Conventional machining processes such as turning, milling, drilling, abrasive cutting, and grinding are commonly used to bring composite parts to final shape and assembly requirements. However, due to the layered nature of these materials, their machining may generate undesirable defects such as delamination and high surface roughness. The service life of composite components is believed to be highly dependent on machining quality and damage due to machining may result in scraping expensive parts. In this work, an experimental investigation was conducted to determine the effect of spindle speed, feed rate, and tool condition on machining quality of carbon fiber reinforced polymer (CFRP) composites during edge trimming operation. Machining quality was quantified in terms of average delamination depth and surface roughness. Delaminations were also characterized by their type and frequency of occurrence. It was found that average delamination depth and surface roughness increase with an increase in feed rate and an increase in cutting distance and decrease with an increase in spindle speed. There is a strong relationship between delamination damage and effective chip thickness. The cutting conditions for best machining quality are high spindle speed and low feed rate, which correspond to small effective chip thickness. The most frequent delamination type was found to be Type I/II.

Friction stir welding (FSW) and friction stir spot welding (FSSW) techniques are becoming widely popular joining techniques because of their increasing potential applications in automotive, aerospace, and other structural industries. These techniques have not only successfully joined similar and dissimilar metal and polymer parts but have also successfully developed polymer-metallic hybrid joints. This study classifies the literature available on the FSW and FSSW of thermoplastic polymers and polymer composites on the basis of joining materials (similar or dissimilar), joint configurations, tooling conditions, medium conditions, and study types. It provides a state-of-the-art and detailed review of the experimental studies available on the FSW and FSSW between similar thermoplastics. The mechanical properties of FSW (butt- and lap-joint configurations) and FSSW weld joints depend on various factors. These factors include the welding process parameters (tool rotational speed, tool traverse speed, tool tilt angle, etc.), base material, tool geometry (pin and shoulder size, pin profile, etc.) and tool material, and medium conditions (submerged, non-submerged, heat-assisted tooling, cooling-assisted tooling). Because of the dependence on many factors, it is difficult to optimize the welding conditions to obtain a high-quality weld joint with superior mechanical properties. The general guidelines are established by reviewing the available literature. These guidelines, if followed, will help to achieve high-quality weld joints with least defects and superior mechanical properties. Apart from parametric-based studies, the statistical-based studies (e.g., analysis of variance (ANOVA)-based studies) are covered, which helps with the determination of the influential parameters that affect the FSW and FSSW weld joint strength. Also, the optimal ranges of the most influential process parameters for different thermoplastic materials are established. The current work on the development of general guidelines and determination of influential parameters and their operating ranges from published literature can help with designing smart future experimental studies for obtaining the global optimum welding conditions. The gaps in the available literature and recommendations for future studies are also discussed.

Metamaterials, also known as lattice‐structured materials, imitate the multifunctionality of natural architects as tailoring their physical properties is associated with manipulating their microstructure. As the recent evolution of additive manufacturing enables the creation of intricate geometries with minimal material wastage, improving the design to manufacturing cycle of lattice structured materials has become one of the trending research areas. Triply periodic minimal surface (TPMS) and plate lattice materials are renowned for their exceptional mechanical behavior in lightweight applications. Apparently, several types of design optimization strategies are explored to maximize their performance for better biocompatibility and mechanical loading resistance. Some of these strategies include functional gradation and multimorphology hybridization that are comprehensively described in this review. Their benefits and drawbacks are highlighted with a focus on TPMS and plate lattice materials. The review anticipates the utilization of automated design exploration methods (i.e., topology optimization and data‐driven methods) to further enhance the design optimization procedure of lattice structured materials.

Carbon fiber reinforced polymers (CFRPs) have found wide-ranging applications in numerous industrial fields such as aerospace, automotive, and shipping industries due to their excellent mechanical properties that lead to enhanced functional performance. In this paper, an experimental study on edge trimming of CFRP was done with various cutting conditions and different geometry of tools such as helical-, fluted-, and burr-type tools. The investigation involves the measurement of cutting forces for the different machining conditions and its effect on the surface quality of the trimmed edges. The modern cutting tools (router tools or burr tools) selected for machining CFRPs, have complex geometries in cutting edges and surfaces, and therefore a traditional method of direct tool wear evaluation is not applicable. An acoustic emission (AE) sensing was employed for on-line monitoring of the performance of router tools to determine the relationship between AE signal and length of machining for different kinds of geometry of tools. The investigation showed that the router tool with a flat cutting edge has better performance by generating lower cutting force and better surface finish with no delamination on trimmed edges. The mathematical modeling for the prediction of cutting forces was also done using Artificial Neural Network and Regression Analysis.