Laser forming is an emerging manufacturing process capable of producing either uncomplicated and complicated shapes by employing a concentrated heating source. The heat source movement creates local softening, and a plastic strain will be induced during the rise of temperature and the subsequent cooling. This contactless forming process may be used for the simple bending of sheets and tubes or fabrication of doubly-curved parts. Different studies have been carried out over recent years to understand the mechanism of forming and predicting the bending angle. The analysis of process parameters and search for optimized manufacturing conditions are among the most discussed topics. This review describes the main recent findings in the laser forming of single and multilayer sheets, composite and fiber-metal laminate plates, force assisted laser bending, tube bending by laser beam, the optimization technique implemented for process parameters selection and control, doubly-curved parts, and the analytical solutions in laser bending. The main focus is set to the researches published since 2015.

The laser tube bending process (LTBP) is a new and powerful manufacturing method for bending tubes more accurately and economically by eliminating the bending die. The irradiated laser beam creates a local plastic deformation area, and the bending of the tube occurs depending on the magnitude of the heat absorbed by the tube and its material characteristics. The main bending angle and lateral bending angle are the output variables of the LTBP. In this study, the output variables are predicted by support vector regression (SVR) modeling, which is an effective methodology in machine learning. The SVR input data is provided by performing 92 experimental tests determined by the design of the experimental techniques. The measurement results are divided into two sub-datasets: 70% for the training dataset, and 30% for the testing dataset. The inputs of the SVR model are process parameters, which can be listed as the laser power, laser beam diameter, scanning speed, irradiation length, irradiation scheme, and the number of irradiations. Two SVR models are developed for the prediction of the output variables separately. The SVR predictor achieved a mean absolute error of 0.021/0.003, a mean absolute percentage error of 1.485/1.849, a root mean square error of 0.039/0.005, and a determination factor of 93.5/90.8% for the main/lateral bending angle. Accordingly, the SVR models prove the possibility of applying SVR to the prediction of the main bending angle and lateral bending angle in LTBP with quite an acceptable accuracy.

Laser forming is a powerful tool for fabricating complicated shapes economically. The pattern of laser movement (irradiating scheme) has an essential effect on the shaped form. In this article, the forming of a saddle-shaped surface will be investigated experimentally by the laser forming process. A spiral irradiating scheme is implemented to manufacture a saddle-shaped surface. The main idea of this study is the investigation of the simultaneous variations of the process parameters and their effect on the curvature of the final part. The process parameters of the study are the spiral pitch, number of spiral passes, and movement pattern (In-to-Out or reversely Out-to-In scanning path). The response surface methodology is selected for experimentation. The measurement of the deformation results shows that the deformations of laser-formed saddle-shaped surfaces decrease with an increase in the spiral pitch of the path. Additionally, the deformations of the saddle-shaped surface increase by increasing the number of spiral passes. The results demonstrate that the pattern movement has little effect on the deformations of laser-formed saddle-shaped surfaces and an Out-to-In spiral pattern movement is advised. At last, the proper input variables to obtain the maximum value of displacements for the saddle point are determined (10 mm spiral pitch, three spiral passes, and Out-to-In pattern movement).

Graphene quantum dots are zero-dimensional nanoparticles that are used widely in advanced composite materials such as filtration membranes, adsorbent materials, optical devices, biomedical applications (especially biosensors), flame retardancy, and automotive, aerospace, agricultural and environmental applications. In this article, the mechanical properties (flexural strength, flexural strain and elastic modulus) of polymer-based nanocomposites will be investigated. The main novelty of the current work is the green synthesis of graphene quantum dots which were extracted from lemon juice. XRD and FTIR tests have been conducted to determine the composition of the prepared powder. The polyester resin and graphene quantum dots were mixed with different weight percentages (0.25%, 0.5% and 1% wt. graphene) and processed to fabricate nanocomposite samples. The mechanical properties of the prepared samples were measured according to the ASTM D790-17 standard testing method. The experimental results show that the strength increased from 80 MPa to about 112 MPa (40% increase in strength) by adding 0.25% wt. graphene quantum dots. The flexural modulus decreased from 2.70 GPa to 2.06 GPa by adding 1% wt. graphene content (23% decrease). The flexural strain increased considerably (up to 14.2%) by adding 1% wt. graphene quantum dots. Consequently, the ductility of the nanocomposites increased by adding green synthesized graphene quantum dots. The fracture behavior changed from brittle fracture mode to ductile fracture mode by adding the graphene quantum dots. Additionally, a flame retardancy test has been carried out by implementing the UL-94 test. The fabricated nanocomposites showed fire retardancy due to char barrier formation on the surface of the nanocomposites.