Using the TESS and Kepler K2 light curve archives, I have reanalyzed 13 known δ Scuti stars. AD Ari is now reclassified as a rotating ellipsoidal binary variable. EX Cnc and HD 73712 are reclassified ...as hybrid δ Sct-γ Dor pulsators. EX Cnc turns out to be an enticing asteroseismic target because of its three distinct groups of pulsation frequencies. The strong beating caused by two close frequencies is present in the star CD-54 7154. More than 71 pulsation frequencies were resolved for ι Boo and IT Dra with high significant levels, while V1821 Cyg, V2238 Cyg, BR Cnc, BU Cnc, and BV Cnc pulsate with a few dozen frequencies. In particular, K2 data revealed a significantly richer pulsational spectrum for the two δ Scuti stars BU Cnc and BV Cnc from six to 26. Unlike the other 12 stars, BN Cnc shows the simplest pulsation pattern. With high-precision and long-term space-based photometry, we are able to discern the pulsational contents of these stars more clearly and enhance our knowledge of them. This reanalysis using TESS and Kepler K2 data highlights the diversity of pulsational behavior among δ Scuti stars and the value of long-duration, high-precision photometry. Further asteroseismic modeling of these stars, particularly EX Cnc with its distinct frequency groups, promises to refine our understanding of their internal structures and pulsational mechanisms.
•Identify the variability type of a mis-classified δ Sct star AD Ari to be ELL; Disclaim the binary suspicion of ι Boo.•Resolving multiple dozens of new pulsation frequencies for the selected δ Sct stars.•Revealing hybrid δ Sct-γ Dor pulsation in EX Cnc and HD 73712.
Abstract
As China has stepped into a new era of development, the popularity of computer applications is becoming more and more profound. Especially in the machinery manufacturing industry, the ...current market has put forward a higher requirement for the accuracy and toughness of machinery manufacturing, and the traditional artificial manufacturing has not been adapted to the development of the industry. Through numerical control technology, numerical control tool manufacturing process can become very standardized, its accuracy and toughness is also guaranteed. This paper will from the numerical control this point of view, to explore the influence of numerical control cutting parameters on the numerical control tool.
To address the brittleness challenge of polyhydroxybutyrate-co-valerate (PHBV) rooting from its slow nucleation rate, cellulose nanocrystals (CNCs) were employed as bio-based nano nucleation and ...reinforcement agents. CNCs were further functionalized through succinylation using aliphatic dodecyl succinic anhydride (DDSA), for improved compatibility and dispersion within PHBV. Nanocomposites of PHBV incorporating pristine or DDSA-modified CNC (mCNC) were prepared through solution mixing followed by melt injection molding. The study focused on investigating how CNCs and mCNCs affect the crystallization behavior, thermal, rheological, and mechanical properties of PHBV nanocomposites over time, in the course of annealing or conditioning. Thermal results revealed that the incorporation of neat CNCs generally improving stability due to restricted polymer chain mobility and hydrogen bonding, while DDSA-modified CNCs show varied effects depending on the concentration, sometimes diminishing stability due to increased chain mobility. Polarized optical microscopy revealed the superior nucleation efficiency of mCNC, especially at low contents, leading to smaller and numerous spherulites over conditioning. Rheological analysis indicated a dilution effect of the hairy mCNCs, decreasing both dynamic modulus and complex viscosity. Mechanical properties, assessed through tensile testing and dynamic mechanical analysis after 15 days of conditioning, demonstrated the evolving effect of CNCs on aging-induced embrittlement and thermo-mechanical performance of PHBV during storage. The results revealed that incorporating 1 wt% mCNCs effectively toughened PHBV, increasing Young's modulus, and decreasing Tg without scarifying elongation at break compared to neat PHBV. The findings position mCNCs as a promising nucleation agent which retains PHBV nanocomposites toughness after aging.
Display omitted
•Successful succinylation CNCs via DDSA agent to generate mCNCs.•Superior nucleation efficiency of mCNCs over pristine CNCs in PHBV.•Optimum toughness improvement of PHBV achieved with 1 wt% of mCNCs.•Crucially impact of mCNCs on the crystallization kinetics and mechanical performance of PHBV.
This work presents an environmentally friendly, iodine-catalyzed chemical modification method to generate highly hydrophobic, optically active nanocrystalline cellulose (CNC). The high degree of ...ester substitution (DS = 2.18), hydrophobicity, crystalline behavior, and optical activity of the generated acetylated CNC (Ac-CNC) were quantified by TEM, FTIR, solid 13C NMR, contact angle, XRD, and POM analyses. Ac-CNC possesses substantial enhancement in thermal stability (16.8%) and forms thin films with an interlayer distance of 50–150 nm, presenting cavities suitable for entrapping nano- and microparticles. Generated Ac-CNC proved to be an effective reinforcing agent in hydrophobic polymer matrices for fabricating high performance nanocomposites. When integrated at a very low weight percentage (0.5%) in an epoxy matrix, Ac-CNC provided for a 73% increase in tensile strength and a 98% increase in modulus, demonstrating its remarkable reinforcing potential and effective stress transfer behavior. The method of modification and the unique properties of the modified CNC (hydrophobicity, crystallinity, reinforcing ability, and optical activity) render them a novel bionanomaterial for a range of multipurpose applications.
