The ultrasonic welding (UW) technique is an ultra-fast joining process, and it is used to join thermoplastic composite structures, and provides an excellent bonding strength. It is more cost-efficient as opposed to the conventional adhesive, mechanical and other joining methods. This review paper presents the detailed progress made by the scientific and research community to date in the direction of the UW of thermoplastic composites. The focus of this paper is to review the recent development of the ultrasonic welding technique for thermoplastic composites to thermoplastic composites, and to dissimilar materials. Different ultrasonic welding modes and their processing parameters, namely, weld time, weld pressure, amplitude, type of energy directors (EDs) affecting the welding quality and the advantages and disadvantages of UW over other bonding techniques, are summarized. The current state of the ultrasonic welding of thermoplastic composites and their future perspectives are also deliberated.

Joining large and complex polymer–matrix composite structures is becoming increasingly important in industries such as automobiles, aerospace, sports, wind turbines, and others. Ultrasonic welding is an ultra-fast joining process and also provides excellent joint quality as a cost-effective alternative to other joining processes. This research aims at investigating the welding characteristics of novel methyl methacrylate Elium^®, a liquid thermoplastic resin. Elium^® is the first of its kind of thermoplastic resin, which is curable at room temperature and is suitable for mass production processes. The welding characteristics of Elium^® composites were investigated by optimizing the welding parameters with specially designed integrated energy directors (ED) and manufactured using the Resin transfer molding process. The results showed a 23% higher lap shear strength for ultrasonically welded composite joints when compared to the adhesively bonded joints. The optimized welding time for the ultrasonic welded joint was found to be 1.5 s whereas it was 10 min for the adhesively bonded joint. Fractographic analysis showed the significant plastic deformation and shear cusps formation on the fractured surface, which are typical characteristics for strong interfacial bonding.

The current research work presents a first attempt to investigate the welding attributes of Elium^® thermoplastic resin and the fusion bonding using ultrafast ultrasonic welding technique. The integrated energy director (ED) polymer-matrix composites (PMCs) panel manufacturing was carried out using the Resin Transfer Moulding (RTM) technique and the scheme is deduced to manufacture a bubble-free panel. Integrated ED configurations and flat specimens with Elium^® film of different thickness at the interface were investigated for ultrasonic welding optimization. Optimised weld time for integrated ED and flat Elium^® panels with film (0.5 mm thick) configuration was found to be 1 s and 5.5 s, respectively. The ED integrated configuration showed the best welding results with a lap shear strength of 18.68 MPa. The morphological assessment has shown significant plastic deformation of Elium^® resin and the shear cusps formation, which enhances the welding strength. This research has the potential to open up an excellent and automated way of joining Elium^® composite parts in automotive, wind turbines, sports, and many other industrial applications.

Advanced manufacturing techniques, such as automated fiber placement and additive manufacturing enables the fabrication of fiber-reinforced polymer composite components with customized material and structural configurations. In order to take advantage of this customizability, the design process for fiber-reinforced polymer composite components needs to be improved. Machine learning methods have been identified as potential techniques capable of handling the complexity of the design problem. In this review, the applications of machine learning methods in various aspects of structural component design are discussed. They include studies on microstructure-based material design, applications of machine learning models in stress analysis, and topology optimization of fiber-reinforced polymer composites. A design automation framework for performance-optimized fiber-reinforced polymer composite components is also proposed. The proposed framework aims to provide a comprehensive and efficient approach for the design and optimization of fiber-reinforced polymer composite components. The challenges in building the models required for the proposed framework are also discussed briefly.