Surface grinding of carbon fiber-reinforced plastic composites using rotary ultrasonic machining: Effects of tool variables

Citation: Wang, H., Ning, F. D., Hu, Y. B., Fernando, P., Pei, Z. J., & Cong, W. L. (2016). Surface grinding of carbon fiber-reinforced plastic composites using rotary ultrasonic machining: Effects of tool variables. Advances in Mechanical Engineering, 8(9), 14. doi:10.1177/1687814016670284Carbon fiber-reinforced plastic composites have many superior properties, including low density, high strength-to-weight ratio, and good durability, which make them attractive in many industries. However, due to anisotropic properties, high stiffness, and high abrasiveness of carbon fibers in carbon fiber-reinforced plastic, high cutting force, high tool wear, and high surface roughness are always caused in conventional machining processes. This article reports an investigation using rotary ultrasonic machining in surface grinding of carbon fiber-reinforced plastic composites in order to develop an effective and high-quality surface grinding process. In rotary ultrasonic machining surface grinding of carbon fiber-reinforced plastic composites, tool selection is of great importance since tool variables will significantly affect output variables. In this work, the effects of tool variables, including abrasive size, abrasive concentration, number of slots, and tool end geometry, on machining performances, including the cutting force, torque, and surface roughness, are experimentally studied. The results show that lower cutting forces and torque are generated by the tool with higher abrasive size, lower abrasive concentration, and two slots. Lower surface roughness is generated by the tool with smaller abrasive size, smaller abrasive concentration, two slots, and convex end geometry. This investigation will provide guides for tool selections during rotary ultrasonic machining surface grinding of carbon fiber-reinforced plastic composites

Gene-SGAN: Discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer\u27s disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes

Radiative Seesaw Mechanism at Weak Scale

We investigate an alternative seesaw mechanism for neutrino mass generation. Neutrino mass is generated at loop level but the basic concept of usual seesaw mechanism is kept. One simple model is constructed to show how this mechanism is realized. The applications of this seesaw mechanism at weak scale to cosmology and neutrino physics are discussed.Comment: 12 Pages, latex, no figure

Rotary ultrasonic machining of CFRP composites: a study on power consumption

Carbon fiber reinforced plastic (CFRP) composites are very difficult to machine. A large number of holes need to be drilled in CFRP for many applications. Therefore, so it is important to develop cost-effective drilling processes. CFRP has been drilled by rotary ultrasonic machining (RUM) successfully. The literature has reports about the effects of input variables on output variables (including cutting force, torque, surface roughness, tool wear, and workpiece delamination) in RUM of CFRP. However, there are no reports on power consumption in RUM of CFRP. This paper reports the first study on power consumption in RUM of CFRP. It reports an experimental investigation on effects of input variables (ultrasonic power, tool rotation speed, feedrate, and type of CFRP) on power consumption of each component (including ultrasonic power supply, spindle motor, coolant pump, and air compressor) and the entire RUM system

RNA helicase signaling is critical for type I interferon production and protection against rift valley fever virus during mucosal challenge

Rift Valley fever virus (RVFV) is an emerging RNA virus with devastating economic and social consequences. Clinically, RVFV induces a gamut of symptoms ranging from febrile illness to retinitis, hepatic necrosis, hemorrhagic fever, and death. It is known that type I interferon (IFN) responses can be protective against severe pathology; however, it is unknown which innate immune receptor pathways are crucial for mounting this response. Using both in vitro assays and in vivo mucosal mouse challenge, we demonstrate here that RNA helicases are critical for IFN production by immune cells and that signaling through the helicase adaptor molecule MAVS (mitochondrial antiviral signaling) is protective against mortality and more subtle pathology during RVFV infection. In addition, we demonstrate that Toll-like-receptor-mediated signaling is not involved in IFN production, further emphasizing the importance of the RNA cellular helicases in type I IFN responses to RVFV

Planck scale effects in neutrino physics

We study the phenomenology and cosmology of the Majoron (flavon) models of three active and one inert neutrino paying special attention to the possible (almost) conserved generalization of the Zeldovich-Konopinski-Mahmoud lepton charge. Using Planck scale physics effects which provide the breaking of the lepton charge, we show how in this picture one can incorporate the solutions to some of the central issues in neutrino physics such as the solar and atmospheric neutrino puzzles, dark matter and a 17 keV neutrino. These gravitational effects induce tiny Majorana mass terms for neutrinos and considerable masses for flavons. The cosmological demand for the sufficiently fast decay of flavons implies a lower limit on the electron neutrino mass in the range of 0.1-1 eV.Comment: 24 pages, 1 figure (not included but available upon request), LaTex, IC/92/196, SISSA-140/92/EP, LMU-09/9

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes