during machining","authors":"Mayur A. Makhesana, Prashant J. Bagga, Kaushik M. Patel, Haresh D. Patel, Aditya Balu, Navneet Khanna","doi":"10.1007/s10845-024-02467-3","DOIUrl":"https://doi.org/10.1007/s10845-024-02467-3","url":null,"abstract":"<p>Automatic tool condition monitoring becomes crucial in metal cutting because tool wear impacts the final product’s quality. The optical microscope approach for assessing tool wear is offline, time-consuming, and subject to measurement error by humans. To accomplish this, the machine must be stopped, and the tool must be removed, which causes downtime. As a result, numerous research attempts have been made to develop robust systems for direct tool wear measurement during machining. Therefore, the proposed work focused on developing a direct tool condition monitoring system using machine vision to calculate tool wear parameters, specifically flank wear. The cutting tool insert images are collected using a machine vision setup equipped with an industrial camera, bi-telecentric lens, and a proper illumination system during the machining of AISI 4140 steel. The comparative analysis of image processing algorithms for tool wear measurement is proposed under the selected machining environment. The wear boundary is extracted using digital image processing tools such as image enhancement, image segmentation, image morphology operation, and edge detection. The wear amount on the tool insert is extracted and recorded using the Hough line transformation function and pixel scanning. The comparison of results revealed the measurement accuracy and repeatability of the proposed image processing algorithm with a maximum of 6.25% and minimum of 1.10% error compared to manual measurement. Hence, the proposed approach eliminates manual measurements and improves the machining productivity.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"151 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740936","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}

{"title":"Design patterns of deep reinforcement learning models for job shop scheduling problems","authors":"Shiyong Wang, Jiaxian Li, Qingsong Jiao, Fang Ma","doi":"10.1007/s10845-024-02454-8","DOIUrl":"https://doi.org/10.1007/s10845-024-02454-8","url":null,"abstract":"<p>Production scheduling has a significant role when optimizing production objectives such as production efficiency, resource utilization, cost control, energy-saving, and emission reduction. Currently, deep reinforcement learning-based production scheduling methods achieve roughly equivalent precision as the widely used meta-heuristic algorithms while exhibiting higher efficiency, along with powerful generalization abilities. Therefore, this new paradigm has drawn much attention and plenty of research results have been reported. By reviewing available deep reinforcement learning models for the job shop scheduling problems, the typical design patterns and pattern combinations of the common components, i.e., agent, environment, state, action, and reward, were identified. Around this essential contribution, the architecture and procedure of training deep reinforcement learning scheduling models and applying resultant scheduling solvers were generalized. Furthermore, the key evaluation indicators were summarized and the promising research areas were outlined. This work surveys several deep reinforcement learning models for a range of production scheduling problems.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"18 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740926","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}

{"title":"A comparison study on anomaly detection methods in manufacturing process monitoring with X-ray images","authors":"Congfang Huang, David Blondheim, Shiyu Zhou","doi":"10.1007/s10845-024-02435-x","DOIUrl":"https://doi.org/10.1007/s10845-024-02435-x","url":null,"abstract":"<p>Process monitoring and anomaly detection in manufacturing production systems are of vital importance in the consistency, reliability, and quality of manufacturing operations, which motivates the development of a large number of anomaly detection methods. In this work, a comparison study on representative unsupervised X-ray image based anomaly detection methods is conducted. Statistical, physical, and deep learning based dimension reduction methods and different anomaly detection criteria are considered and compared. Case studies on real-world X-ray image data with simulated anomalies and real anomalies are both carried out. Grey level co-occurrence matrix with Hotelling <span>(T^2)</span> statistic as detection criteria achieves the best performance on the simulated anomalies case with an overall detection accuracy of 96%. Principal component analysis with reconstruction error as criteria obtains the highest detection rate of 90.6% in the real anomalies case. Considering the ever-growing availability of image data in intelligent manufacturing processes, this study will provide very useful knowledge and find a broad audience.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"83 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740927","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}

{"title":"Multi-channel anomaly detection using graphical models","authors":"Bernadin Namoano, Christina Latsou, John Ahmet Erkoyuncu","doi":"10.1007/s10845-024-02447-7","DOIUrl":"https://doi.org/10.1007/s10845-024-02447-7","url":null,"abstract":"<p>Anomaly detection in multivariate time-series data is critical for monitoring asset conditions, enabling prompt fault detection and diagnosis to mitigate damage, reduce downtime and enhance safety. Existing literature predominately emphasises temporal dependencies in single-channel data, often overlooking interrelations between features in multivariate time-series data and across multiple channels. This paper introduces G-BOCPD, a novel graphical model-based annotation method designed to automatically detect anomalies in multi-channel multivariate time-series data. To address internal and external dependencies, G-BOCPD proposes a hybridisation of the graphical lasso and expectation maximisation algorithms. This approach detects anomalies in multi-channel multivariate time-series by identifying segments with diverse behaviours and patterns, which are then annotated to highlight variations. The method alternates between estimating the concentration matrix, which represents dependencies between variables, using the graphical lasso algorithm, and annotating segments through a minimal path clustering method for a comprehensive understanding of variations. To demonstrate its effectiveness, G-BOCPD is applied to multichannel time-series obtained from: (i) Diesel Multiple Unit train engines exhibiting faulty behaviours; and (ii) a group of train doors at various degradation stages. Empirical evidence highlights G-BOCPD's superior performance compared to previous approaches in terms of precision, recall and F1-score.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"55 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141608987","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}