Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.     

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions. However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD). Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown. Therefore, to tackle the FGAD problem, this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators. Accordingly, this paper proposes a multivariate time series fine-grained anomaly detection (MFGAD) framework. To avoid excessive fusion of features, MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly. Based on this framework, an algorithm based on Graph Attention Neural Network (GAT) and Attention Convolutional Long-Short Term Memory (A-ConvLSTM) is proposed, in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators. Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.     

Study on dislocation density,microstructure, and mechanical properties of P92 steel for different heat treatment conditions

The impact of various heat treatment procedures on microstructure, dislocation density, hardness, tensile characteristics, and impact toughness of P92 steel was examined in the current experiment. The martensitic microstructure and average microhardness of 463 HV 0.2±8 HV 0.2 of the normalized steel were prevalent. A tempering procedure was carried out at 760 °C for a range of 2 hours to 6 hours. Additionally, an X-ray diffraction examination was carried out, and the results were used to determine the dislocation density. The normalized sample was characterized by a high dislocation density. The dislocation density was decreased by tempering of normalized samples. With an increase in tempering time, the effect of the treatment coarsened the grains, precipitates, and decreased the area fraction of precipitates. After tempering, MX, M₂₃C₆, and M₇C₃ types precipitates were found to have precipitated, according to energy dispersive spectroscopy and x-ray diffraction research. The ideal tempering period was determined to be 4 hours at a tempering temperature of 760 °C based on the microstructure and mechanical characteristics. Steel that was tempered at 760 °C for 4 hours had a yield strength of 472 MPa, an ultimate tensile strength of 668.02 MPa, and an elongation of 26.05 %, respectively.     

Sol-gel processed barium titanate ceramics and thin films

Ferroelectric barium titanate (BaTiO3) ceramics and thin films have been prepared from barium acetate (Ba(CH3COO)2) and titanium (IV) isopropoxied (Ti((CH3)2CHO)4) precursors by a sol–gel technique. The as-grown powder and thin films were found to be amorphous, which crystallized to the tetragonal phase after annealing at 700°C in air for 1 h. Both the ceramics and thin films showed well-saturated polarization–field (P–E) hysteresis loops at room temperature. The value of the spontaneous polarization, PS, remnant polarization, Pr, and coercive field, Ec, of the ceramics and thin films determined from the P–E hysteresis loop were found to be 19.0 and 12.6; 14.0 and 3.2 G cm–2, and 30 and 53 kV cm–1, respectively. The coercive field of the film determined from the capacitance–voltage, C–V, characteristics is slightly lower than that determined from the P–E hysteresis loop (43 kV cm–1). The room-temperature dielectric constant, , of the ceramics and films was found to be 1135 and 370, respectively. Both the films and ceramics showed dielectric anomaly peaks at 125 °C, showing ferroelectric to paraelectric phase transition. © 1998 Kluwer Academic Publishers     

Chromatic aberrations of radial gradient-index lenses. I. Theory

Chromatic effects of radial gradient-index materials have been analyzed, and several important conclusions have been derived in terms of material dispersion data. The use of Buchdahl dispersion data, both for base glass materials and ion-exchange pairs, provides some simple relationships for chromatic aberration and helps in selecting suitable materials for producing achromatic radial gradient-index lenses.     

Chaos in fractional ordered Liu system

Present paper deals with fractional version of a dynamical system introduced by C. Liu, L. Liu and T. Liu [C. Liu, L. Liu, T. Liu, A novel three-dimensional autonomous chaos system, Chaos Solitons Fractals 39 (4) (2009) 1950–1958]. Numerical investigations on the dynamics of this system have been carried out. Properties of the system have been analyzed by means of Lyapunov exponents. Furthermore the minimum effective dimensions have been identified for chaos to exist in commensurate and incommensurate orders. It is noteworthy that the results obtained are consistent with the analytical conditions given in the literature.     

Stability of a class of discrete-time nonlinear recursive observers

This work provides a framework for nominal and robust stability analysis for a class of discrete-time nonlinear recursive observers (DNRO). Given that the system has linear output mapping, local observability and Jacobian matrices satisfying certain conditions, the nominal and robust stability of the DNRO is defined by the property of estimation error dynamics and is analyzed using Lyapunov theory. Moreover, a simultaneous state and parameter estimation scheme is shown to be Input-to-State Stable (ISS), and adaptively reduce plant-model mismatch on-line. Three design strategies of the DNRO that satisfy the stability results are given as examples, including the widely used extended Kalman filter, extended Luenberger observer, and the fixed gain observer.