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Analiz formyemosti termoplastov i kompozitov na ih osnove pri ekstruzionno-pressovoj pererabotke

100%

Gerasimenko S. A. , Peseckij S. S.

2010

tom Nr 12

91-96

The effect of main technological factors on moldability of crystallizable (LDPE, HDPE, P~ and amorphous (ABS, PC) thermoplastics has been studied during their melt processing under t conditions which simulate the extrusion-molding technology. The results obtained show that ( flowability of the polymer in a mold depends on a series of technological parameters, rheol relaxation and thermal properties of the polymer. It has been found that the flowability is the nr factor that determines the PM moldability characterized by the melt flow distance in the mo cavity.

Mouldability and its Recovery Properties of 2D Plain Woven Para-Aramid Fabric for Soft Body Armour Applications

100%

Abtew M. A. , Loghin C. , Cristian I. , Boussu F. , Bruniaux P. , Chen Y. , Wang L.

2019

tom Vol. 27, no. 6 (138)

54--62

Mouldability, along with other mechanical properties, is a very crucial material parameter in various technical textile applications, from composites to soft body armour products. Moreover, the mouldability and recovery behaviours of the material will be affected by various internal and external paramters before, during and after the forming process. The current research particularly tried to study the effects of blank-holder pressure (BHP) and the number of layers not only on the moulding characterstics but also on the recovery behaviour of plain woven p-aramid fabrics made from a high-performance yarn with a linear density of 930 dTex. Samples with various numbers of layers were arranged in the same orientation for the moulding process. The moulding approach utilised a specific moulding device in a low-speed forming process and a predefined semi-hemispherically shaped punch for all specimens.Various important dry textile material moulding characteristics and, most importantly, the moulding recovery properties, such as warp and weft direction drawing-in recovery, center high-point recovery, shear angle recovery etc. were investigated.

Formowalność wraz z innymi właściwościami mechanicznymi jest bardzo ważnym parametrem materiałowym w różnych technicznych zastosowaniach tekstylnych, od kompozytów po miękkie pancerze. Ponadto na formowalność i odprężenie elastyczne materiału mają wpływ różne parametry wewnętrzne i zewnętrzne przed, podczas i po procesie formowania. W pracy szczególnie starano się zbadać wpływ nacisku ślepej próby (BHP) i liczby warstw nie tylko na charakterystykę formowania, ale także na odprężenie elastyczne tkanin p-aramidowych wykonanych z wysokowydajnej przędzy o gęstości liniowej 930 dTex. Próbki o różnej liczbie warstw ułożono w tej samej orientacji do procesu formowania. Zbadano cechy formowania materiału tekstylnego oraz, co najważniejsze, odprężenie elastyczne zarówno w kierunku osnowy i wątku.

Prediction of average surface roughness and formability in single point incremental forming using artificial neural network

100%

Mulay A. , Ben B. S. , Ismail S. , Kocanda A.

tom Vol. 19, no. 4

1135--1149

Single point incremental forming (SPIF) is a flexible, innovative, and cheap process for rapidmanufacturing of complex sheet metal parts. It is a crucial task for engineers to predict aprocess when many independent parameters are affecting simultaneously its performance.An artificial neural network (ANN) based prediction model was developed to evaluateaverage surface roughness (Ra) and maximum forming angle (Ømax) while SPIF forming ofAA5052-H32 material. A feedforward backpropagation network with Levenberg–Marquardtalgorithm was employed to build ANN model. The ANNs (4-n-1, 4-n-2) were generated byintroducing different combinations of transfer functions and a number of neurons. Theconfirmation runs were performed to verify the agreement between the ANN predicted andthe experimental results. The developed ANN model (4-n-1) was capable of predicting theprocess response with an excellent accuracy and resulted in overall R-value, MSE, and MAPEof 0.99807, 0.0209, and 5.96% for Ra0.99913, 0.0281, and 0.003 for Ømax. The optimum 4-n-2model was built with overall R-value, MSE of 0.99999 and 0.057194, respectively. Hence, itwas found that the engineering efforts may be reduced in the SPIF process with successfulANN model implementation.