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Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting machine tool
摘要： Selective laser melting has become one of the most current new technologies used to produce complex components in comparison to conventional manufacturing technologies. Especially, existing selective laser melting machine tools are not equipped with analytics tools that evaluate sensor data. This paper describes an approach to analyze and visualize offline data from different sources based on machine learning algorithms. Data from three sensors were utilized to identify clusters. They illustrate the normal operation of the machine tool and three faulty conditions. With these results, a condition monitoring system can be implemented that enables those machine tools for predictive maintenance solutions.
关键词： clustering,machine learning,Selective laser melting,sensor data,predictive maintenance
更新于2025-09-12 10:27:22
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Atom-Varied Side Chains in Conjugated Polymers Affect Efficiencies of Photovoltaic Devices Incorporating Small Molecules
摘要： We synthesized three conjugated polymers involving sulfur(S)-inserted and chlorine(Cl)-substituted side chains of the parent two-dimensional conjugated polymer—poly(benzodithiophene-thiophene-benzooxadiazole) (BO)—to form S-inserted (BO2S), Cl-substituted (BO2Cl) and S-inserted with Cl-substituted (BO2S2Cl) polymers for tuning their surface energies and, thus, interaction with IT-4F small molecule in their binary blends. In the BO:IT-4F blend, the bulk heterojunction (BHJ) structure was constituted mainly from the long-rod BO domains along with a few IT-4F domains that dispersed well in the blend; in contrast, favorable networks for charge transport existed in the BO2S or BO2S2Cl with IT-4F blend. The disk sizes of IT-4F in the BO2S2Cl:IT-4F blend were larger than that in the BO2S:IT-4F blend (23.3 vs. 18.1 nm). As the extent of atom variation increased from BO to BO2S to BO2S2Cl, the induced IT-4F crystallinity increased, and the orientation of molecular packing of the polymer varied. The highest PCE (12.06%) was that for the device based on the double sulfur-inserted/chlorine-substituted side chain polymer and IT-4F acceptor (BO2S2Cl:IT-4F), owing to the more balanced hole-to-electron mobility, being consistent with the value predicted (11.8%) using the random forest machine learning model. This study not only provides insight into the photovoltaic performance of the polymers with atom-inserted or -substituted side chain but also reveals that the effects of molecular packing.
关键词： organic photovoltaics,small molecule acceptor,machine learning,packing orientation,atom-varied polymer side chain
更新于2025-09-12 10:27:22
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Customizing Supercontinuum Generation Via Adaptive On-Chip Pulse Splitting
摘要： Modern optical systems increasingly rely on complex physical processes. Advanced light sources, such as supercontinuum (SC) [1], are highly sought for imaging and metrology, and are based on nonlinear dynamics where the output properties must often finely match target performance characteristics. However, in these systems, the availability of control parameters and the means to adjust them in a versatile manner are usually limited. Moreover, finding the ideal parameters for a specific application can become inherently complex. Here, we use an actively-controlled photonic chip to prepare and manipulate patterns of femtosecond optical pulses seeding supercontinuum generation [1]. Taking advantage of machine learning concepts [2], we exploit this access to an enhanced and tunable parameter space and experimentally demonstrate the customization of nonlinear interactions responsible for tailoring supercontinuum properties [3].
关键词： femtosecond optical pulses,supercontinuum generation,photonic chip,machine learning,nonlinear dynamics
更新于2025-09-12 10:27:22
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Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy
摘要： In this article, we address the problem of the classification of the health state of the colon’s wall of mice, possibly injured by cancer with machine learning approaches. This problem is essential for translational research on cancer and is a priori challenging since the amount of data is usually limited in all preclinical studies for practical and ethical reasons. Three states considered including cancer, health, and inflammatory on tissues. Fully automated machine learning-based methods are proposed, including deep learning, transfer learning, and shallow learning with SVM. These methods addressed different training strategies corresponding to clinical questions such as the automatic clinical state prediction on unseen data using a pre-trained model, or in an alternative setting, real-time estimation of the clinical state of individual tissue samples during the examination. Experimental results show the best performance of 99.93% correct recognition rate obtained for the second strategy as well as the performance of 98.49% which were achieved for the more difficult first case.
关键词： classification,confocal laser endomicroscopy,machine learning,cancer study,health state,mice colon
更新于2025-09-12 10:27:22
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Extreme Events Prediction in Optical Fibre Modulation Instability using Machine Learning
摘要： The study of instabilities that drive extreme events is central to nonlinear science. One of the most celebrated example of nonlinear instability is modulation instability (MI) which describes the exponential amplification of noise on top of an input signal. When seeded by noise, MI has been shown to be associated with the emergence of high intensity localized temporal breathers with random statistics and it has also been suggested that MI may be linked to the formation of extreme events or rogue waves [1]. Real-time techniques such as the dispersive Fourier transform (DFT) are commonly used to measure ultrafast instabilities [2]. Although conceptually simple and easy to implement, the DFT only provides spectral information, limiting the knowledge of associated temporal properties. Here, we show how machine learning can overcome this restriction to study time-domain properties of optical fibre modulation instability based only on spectral intensity measurements. Specifically, we train a supervised neural network (NN) to correlate the spectral and temporal properties of modulation instability using numerical simulations, and then we apply the neural network model to analyse high dynamic range experimental MI spectra to yield the probability distribution for the highest temporal peaks in the instability field [3].
