Behavior and design of perforated steel storage rack columns under axial compression

Kadi, Bassel El; Kıymaz, Güven

doi:10.12989/scs.2015.18.5.1259

Cited by 6 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various research investigations are being increasingly targeted in the direction of using natural and synthetic fibers, and other particulate reinforced composites to develop distinct automobile and aircraft parts. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] In this regard, recently Jagadessh et al [25] reported areca/sisal/carbon reinforced hybrid epoxy composites for the application in railways interior parts. The investigation results for mechanical and morphological properties showed a positive signal toward their use in railways/relevant applications.…”

Section: Introductionmentioning

confidence: 99%

Recycling and reinforcement potential for the fly ash and sisal fiber reinforced hybrid polypropylene composite

2021

View full text Add to dashboard Cite

The investigation explores mechanical, thermal and thermomechanical behavior of hybrid composites post-recycling, and subsequent, 5 wt.% additional reinforcement to the recyclate of sisal (SSL) fiber fly ash (FA)/polypropylene (PP) hybrid composite. Impact of recycling and 5 wt.% of SSL fiber reinforcement of the recyclate on mechanical properties of hybrid composites made of 15-30 wt.% SSL and 5-15 wt.% FA were investigated. Noticeably, after recycling, a minimal loss of 2.39% only in tensile and 2.20% in flexural strength were recorded for the hybrid composites fabricated with 15 wt.% each of FA and SSL. The highest loss in tensile (10.32%) and flexural (4.22%) strength were reported for only 30 wt.% SSL fiber reinforced PP indicating that hybridization with FA has a benign effect on reducing property degradation. These lost properties were regained by reinforcing recycled composites with 5 wt.% of SSL fiber. Mechanical studies showed that the composite with 30 wt.% SSL fibers recycled and reinforced with additional 5 wt.% fibers showed a substantial improvement of ~10% and ~4% in the tensile and flexural strength while keeping the impact strength stable. FE-SEM images confirmed a reduction (~67%) in aspect ratio of the SSL fiber due to reprocessing. DMA demonstrated a decrease of ~11% in storage modulus of the composites after first recycling. TGA confirmed an insignificant change in the degradation temperature of the recycled and re-reinforced recycled composites versus fresh composites. The study paves the way for potential future recycling applications of waste natural fiber reinforced PP.

show abstract

Section: Introductionmentioning

confidence: 99%

Recycling and reinforcement potential for the fly ash and sisal fiber reinforced hybrid polypropylene composite

2021

View full text Add to dashboard Cite

show abstract

“…This proposed approach not only yields a sufficient understanding of the effects of process parameters but also produces an optimal parameter setting to ensure that the composites exhibit the best wear performance characteristics. The details of this methodology are described by Kadi .…”

Section: Mechanical Characterizationmentioning

confidence: 99%

Preparation, properties and wear performance evaluation of epoxy‐palmyra fiber composites

Biswal

Satapathy

2016

Polymer Composites

View full text Add to dashboard Cite

This article presents a parametric analysis of dry sliding wear process undergone by a new class of composite material consisting of epoxy and short palmyra fibers (SPF). It is an attempt to explore the possibility of improving the dry sliding wear resistance of neat epoxy by reinforcing it SPF. Palmyra fiber is an inexpensive, strong, and naturally available fiber derived from the stalks of palmyra leaf. In the present investigation, epoxy‐based composites with different weight proportions of SPF (0, 4, 8, and 12 wt%) are fabricated although the simple hand‐lay‐up route. The tensile, flexural, and micro‐hardness properties of the samples are found out which is followed by a FTIR spectroscopy of the samples. The composite samples are then subjected to wear trials using a pin‐on‐disc test rig as per ASTM G 99‐05 test standards under different operating conditions. The effects of various operational variables on the dry sliding wear behavior of these epoxy composites with and without SPF reinforcement are studied. The design of experiments approach using Taguchi's L16 orthogonal array is used for parametric analysis of the wear process. Significant parameters affecting the wear rate of epoxy are identified. This study revealed that while the fiber content and the sliding velocities have significant influence on the wear arte of the composites, the effects of sliding distance and normal load are ignorable. An optimal parameter setting for minimum specific wear rate is obtained. The morphologies of the worn surfaces are examined by scanning electron microscopy and possible wear mechanisms are identified. POLYM. COMPOS., 39:1827–1834, 2018. © 2016 Society of Plastics Engineers

show abstract

“…22 ML methods are now being applied to a variety of problems in materials science, including fields such as the development of battery materials and the testing of composite materials, 23,24 where enormous amounts of raw data are generated on a daily basis that can be used to train the model for effective decision-making. 25 The artificial neural network (ANN) is a computational ML method, inspired by the architecture of biological neural networks, 26 that has been applied to many problems related to composite materials [27][28][29][30] and other materials for defect detection. 31,32 ML methods are being applied to materials design and characterization fields.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Framework for Detection of Defects in 3D-Printed Fiber Reinforcement Composites

Chen

Yanamandra

Gupta

2021

JOM

View full text Add to dashboard Cite

One of the major challenges in applying tomography methods for detecting defects in composite materials is the large image datasets generated during imaging, which require significant effort for the detection of damage. Machinelearning (ML) methods require a large training dataset and can be efficient in processing tomography datasets for defect detection. Methods need to be developed for processing images to train the ML algorithms, which is the focus of the present work. An additive manufactured fiber reinforced composite material is imaged using a micro-CT scan to generate an image set for defect detection. The microstructures are processed using the binarized statistical image features (BSIF) method for compression without compromising the desired information about defects. The result shows that the convolutional neural network model has a mean square error of 0.001 in fiber orientation prediction, and a scheme has been developed for defect detection based on the predictions obtained from the ML models.

show abstract

Behavior and design of perforated steel storage rack columns under axial compression

Cited by 6 publications

References 8 publications

Recycling and reinforcement potential for the fly ash and sisal fiber reinforced hybrid polypropylene composite

Recycling and reinforcement potential for the fly ash and sisal fiber reinforced hybrid polypropylene composite

Preparation, properties and wear performance evaluation of epoxy‐palmyra fiber composites

Artificial Neural Networks Framework for Detection of Defects in 3D-Printed Fiber Reinforcement Composites

Contact Info

Product

Resources

About