Thermal error of the machine tool spindle is one of the main factors affecting the machining accuracy. For the complex operating environment of the machine tool, the difficulty of thermal error ...prediction modeling, and the low accuracy of the traditional thermal error prediction model, a spindle thermal error prediction model based on the improved particle swarm optimization (IPSO) optimize back propagation (BP) neural network is established in this paper. The temperature measurement points are clustered by SOM neural network, and the correlation analysis method is used to explore the correlation between the thermal sensitive points and the thermal error of the spindle. The S-type function is used to improve the inertia weight coefficient of the IPSO algorithm so as to improve the particle optimization effect. IPSO is used to optimize the parameters of BP neural network, such as the initial weights and thresholds. Compared with the GA-BP prediction model, the modeling efficiency, robustness, and accuracy of IPSO-BP neural network prediction model are all superior GA-BP prediction model. Taking the thermal error of the electric spindle of precision CNC machining center as the research object, the intelligent temperature sensor and the laser displacement sensor are used to obtain the machine tool temperature values and the spindle thermal error values. The prediction accuracy of the GA-BP model for the spindle thermal error was 93.1%, and the prediction accuracy of the IPSO-BP model was 96.5%. The results show that the IPSO-BP model can accurately predict the thermal error of the spindle under different working conditions. The model can obtain higher thermal error prediction accuracy and is more suitable for the thermal error compensation model.
Cutting parameters are important components in the process of computer numerical control (CNC) machining, and reasonable choice of cutting parameters can significantly affect the energy efficiency. ...This paper presents a multi-objective parameter optimization method for energy efficiency in CNC milling process. Firstly, the energy consumption composition characteristics and temporal characteristics in CNC milling are analyzed, respectively. The energy model of CNC milling is then established, of which the correlation coefficient is obtained through nonlinear regression fitting. Then a multi-objective optimization model is proposed to take the highest energy efficiency and the minimum production time as the optimization objectives, which is solved based on Tabu search algorithm. Finally, a case study is conducted to validate the proposed multi-objective optimization model and the optimal parameter solutions of maximum energy efficiency and minimum production time is obtained. Moreover, the parametric influence on specific energy consumption and production time are explicitly analyzed. The experiment results show that cutting depth and width are the most influential parameters for specific energy consumption, and spindle speed ranks the first for the production time.
•The geometric errors to the machining with non-rotary cutters are compensated.•The cutter rotation angle is considered for the machining with non-rotary cutters.•The geometric errors are modeled by ...considering the cutter rotation angle.•The cutting point is chosen as the reference point in the compensation algorithm.
As one of the main error sources, the geometric errors are compensated in this work for the accurate worm grinding of spur face gears. The previous methods of the geometric error compensation are usually applied to the machining with rotary cutters, of which the error of cutter rotation angle is not compensated since it does not affect the machining result. These methods are inappropriate to the worm grinding of face gears, because the worm is a non-rotary cutter and the machining result is sensitive to the error of cutter rotation angle. In this work, an innovative geometric error compensation method is proposed to the machining with non-rotary cutters based on two main points. First, the cutter rotation angle is considered to both the modeling and compensation of the geometric errors. Second, the instantaneous ideal contact point, which is calculated according to the generation process of the worm grinding of face gears, is chosen as the reference point to the compensation algorithm rather than the cutter tip point used in the previous method. The proposed method is validated by the example with both the theoretical calculation and practical machining.
Display omitted
•CNCs-I and CNCs-II showed different crystal structure and morphologies.•CNCs-II had higher hydrophilicity resulted in less stable emulsion than CNCs-I.•Crystalline allomorph of CNCs plays main role ...to stabilization of Pickering emulsion.
In recent years, nanocelluloses have attracted a lot of interests as promising stabilizers for Pickering emulsion particularly in food, cosmetics and pharmaceutics industries. In this work, two cellulose nanocrystals (named CNCs-I and CNCs-II) with different crystalline allomorph were investigated as stabilizers for oil-water Pickering emulsion. CNCs were prepared by sulfuric acid hydrolysis of unmodified and mercerized microcrystalline cellulose (MCC), respectively. CNCs-I was needle-like particles (length – 200 nm, width – 16.4 nm), while CNCs-II was individual granules (length – 18.8 nm, width – 10.9 nm) with ellipsoid shapes. Both CNCs had surface charge density higher than 0.1 e/nm2. Pickering emulsions stabilized by CNCs-I had larger emulsion ratio, two times smaller droplet size and superior performance of stability than CNCs-II. It is concluded that crystalline allomorph of CNCs played more dominating roles to the stabilization of Pickering emulsion than morphologies of CNCs themselves in this work, and CNCs-II is not the good candidates as stabilizer for Pickering emulsion compared to CNCs-I.
PDF
