scholarly journals Modeling the Adhesion Bonding Strength in Injection Overmolding of Polypropylene Parts

Polymers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2063
Author(s):  
Ruggero Giusti ◽  
Giovanni Lucchetta
Keyword(s):  
Bonding Strength ◽  
Prediction Error ◽  
Strength Prediction ◽  
Interface Temperature ◽  
Base Temperature ◽  
Melt Temperature ◽  
Self Diffusion ◽  
Localized Heating ◽  
Quadratic Distance ◽  
Healing Model

In this work, the bonding strength of overmolded polypropylene is investigated and modeled. A T-joint specimen was designed to replicate the bonding between a base and an overmolded stem made of the same polymer: a previously molded plaque was used for the base, and the stem was directly overmolded. The effect of melt temperature, holding pressure, and localized heating was investigated following the design of experiments approach. Both the melt and base temperature positively affect the welding strength. On the contrary, the holding pressure negatively contributed, as the crystallization temperature significantly increases with pressure. Then, the bonding strength of the specimens was predicted using a non-isothermal healing model. Moreover, the quadratic distance of diffusion (based on the self-diffusion model) was calculated and correlated with the bonding strength prediction. The non-isothermal healing model well predicts the bonding strength when the reptation time is calculated within the first 0.09 s of the interface temperature evolution. The prediction error ranges from 1% to 35% for the specimens overmolded at high and low melt and base temperatures, respectively.

scholarly journals Prediction of Development of Asiatic Lilies Based on Air Temperature and Thermal Units

HortScience ◽  
2003 ◽  
Vol 38 (6) ◽  
pp. 1100-1103 ◽  
Author(s):  
J. Steininger ◽  
C.C. Pasian
Keyword(s):  
Linear Regression ◽  
Change Point ◽  
Prediction Error ◽  
Base Temperature ◽  
Point Model ◽  
Degree Days ◽  
Flower Bud ◽  
Open Flower ◽  
Average Prediction Error ◽  
Change Point Model

`Butter Pixie' and `Horizon' Asiatic lilies (Lilium spp.), were grown at several temperatures. The phenological events of visible shoot (VS), visible flower bud (VB), and open flower (OF) were recorded daily. Based on these events, phenophases from VS to VB (VS:VB), from VB to OF (VB:OF), and from VS to OF (VS:OF) were defined. Daily rates of development to complete a phenophase increased with temperature. Nonlinearity was obvious for all phenophases around 25 °C for `Horizon' and 27 °C for `Butter Pixie'. A piece-wise linear regression change point model was fitted to each dataset. The base temperature (Tb), the temperature at which the nonlinearity occurred (Ti), and the temperature for fastest development (To) could then be determined. Tb for the phenophase VS: OF was -0.4 °C for `Butter Pixie' and 3.0 °C for `Horizon'. Ti for `Butter Pixie' was 25.7 °C for VB:OF and 26.1 °C for the phenophase VS:OF. However, Ti for `Horizon' was found only for the phenophase VS:OF. To complete the phenophase VS:OF, 1102.2 degree days (°Cd) were predicted necessary for `Butter Pixie' and 833.2 °Cd for `Horizon'. Predicted time of events was compared with observed values. Subdividing VS:OF into VS:VB and VB:OF and using their respective Tb and TU reduced the average prediction error from 2.13 to 1.87 d for `Butter Pixie' and from 2.39 to 1.86 days for `Horizon'.

Effect of the Interdiffusion at the Polymer/Polymer Interface on the Flexural Properties of Over-Moulded Short Glass Fibre/Glass Fabric Reinforced PA6 Composites

2014 ◽  
Vol 611-612 ◽  
pp. 821-828 ◽  
Author(s):  
Eric Lafranche ◽  
Thierry Renault ◽  
Patricia Krawczak
Keyword(s):  
Cooling Rate ◽  
Glass Fibre ◽  
Molecular Diffusion ◽  
Flexural Modulus ◽  
Glass Fabric ◽  
Melt Temperature ◽  
Engineering Plastics ◽  
Short Glass Fibre ◽  
Quadratic Distance ◽  
Short Glass

The injection over-moulding of 30wt% short glass fibre reinforced PA6 (SGF from Solvay Engineering Plastics) onto consolidated unbalanced (87/13) 70wt% glass fabric reinforced PA6 (Continuous Fibre Reinforced Thermoplastic (CFRT) from Solvay Engineering Plastics) was investigated with the objective to optimise the flexural and interlaminar shearing of the complex. Among the processing parameters, the temperature of the fabric before injection and the over-moulded melt temperature associated to the mould temperature (cooling rate of the complex) were revealed as the main parameters directing the mechanical properties of the complex. Moreover, the flexural modulus and the apparent interlaminar shear strength fall down critically in the main direction (chain direction of the fabric) under a CFRT temperature of 150°C. The effect of the SGF/CFRT interface was quantified in term of quadratic distance of diffusion through the interface. First, the 1D cooling of the complex was simulated according to the heat transfer module of COMSOL Multiphysics® in order to determinate the variation of the temperature field during the cooling stage of process. The calculations were achieved with an initial CFRT temperature of 23, 100, 150 and 200°C, the mould and SGF melt temperatures were kept constant. The diffusion theory has then been applied to calculate the variation of the auto-diffusion coefficient through the thickness during the complex cooling, the diffusion is supposed occurring only at a temperature above the PA6 crystallisation temperature (185°C). The calculation of the quadratic distance of diffusion through the thickness confirmed the mechanical results. Under a CFRT temperature of 150°C, the ability to the molecular diffusion at the interface becomes non-existent. The melt temperature of the SGF PA6 has to be sufficient to melt the CFRT PA6 interface, the time of diffusion directed by both the CFRT and mould temperatures (cooling rate) has to be long enough to allow the molecular diffusion from the material to the other.

