Comparison of Mechanical Properties and Artificial Neural Networks Modeling of PP/PET Blends

Highlights: Graphical/TabularAbstract  Measurement of mechanical properties of PP /PET blends  Improving mechanical properties  Morphological characteristics of PP /PET blends When the morphology and mechanical properties of PP / PET mixtures are examined, it is seen that there is a more homogenized structure in PP +%30PET sample in SEM images. In PP + %40 PET mixture, the transition from droplet to fibril form appears more clearly. In PP +%50 PET mixture, homogeneity started to deteriorate again and gaps started to form again in structure it was observed that Young's modulus reached 2653.79 MPa in PP +%40 PET sample, and an increase of about 210% compared to pure PP. Figure A. SEM images of PP / PET mixtures and Young modulus change Purpose: In this study, the dynamic-mechanical and spectroscopic properties of PP / PET polymer blends with different ratios have been investigated experimentally and the effect of PET additive on dynamic-mechanical and spectroscopic properties has been tried to be explained. Theory and Methods: In this study, tensile test of PP and PET blends were tested at a drawing speed of 25 mm/min and at different ambient temperatures. SEM analyzes were performed and DMA tests used to determine the viscoelastic properties of PP / PET mixtures were performed at a frequency of 1 Hz from 30°C to 120°C with a temperature increase of 2,00 ° C / min. For structure characterization of PP / PET mixtures, FTIR analyzes were performed between 4000-650 cm-1. It was compared with the characteristic peaks of pure materials to see the interactions of PP / PET blend films. Results: It is observed that mechanical properties reaches its maximum value for all temperatures at 40% PET. There is a structural phase transition and matrix structure changes in the PP + 50% PET sample. Because PET and PP ratios are equal For this reason, it was observed that Young's modulus values decreased in comparison to PP + 40% PET in this example.SEM images also support this view. Conclusion: In this study, the effect of PET additives on the mechanical properties of PP, instead of glass fiber, was investigated especially for plastic pipe manufacturing., and it was concluded that the best mechanical properties were PP + 40% PET

show abstract

“…Buna bağlı olarak karışımın mekanik özellikleri iyileşmektedir. Yapılan mekanik testlerde bunu desteklemektedir [11].…”

Section: Sem Analizi (Sem Analysis)unclassified

PET katkısının PP’nin mekanik ve morfolojik özelliklerine etkisi

ÇAVUŞ¹,

Özcanli²,

Beken³

2018

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

View full text Add to dashboard Cite

show abstract

“…ANNs have the ability to establish relationships between the inputs and outputs and produce results. ANN can be trained with current examples to model the problem and the results can be used in real time [11][12][13][14].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network

Boran

Diren

2017

Acta Phys. Pol. A

View full text Add to dashboard Cite

Control charts that are used for monitoring the process and detecting the out-of-control signals are important tools for statistical process control. It is simple to estimate source(s) for out-of-control signals in the univariate process, whereas it is difficult to identify the source(s) in the multivariate processes. The reason is that these kinds of processes require monitoring and controlling of more than one quality characteristics simultaneously. In this study, the proposed model is expected to detect the source(s) for out-of-control signals without help of an expert in the process, by using a multilayer neural network. This model was implemented in furniture fasteners manufacturing. Time gain was obtained while detecting source(s) for out-of-control signals.

show abstract

“…Ibadov investigated duration and work costs of buildings in Polish market [21]. Once learning process has been completed, the results become available in real time [22]. * e-mail: latifugur@duzce.edu.tr…”

Section: Introductionmentioning

confidence: 99%

A Neuro-Adaptive Learning (NAL) Approach about Costs of Residential Buildings

Uğur

2017

Acta Phys. Pol. A

View full text Add to dashboard Cite

The artificial neural networks and fuzzy logic models are two well-known branches of artificial intelligence and have been broadly and successfully used to simulate input-output systems. Over the last two decades, a different modeling method based on fuzzy logic or neural networks has become popular and has been used by many researchers for a variety of engineering applications. Nowadays, for reducing the amount of experiment costs, modeling methods based on artificial neural networks and fuzzy logic systems have become more popular and have been used by many researchers for many civil engineering management applications. In this study a neuro-adaptive learning approach about costs of residential buildings was designed. As a result, NAL can be an alternative approach for the evaluation of the cost estimations of residential buildings construction.

show abstract

Comparison of Mechanical Properties and Artificial Neural Networks Modeling of PP/PET Blends

Cited by 11 publications

References 9 publications

PET katkısının PP’nin mekanik ve morfolojik özelliklerine etkisi

PET katkısının PP’nin mekanik ve morfolojik özelliklerine etkisi

Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network

A Neuro-Adaptive Learning (NAL) Approach about Costs of Residential Buildings

Contact Info

Product

Resources

About