Rubber recycling attracts considerable attention by a variety of industries around the world due to shrinking resources, increasing cost of raw materials, growing awareness of sustainable development, and environmental issues. Recycled rubber is commonly used in aeronautic, automotive, and transportation industries. In this study, recycled rubber composites designed with different reinforcements in the literature are scrutinized by means of toughening mechanisms, mechanical and physical properties, as well as microstructural and fracture surface analysis. Microscale reinforcements (glass bubbles, alumina fiber, etc.) and nanoscale reinforcements (nanosilica, graphene nanoplatelets, etc.) utilized as reinforcements in rubber composites are thoroughly reviewed. The general mechanical properties reported by previous studies, such as tensile, compressive, and flexural strength, are investigated with the main goal of optimizing the amount of reinforcement used. The majority of the studies on recycled rubber composites show that recycled rubber reinforced with microscale particles leads to the development of physical and mechanical properties of the structures and also provides low-cost and lightweight composites for several application areas. Moreover, recycled rubber containing composites can be suitable for applications where high toughness and high resistance to impact are desirable. The present review aims to demonstrate research on reinforced recycled rubber composites in the literature and prospective outcomes.

Recycling of materials attracts considerable attention around the world due to environmental and economic concerns. Recycled rubber is one of the most commonly used recyclable materials in a number of industries, including automotive and aeronautic because of their low weight and cost efficiency. In this research, devulcanized recycled rubber-based composites are designed with glass bubble microsphere, short glass fiber, aluminum chip and fine gamma alumina fiber (γ-Al₂O₃) reinforcements. After the determination of the reinforcements with matrix, bending strength and fracture characteristics of the composite are investigated by three-point bending (3PB) tests. Halpin–Tsai homogenization model is adapted to the rubber-based composites to estimate the moduli of the composites. Furthermore, the relevant toughening mechanisms for the most suitable reinforcements are analyzed and stress intensity factor, K_Ic and critical energy release rate, G_Ic in mode I are determined by 3PB test with single edge notch specimens. In addition, 3PB tests are simulated by finite element analysis and the results are compared with the experimental results. Microstructural and fracture surfaces analysis are carried out by means of scanning electron microscopy (SEM). Mechanical test results show that the reinforcement with glass bubbles, aluminum oxide ceramic fibers and aluminum chips generally increase the fracture toughness of the composites.

Aluminum alloys have inherent tendencies to produce casting defects caused by alloying or metal melt flow inside the mold. The traditional detection method for these defects includes reduced pressure tests, which assess metal quality in a destructive manner. This leaves a gap between metal quality assessments and tensile test correlations. Computed tomography (CT) scans offer crucial assistance in evaluating the internal quality of castings without damaging the structure. This provides a valuable opportunity to couple mechanical tests with numerical methods such as finite element analysis to predict the mechanical performance of the alloy. The present study aims to evaluate the internal quality of cast aluminum alloys using CT scans and to correlate the defect metrics obtained from CT scans with mechanical test results. The Gurson-type material model and finite element methodology have been used to validate the correlation studies. Therefore, we propose a more holistic approach to predicting the behavior of metals by coupling damage models with CT scans and mechanical tests. The study investigates several CT metrics such as the defect volume, total defect surface, biggest defect surface, and projected area of defects. The conclusion reveals that CT scans provide crucial assistance in evaluating the internal quality of castings, and CT defect metrics can be used to build correlations between mechanical tests and CT evaluations. The study also suggests that the concept of adjusted representative material yield parameter (ARMY) or computed representative material yield parameter (CRMY) can be used to correlate CT metrics with mechanical strength in cast materials and parts for a given aluminum alloy. Overall, the study proposes a more comprehensive methodology to assess the quality of cast aluminum alloys and couple the quality to mechanical performance.

This paper aims toward the successful detection of harmful materials in a substance by integrating machine learning (ML) into laser-induced breakdown spectroscopy (LIBS). LIBS is used to distinguish five different synthetic polymers where eight different heavy material contents are also detected by LIBS. Each material intensity-wavelength graph is obtained and the dataset is constructed for classification by a machine learning (ML) algorithm. Seven popular machine learning algorithms are applied to the dataset which include eight different substances with their wavelength-intensity value. Machine learning algorithms are used to train the dataset, results are discussed and which classification algorithm is appropriate for this dataset is determined.

This work aims at the unification of the thermodynamically consistent representation of the micromorphic theory and the microdamage approach for the purpose of modeling crack growth and damage regularization in crystalline solids. In contrast to the thermodynamical representation of the microdamage theory, micromorphic contribution to flow resistance is defined in a dual fashion as energetic and dissipative in character, in order to bring certain clarity and consistency to the modeling aspects. The approach is further extended for large deformations and numerically implemented in a commercial finite element software. Specific numerical model problems are presented in order to demonstrate the ability of the approach to regularize anisotropic damage fields for large deformations and eliminate mesh dependency.