Determination of the SSM Processing Window

Identification of critical temperatures is paramount for semisolid processing. Application of the principles of differential calculus to identify these temperatures on semisolid transformation curves allows the semisolid metal (SSM) processing window to be determined. This paper synthesizes and organizes a methodology that can be used to this end, namely the differentiation method (DM). Examples are given of the application of the method to 356, 355, and 319 aluminum alloys, which are commonly used in SSM processing, and the results are compared with those of numerical simulations performed with Thermo-Calc® (under the Scheil condition). The DM is applied to experimental differential scanning calorimetry (DSC) heat-flow data for cooling and heating cycles under different kinetic conditions (5, 10, 15, 20, and 25 °C/min). The findings indicate that the DM is an efficient tool for identifying critical points such as the solidus, liquidus, and knee as well as tertiary transformations. The results obtained using the method agree well with those obtained using traditional techniques. The method is operator-independent as it uses well-defined mathematical/graphical criteria to identify critical points. Furthermore, the DM identifies an SSM processing window defined in terms of a higher and lower temperature for rheocasting or thixoforming operations (TSSML and TSSMH) between which the sensitivity is less than 0.03 °C-1 and, consequently, the process is highly controllable. This DM has already been published in a partial and dispersed way in different works in the past and the aim here is to present it in a more cohesive and didactic way, synthesizing the presented data and comparing them.

Denoising and Bad Data Detection in Distribution Phasor Measurements using Filtering, Clustering and Koopman Mode Analysis

Distribution-level phasor measurement units (D-PMU) data are prone to different types of anomalies given complex data flow and processing infrastructure in an active power distribution system with enhanced digital automation. It is essential to pre-process the data before being used by critical applications for situational awareness and control. In this work, two approaches for detection of data anomalies are introduced for offline (larger data processing window) and online (shorter data processing window) applications. A margin-based maximum likelihood estimator (MB-MLE) method is developed to detect anomalies by integrating the results of different base detectors including Hampel filter, Quartile detector and DBSCAN. A smoothing wavelet denoising method is used to remove high-frequency noises. The processed data with offline analysis is used to fit a model to the underlying dynamics of synchrophasor data using Koopman Mode Analysis, which is subsequently employed for online denoising and bad data detection (BDD) using Kalman Filter (KF). The parameters of the KF are adjusted adaptively based on similarity to the training data set for model fitting purposes. Developed techniques have been validated for the modified IEEE test system with multiple D-PMUs, modeled and simulated in real-time for different case scenarios using the OPAL-RT Hardware-In-the-Loop (HIL) Simulator.

Denoising and Bad Data Detection in Distribution Phasor Measurements using Filtering, Clustering and Koopman Mode Analysis

Distribution-level phasor measurement units (D-PMU) data are prone to different types of anomalies given complex data flow and processing infrastructure in an active power distribution system with enhanced digital automation. It is essential to pre-process the data before being used by critical applications for situational awareness and control. In this work, two approaches for detection of data anomalies are introduced for offline (larger data processing window) and online (shorter data processing window) applications. A margin-based maximum likelihood estimator (MB-MLE) method is developed to detect anomalies by integrating the results of different base detectors including Hampel filter, Quartile detector and DBSCAN. A smoothing wavelet denoising method is used to remove high-frequency noises. The processed data with offline analysis is used to fit a model to the underlying dynamics of synchrophasor data using Koopman Mode Analysis, which is subsequently employed for online denoising and bad data detection (BDD) using Kalman Filter (KF). The parameters of the KF are adjusted adaptively based on similarity to the training data set for model fitting purposes. Developed techniques have been validated for the modified IEEE test system with multiple D-PMUs, modeled and simulated in real-time for different case scenarios using the OPAL-RT Hardware-In-the-Loop (HIL) Simulator.

