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The Effect of HPMC and CNC on the Structure and Properties of Alginate Fibers
Meiyu Ci,Jie Liu,Shenglong Shang,Zhiming Jiang,Ping Zhu,Shuying Sui 한국섬유공학회 2020 Fibers and polymers Vol.21 No.10
Bio-composite alginate fibers with binary and ternary blends were prepared by using cellulose nanocrystal (CNC)and hydroxypropyl methylcellulose (HPMC) as composite fillers through wet-spinning method. Structural, thermal,mechanical properties and surface morphology of fibers were characterized by Fourier transform infrared spectroscopy (FTIR),X-ray diffraction (XRD), Thermogravimetric Analysis (TGA), Mechanical strength testing, Scanning ElectronMicroscopy (SEM). The thermal stability and mechanical performance of SA/HPMC and SA/HPMC/CNC composite fibersimproved as the increasing of crystallinity and intermolecular H-bonding interaction of the fibers. HPMC is helpful toimprove the extensibility and stiffness of alginate fibers, and CNC can further enhance the stiffness of SA/HPMC compositefibers. The tensile strength, elongation at break, the initial modulus and work at break of SA/HPMC/CNC composite fiberswere superior to those of alginate fibers. Roughness of surface and tensile section of SA/HPMC and SA/HPMC/CNCcomposite fibers got increased. Water absorbency and salt resistance were significantly improved.
Optimization of Stamping Process Parameters Based on Improved GA-BP Neural Network Model
Yanmin Xie,Wei Li,Cheng Liu,Meiyu Du,Kai Feng 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.7
Reasonable process parameters are the key measures to ensure the quality of stamping products. In order to reduce the risk of cracking and wrinkling of stamping products, an improved genetic algorithm is proposed and used to optimize the weights and thresholds of the BP neural network(BPNN). A surrogate model combining an improved genetic algorithm and BPNN(IGA-BPNN)is developed. Taking double C as the research object, the training samples and test samples are extracted through Latin hypercube. The training output of IGA-BPNN model is obtained by AutoForm simulation, and the mapping relationship between process parameters and forming quality is established. Then the mapping relationship is optimized by IGA to obtain the optimal process parameters. The results show that this method reduces the wrinkling of the flange edge of double C and obviously improves the forming quality.