Composites can be divided into three groups based on their matrix materials, namely polymer, metal and ceramic. Composite materials fail due to micro cracks. Repairing is complex and almost impossible if cracks appear on the surface and interior, which minimizes reliability and material life. In order to save the material from failure and prolong its lifetime without compromising mechanical properties, self-healing is one of the emerging and best techniques. The studies to address the advantages and challenges of self-healing properties of different matrix materials are very limited; however, this review addresses all three different groups of composites. Self-healing composites are fabricated to heal cracks, prevent any obstructed failure, and improve the lifetime of structures. They can self-diagnose their structure after being affected by external forces and repair damages and cracks to a certain degree. This review aims to provide information on the recent developments and prospects of self-healing composites and their applications in various fields such as aerospace, automobiles etc. Fabrication and characterization techniques as well as intrinsic and extrinsic self-healing techniques are discussed based on the latest achievements, including microcapsule embedment, fibers embedment, and vascular networks self-healing.

Additive manufacturing technology provides a gateway to completely new horizons for producing a wide range of components, such as manufacturing, medicine, aerospace, automotive, and space explorations, especially in non-conventional manufacturing processes. The present study analyzes the influence of the additive manufactured tool in electrochemical micromachining (ECMM) on machining beta titanium alloy. The influence of different machining parameters, such as applied voltage, electrolytic concentration, and duty ratio on material removal rate (MRR), overcut, and circularity was also analyzed. It was inferred that the additive manufactured tool can produce better circularity and overcut than a bare tool due to its higher corrosion resistance and localization effect. The additive manufactured tool can remove more material owing to its strong atomic bond of metals and higher electrical conductivity.

Surface composites are viable choices for various applications in the aerospace and automotive industries. Friction Stir Processing (FSP) is a promising method for fabricating surface composites. Aluminum Hybrid Surface Composites (AHSC) are fabricated using the FSP to strengthen a hybrid mixture prepared with equal parts of Boron carbide (B₄C), Silicon Carbide (SiC), and Calcium Carbonate (CaCO₃) particles. Different hybrid reinforcement weight percentages (reinforcement content of 5% (T1), 10% (T2), and 15% (T3)) were used in fabricating AHSC samples. Furthermore, different mechanical tests were performed on hybrid surface composite samples with different weight percentages of the reinforcements. Dry sliding wear assessments were performed in standard pin-on-disc apparatus as per ASTM G99 guidelines to estimate wear rates. The presence of reinforcement contents and dislocation behavior was investigated using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) studies. The results indicated that the Ultimate Tensile Strength (UTS) of sample T3 exhibited 62.63% and 15.17% higher than that of samples T1 and T2, respectively, while the Elongation (%) of T3 exhibited 38.46% and 15.38% lower than that of samples T1 and T2, respectively. Moreover, it was found that the hardness of sample T3 increased in the stir zone compared to samples T1 and T2, owing to its higher brittle response. The higher brittle response of sample T3 compared to samples T1 and T2 was confirmed by the higher value of Young’s modulus and the lower value of Elongation (%).

The use of additive manufacturing in fabricating composite components has been gaining traction in the past decade. However, some issues with mechanical performance still need to be resolved. The issue of material porosity remains a pertinent one which needs more understanding to be able to come up with more viable solutions. Different researchers have examined the subject; however, more research to quantitatively determine fabrication temperatures effects at the micro-scale are still needed. This study employed micro-CT scan analysis to quantitatively compare fabrication temperatures effect at 230 °C, 250 °C, 270 °C, and 290 °C on the mechanical properties of AM fabricated carbon-fiber-reinforces plastic (CFRP) composites, testing carbon fiber-reinforced polyamide (CF-PA) and carbon fiber-reinforced acrylonitrile butadiene styrene (CF-ABS) samples. This micro-CT examination followed an SEM evaluation, which was used to determine temperature effects on interlayer and intralayer porosity generation. The porosity volume was related to the mechanical properties, in which it was determined how temperatures influence porosity volumes. It was also determined that fabrication temperature generally affects semicrystalline composites more than amorphous composites. The overall porosity volumes from the interlayer and intralayer voids were determined, with the interlayer voids being more influential in influencing the mechanical properties.

Data-driven algorithms have been widely applied in predicting tool wear because of the high prediction performance of the algorithms, availability of data sets, and advancements in computing capabilities in recent years. Although most algorithms are supposed to generate outcomes with high precision and accuracy, this is not always true in practice. Uncertainty exists in distinct phases of applying data-driven algorithms due to noises and randomness in data, the presence of redundant and irrelevant features, and model assumptions. Uncertainty due to noise and missing data is known as data uncertainty. On the other hand, model assumptions and imperfection are reasons for model uncertainty. In this paper, both types of uncertainty are considered in the tool wear prediction. Empirical mode decomposition is applied to reduce uncertainty from raw data. Additionally, the Monte Carlo dropout technique is used in training a neural network algorithm to incorporate model uncertainty. The unique feature of the proposed method is that it estimates tool wear as an interval, and the interval range represents the degree of uncertainty. Different performance measurement matrices are used to compare the proposed method. It is shown that the proposed approach can predict tool wear with higher accuracy.