A Hydrophobic Derivative of Ciprofloxacin as a New Photoinitiator of Two-Photon Polymerization: Synthesis and Usage for the Formation of Biocompatible Polylactide-Based 3D Scaffolds

A hydrophobic derivative of ciprofloxacin, hexanoylated ciprofloxacin (CPF-hex), has been used as a photoinitiator (PI) for two-photon polymerization (2PP) for the first time. We present, here, the synthesis of CPF-hex and its application for 2PP of methacrylate-terminated star-shaped poly (D,L-lactide), as well a systematic study on the optical, physicochemical and mechanical properties of the photocurable resin and prepared three-dimensional scaffolds. CPF-hex exhibited good solubility in the photocurable resin, high absorption at the two-photon wavelength and a low fluorescence quantum yield = 0.079. Structuring tests showed a relatively broad processing window and revealed the efficiency of CPF-hex as a 2PP PI. The prepared three-dimensional scaffolds showed good thermal stability; thermal decomposition was observed only at 314 °C. In addition, they demonstrated an increase in Young’s modulus after the UV post-curing (from 336 ± 79 MPa to 564 ± 183 MPa, which is close to those of a cancellous (trabecular) bone). Moreover, using CPF-hex as a 2PP PI did not compromise the scaffolds’ low cytotoxicity, thus they are suitable for potential application in bone tissue regeneration.

Optimization of Glass Transition Temperature and Pot Life of Epoxy Blends Using Response Surface Methodology (RSM)

The aim of this work was to improve the processability of triglycidyl-p-aminophenol (TGPAP) epoxy resin. To achieve this improvement, a diluent, the diglycidyl ether of bisphenol F (DGEBF or BPF), was added to TGPAP, and the blended epoxy was then cured with 4, 4′-diaminodiphenyl sulfones (DDS). A response surface methodology (RSM) was used, with the target response being to achieve a blended resin with a high glass transition temperature (Tg) and maximum pot life (or processing window, PW). Characterization through dynamic mechanical thermal analysis (DMTA) and using a rheometer indicated that the optimum formulation was obtained at 55.6 wt.% of BPF and a stoichiometric ratio of 0.60. Both values were predicted to give Tg at 180 °C and a processing window of up to 136.1 min. The predicted values were verified, with the obtained Tg and processing window (PW) being 181.2 ± 0.8 °C and 140 min, respectively, which is close to the values predicted using the RSM.

A high-performance silicon-containing arylacetylene resin with conjugated naphthalene rings

2,7-Diphenyloxynaphthalene with large conjugated structure has been introduced into the main chain of silicon-containing arylacetylene resin and then poly(dimethylsilylene-ethynylenephenoxynaphthyloxyphenylene-ethynylene) ( m-PPNSA-MM) was synthesized. The m-PPNSA-MM resin shows good solubility and a wide processing window. A stable three-dimensional cross-linking network formed due to thermal crosslinking reactions of acetylene groups and intermolecular π–π stacking interactions after m-PPNSA-MM was cured through the process of 180°C/2 h + 220°C/2 h + 260°C/4 h. The flexural strength and modulus of the cured m-PPNSA-MM resin at room temperature are 49.2 MPa and 2.75 GPa, respectively. The cured m-PPNSA-MM resin show high thermal stabilities, the 5% temperature of weight loss ( Td5) reaches 560°C in nitrogen.

Structural and Magnetic Properties of P Microalloyed Fe76Cu0.8Nb2.2B9Si12 Alloys

The development of Fe-based nanocrystalline alloys with high saturation magnetization (Bs), excellent magnetic softness and good manufacturability is highly desirable. Here, the effect of substituting 1 at% P for B and Si on the thermal stability, microstructure and magnetic properties of Fe76Cu0.8Nb2.2B9Si12 alloy has been studied in detail. It was found that replacing B with P effectively reduces the coercivity (Hc) of the alloy without deteriorating the Bs and permeability (μ). However, replacing Si with P has little effect on the Hc and Bs, yet significantly reduces the μ. The variation in the magnetic properties can be well understood from the evolution of the microstructure and magnetic anisotropy induced by P microalloying. The Fe76Cu0.8Nb2.2B8Si12P1 alloy with a good processing window, a high Bs of 1.41 T, a great μ of 29,000 at 1 kHz and a low Hc of 0.6 A/m is suitable for high-power electronic devices.