关键词： modulation instability,optical fibre,machine learning,neural network,extreme events
更新于2025-09-12 10:27:22
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A Machine‐Learning Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells
摘要： Organic solar cells (OSCs) based on ternary blend active layers are among the most promising photovoltaic technologies. To further improve the power conversion efficiency (PCE), the materials selection criteria must be focused on achieving high open-circuit voltage (Voc) through the alignment of the energy levels of the ternary blend active layers. Hence, machine-learning approaches are in high demand for extracting the complex correlation between Voc and the energy levels of the ternary blend active layers, which are crucial to facilitate device design. In the present work, the data-driven strategies are used to generate a model based on the available experimental data and the Voc are then predicted using available machine-learning methods (Random Forest regression and Support Vector regression). In addition, the Random Forest regression is compared with Support Vector regression to demonstrate the superiority of Random Forest regression for Voc prediction. The Random Forest regression is then developed to find the appropriate energy level alignment of ternary OSCs and to reveal the relationship between Voc and electronic features. Finally, an analysis based on the ranking of variables in terms of importance by the Random Forest model is performed to identify the key feature governing the Voc and the performance of ternary OSCs. From the perspective of device design, the machine-learning approach provides sufficient insights to improve the VOC and advances the comprehensive understanding of ternary OSCs.
关键词： organic field-effect transistors,Machine-learning,charge transport mobility.
更新于2025-09-12 10:27:22
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Optical Fiber Specklegram Sensors for Mechanical Measurements: a Review
摘要： This paper presents a review of FSS technology. The operation principle and main characteristics of specklegram are presented and the applications of FSSs are thoroughly discussed. In addition, advances in microelectronics, machine learning, material processing for new optical fibers and their relation with FSS are also discussed. These new developments on correlated areas can give rise for a new generation of sensors with advantageous features such as lower cost, easy connectivity, higher degree of customization and better performance.
关键词： Optical fiber sensors,Fiber specklegram sensors,Speckle metrology,Machine learning
更新于2025-09-11 14:15:04
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Color tunable carbon quantum dots from wasted paper by different solvents for anti-counterfeiting and fluorescent flexible film
摘要： Forecasting the structural stability of hybrid organic/inorganic compounds, where polyatomic molecules replace atoms, is a challenging task; the composition space is vast, and the reference structure for the organic molecules is ambiguously defined. In this work, we use a range of machine-learning algorithms, constructed from state-of-the-art density functional theory data, to conduct a systematic analysis on the likelihood of a given cation to be housed in the perovskite structure. In particular, we consider both ABC3 chalcogenide (I?V?VI3) and halide (I?II?VII3) perovskites. We find that the effective atomic radius and the number of lone pairs residing on the A-site cation are sufficient features to describe the perovskite phase stability. Thus, the presented machine-learning approach provides an efficient way to map the phase stability of the vast class of compounds, including situations where a cation mixture replaces a single A-site cation. This work demonstrates that advanced electronic structure theory combined with machine-learning analysis can provide an efficient strategy superior to the conventional trial-and-error approach in materials design.
关键词： hybrid organic/inorganic compounds,perovskite,density functional theory,machine-learning,phase stability
更新于2025-09-11 14:15:04
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A cybermanufacturing and AI framework for laser powder bed fusion (LPBF) additive manufacturing process
摘要： In Laser Powder Bed Fusion (LPBF) along, more than 50 process parameters are known to affect print quality. The current state-of-the-art practice in process control only considers a small fraction of them – mainly on laser power and scanning speed affecting temperature gradient and geometry of a melting pool. This letter proposes a system-wide platform involving various machine learning principles and leveraging production data stored in the cloud. The proposed framework aims to identify process parameters that may affect print quality so that a viable process control strategy can be formulated.
关键词： Laser powder bed fusion,Metal 3D printing,Machine learning,Process monitoring
更新于2025-09-11 14:15:04
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Machine learning analysis on stability of perovskite solar cells
摘要： In this work, a dataset containing long-term stability data for 404 organolead halide perovskite cells was constructed from 181 published papers and analyzed using machine-learning tools of association rule mining and decision trees; the effects of cell manufacturing materials, deposition methods and storage conditions on cell stability were investigated. For regular cells, mixed cation perovskites, multi-spin coating as one-step deposition, DMF t DMSO as precursor solution and chlorobenzene as anti-solvent were found to have positive effects on stability; SnO2 as ETL compact layer, PCBM as second ETL, inorganic HTLs or HTL-free cells, LiTFSI t TBP t FK209 and F4TCNQ as HTL additives and carbon as back contact were also found to improve stability. The cells stored under low humidity were found to be more stable as expected. The degradation was slightly faster in inverted cells under humid condition; the use of some materials (like mixed cation perovskites, PTAA and NiOx as HTL, PCBM t C60 as ETL, and BCP interlayer) were found to result in more stable cells.
关键词： Perovskite solar cells,Knowledge extraction,Machine learning,Association rule mining,Data mining,Stability
更新于2025-09-11 14:15:04