scholarly journals High-Entropy Alloys Properties Prediction Model by Using Artificial Neural Network Algorithm

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1559
Author(s):  
Sanggyu Choi ◽  
Sung Yi ◽  
Junghan Kim ◽  
Byungsue Shin ◽  
Soongkeun Hyun
Keyword(s):  
Neural Network ◽  
Yield Strength ◽  
Mole Fraction ◽  
Prediction Error ◽  
Strength Prediction ◽  
High Entropy Alloys ◽  
Process Data ◽  
High Entropy ◽  
Post Process ◽  
The Mean

A new approach method has been studied for the efficient and accurate prediction of high-entropy alloys (HEAs) properties. The artificial neural network (ANN) algorithm was employed to predict the mechanical properties such as yield strength, microstructure, and elongation of the alloy by training from the mole fraction and post-process information that has an influence on the mechanical properties. The mean error rate of prediction for the yield strength was 19.6%. Microstructure predictions were consistent for all test data. On the other hand, the ANN model trained only with mole fraction data had a yield strength prediction error of 33.9%. Omission of post-process data caused a decrease in the accuracy. In addition, the prediction was performed with the lasso regression model in the same way. The mean error rate of the lasso model trained with only a mole fraction was 26.1%. The lasso model trained with a mole fraction and post-process data had a yield strength prediction error of 31.1%. The linear regression equation showed limitations, as the accuracy decreased as the number of independent variables increased. As there are more variables affecting metal properties, the ANN approach is more advantageous, and the more data there are, the more accuracy increases, making it possible to design HEAs alloys that are simpler and more efficient than conventional methods. This approach predicted HEAs properties using only mole fraction and post-processing information, without the need to use conventional physicochemical theories or perform derived complex calculations.

Dynamic studies of silicide-mediated crystallization of amorphous silicon

1992 ◽  
Vol 50 (2) ◽  
pp. 1352-1353
Author(s):  
C. Hayzelden ◽  
J. L. Batstone
Keyword(s):  
Ion Implantation ◽  
Solid Phase ◽  
Metallic Phase ◽  
Single Crystal Substrate ◽  
Free Electrons ◽  
Melt Temperature ◽  
Transmission Electron ◽  
Crystal Substrate ◽  
High Resolution Tem

Epitaxial reordering of amorphous Si(a-Si) on an underlying single-crystal substrate occurs well below the melt temperature by the process of solid phase epitaxial growth (SPEG). Growth of crystalline Si(c-Si) is known to be enhanced by the presence of small amounts of a metallic phase, presumably due to an interaction of the free electrons of the metal with the covalent Si bonds near the growing interface. Ion implantation of Ni was shown to lower the crystallization temperature of an a-Si thin film by approximately 200°C. Using in situ transmission electron microscopy (TEM), precipitates of NiSi2 formed within the a-Si film during annealing, were observed to migrate, leaving a trail of epitaxial c-Si. High resolution TEM revealed an epitaxial NiSi2/Si(l11) interface which was Type A. We discuss here the enhanced nucleation of c-Si and subsequent silicide-mediated SPEG of Ni-implanted a-Si.Thin films of a-Si, 950 Å thick, were deposited onto Si(100) wafers capped with 1000Å of a-SiO2. Ion implantation produced sharply peaked Ni concentrations of 4×l020 and 2×l021 ions cm−3, in the center of the films.

Temporal dynamics of real-world emotion are more strongly linked to prediction error than outcome.

2020 ◽  
Vol 149 (9) ◽  
pp. 1755-1766 ◽  
Author(s):  
William J. Villano ◽  
A. Ross Otto ◽  
C. E. Chiemeka Ezie ◽  
Roderick Gillis ◽  
Aaron S. Heller
Keyword(s):  
Real World ◽  
Prediction Error ◽  
Temporal Dynamics

Self-diffusion study of bile salt-monoolein micelles determination of the intermicellar bile salt concentration

1983 ◽  
Vol 80 ◽  
pp. 315-323 ◽  
Author(s):  
Marc Lindheimer ◽  
Jean-Claude Montet ◽  
Roselyne Bontemps ◽  
Jacques Rouviere ◽  
Bernard Brun
Keyword(s):  
Bile Salt ◽  
Salt Concentration ◽  
Diffusion Study ◽  
Bile Salt Concentration ◽  
Self Diffusion
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