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This paper proposes an incipient chatter detection method to meet high dynamic applications' time and reliability constraints, such as high-speed milling involving heavy noise. The herein introduced method relies on a multiple sampling per revolution (MSPR) technique, coupled with two data preprocessing techniques, a modified adaptive cumulative chatter indicator, and a two-risk levels-based threshold. The MSPR technique enables collecting information-rich enough data to characterize the chatter dynamics thanks to a significant amount of data collected in each revolution. Therefore, the MSPR technique allows for acquiring the data using a short-time window, thus reducing the detection delay. Two data preprocessing techniques, i.e., Z-score normalization and mean-centered, are implemented for data integration and chatter information consolidation. The modified adaptive cumulative chatter indicator has three advantages: (a) it accumulates the information on the chatter feature and highlights the appearance of an incipient chatter; (b) it adapts to the variation of the environmental disturbance noises, resulting in enhanced detection reliability; (c) it is faster than the adaptive cumulative log-likelihood ratio (ACLLR) for decision-making statistically. The two-risk levels-based threshold overcomes the limitations of a unique threshold, and allows simultaneously assessing the two risk levels, thus improving detection reliability. We successfully applied the proposed method to detect incipient chatter in a digital high-speed milling process and assessed its effectiveness by comparing it with several existing chatter detection methods.
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Ruido , Reproducibilidad de los ResultadosRESUMEN
Real-time detection of early chatter is a vital strategy to improve machining quality and material removal rate in the high-speed milling processes. This paper proposes a maximum entropy (MaxEnt) feature-based reliability model method for real-time detection of early chatter based on multiple sampling per revolution (MSPR) technique and second-order reliability method (SORM). To enhance the detection reliability, the MSPR is used to acquire multiple sets of once-per-revolution sampled data (i.e., MSPR data) and to overcome the shortcoming of the once-per-revolution sampling. The proposed MaxEnt feature-based reliability model method solves the issue of the real-time detection of early chatter while ensuring its reliability. The failure hazard function (FHF) is estimated as a chatter indicator by using the SORM with the MaxEnt feature. The proposed method consists of five steps. First, set the prior parameters. Then collect data by using the MSPR technique. Next, calculate a set of the standard deviation of the data collected as a chatter feature and estimate the chatter indicator FHF by applying the SORM with the MaxEnt feature. Finally, implement the real-time detection of early chatter based on the estimated chatter indicator FHF and the threshold FHF0. The proposed method is applied to the high-speed milling process. Two examples prove that the proposed method can detect two kinds of early chatter: the early-stage of a severe chatter and the slightly intolerable chatter.
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Hydrothermal liquefaction of wet biomass such as algae is a promising thermochemical process for the production of bio-oil. Bio-oil aqueous phase generated during liquefaction process is rich in complex organics and can be utilized for biogas production following its pre-treatment with granular activated carbon. In our study, use of 30% activated carbon resulted in higher chemical oxygen demand (COD) reduction (53±0.3%) from aqueous phase. Higher CH4 production (84±12mL/gCOD) was also observed in 30% carbon-treated aqueous phase fed cultures, whereas only 32±6mLCH4/gCOD was observed in control (non-carbon treated) cultures. The results from this study indicate that almost 67±0.3% initial COD of aqueous phase can be reduced using a combination of both carbon treatment and biogas production. This study shows that aqueous phase can be utilized for CH4 production.
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Biomasa , Carbono , Agua , Bacterias/metabolismo , Biocombustibles/análisis , Análisis de la Demanda Biológica de Oxígeno , Purificación del Agua/métodosRESUMEN
The wide variety of nanoscale structures and devices demands novel tools for handling, assembly, and fabrication at nanoscopic positioning precision. The manipulation tools should allow for in situ characterization and testing of fundamental building blocks, such as nanotubes and nanowires, as they are built into functional devices. In this paper, a bottom-up technique for nanomanipulation and nanofabrication is reported by using a 4-probe scanning tunneling microscope (STM) combined with a scanning electron microscope (SEM). The applications of this technique are demonstrated in a variety of nanosystems, from manipulating individual atoms to bending, cutting, breaking carbon nanofibers, and constructing nanodevices for electrical characterizations. The combination of the wide field of view of SEM, the atomic position resolution of STM, and the flexibility of multiple scanning probes is expected to be a valuable tool for rapid prototyping in the nanoscience and nanotechnology.
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We describe the development and the capabilities of an advanced system for nanoscale electrical transport studies. This system consists of a low temperature four-probe scanning tunneling microscope (STM) and a high-resolution scanning electron microscope coupled to a molecular-beam epitaxy sample preparation chamber. The four STM probes can be manipulated independently with subnanometer precision, enabling atomic resolution STM imaging and four-point electrical transport study of surface electronic systems and nanostructured materials at temperatures down to 10 K. Additionally, an integrated energy analyzer allows for scanning Auger microscopy to probe chemical species of nanostructures. Some testing results are presented.