The objective of this research is the fabrication of biodegradable starch-based sandwich materials. The investigated sandwich structures consist of maize starch-based films as skins and biodegradable 3D-printed polylactic filaments (PLA) as the core. To investigate the tensile properties of the skins, conventional and nanocomposite films were prepared by a solution mixing procedure with maize starch and glycerol as the plasticizer, and they were reinforced with sodium montmorillonite clay, cellulose fibers and fiberglass fabric, with different combinations. Test results indicated a significant improvement in the mechanical and morphological properties of composite films prepared with sodium montmorillonite clay in addition with cellulose fibers and fiberglass fabric, with 20 wt% of glycerol. The morphology of the skins was also examined by scanning electron microscopy (SEM). Three orders of hierarchical honeycombs were designed for the 3D-printed core. To investigate how the skin material and the design of the core affect the mechanical properties of the starch-based sandwich, specimens were tested under a three-point bending regime. The test results have shown that the flexural strength of the biodegradable sandwich structure increased with the use of a second order hierarchy core and starch-based skins improved the strength and stiffness of the neat PLA-based honeycomb core. The bending behavior of the hierarchical honeycombs was also assessed with finite element analysis (FEA) in combination with experimental findings. Flexural properties demonstrated that the use of starch-based films and a PLA honeycomb core is a suitable solution for biodegradable sandwich structures.

The aim of the present study is to investigate the performance of two carbon fiber-reinforced composite polymers used to manufacture end-use parts via the fused filament fabrication (FFF) method. The materials under investigation were carbon fiber-reinforced Polyamide-6 (PA6-CF15) and carbon fiber-reinforced polyphenylene sulfide (PPS-CF15). To evaluate their mechanical properties and vibrational behavior, specimens were fabricated with four distinct infill patterns: grid, gyroid, triangle and hexagon. In particular, the vibrational behavior of the 3D-printed composites was determined by conducting cyclic compression testing, as well as modal tests. Additionally, the mechanical behavior of the reinforced polymers was determined by conducting both uniaxial tensile and compression tests, as well as three-point bending tests. The results of the mechanical experiments revealed that the grid pattern exhibited the best overall performance, while the gyroid pattern exhibited the greatest strength-to-weight ratio, making it the most durable infill for use with composite filaments. In vibration experiments, PA6-CF15 structures exhibited higher damping ratios than PPS-CF15, indicating superior damping capacity. Among the infill patterns, the hexagon pattern provided the greatest vibration isolation performance.

There are numerous engineering applications where Glass Fiber Reinforced Polymer (GFRP) composite tubes are utilized, such as desalination plants, power transmission systems, and paper mill, as well as marine, industries. Some type of machining is required for those various applications either for joining or fitting procedures. Machining of GFRP has certain difficulties that may damage the tube itself because of fiber delamination and pull out, as well as matrix deboning. Additionally, short machining tool life may be encountered while the formation of powder like chips maybe relatively hazardous. The present paper investigates the effect of process parameters for surface roughness of glass fiber-reinforced polymer composite pipes manufactured using the filament winding process. Experiments were conducted based on the high-speed turning Computer Numerical Control (CNC) machine using Poly-Crystalline Diamond (PCD) tool. The process parameters considered were cutting speed, feed, and depth of cut. Mathematical models for the surface roughness were developed based on the experimental results, and Analysis of Variance (ANOVA) has been performed with a confidence level of 95% for validation of the models.

Path planning is a vital challenge in robot navigation. In the real world, robots operate in 3D environments with various obstacles and restrictions. An improved artificial fish swarm algorithm (AFSA) is proposed to solve 3D path planning problems in environments with obstacles. The improved AFSA incorporates a 3D model of 24 possible movement points to more realistically simulate real-world robot movement capabilities. Several improvements are adopted, such as methods of simple and advanced 3D elimination. The 3D implementation of an agent’s navigation model, called an “obstacle heatmap”, is also presented. The use of a safety value factor and a total movement point factor in the AFSA’s objective function are introduced. The combination of improved AFSA and a ray-casting algorithm is also presented. Finally, a method called “multiple laser activation” is introduced to overcome both the main disadvantage of the application of AFSAs in path planning and the limitation of the finite number of possible movement points that appear when bio-inspired algorithms are applied to generate the optimal path in a grid environment. The resulting path is applied to real-world challenges with drones, coordinate measuring machines, and industrial robotic arms.

Path planning is an intertemporal problem in the robotics industry. Over the years, several algorithms have been proposed to solve it, but weaknesses are constantly identified by researchers, especially in creating an optimal path in a three-dimensional (3D) environment with obstacles. In this paper, a method to reduce the lengths of optimal 3D paths and correct errors in path planning algorithms is proposed. Optimization is achieved by combining the information of a generated two-dimensional (2D) path with the input 3D path. The 2D path is created by a proposed improved artificial fish swarm algorithm (AFSA) that contains several improvements, such as replacing the random behavior of the fish with a proposed one incorporating the model of the 24 possible movement points and utilizing an introduced model to assist the agent’s navigation called obstacles heatmap. Moreover, a simplified ray casting algorithm is integrated with the improved AFSA to further reduce the length of the final path. The improved algorithm effectually managed to find the optimal path in complex environments and significantly reduce the length of the formed path compared with other state-of-the-art methods. The path was implemented in real-world scenarios of drone and industrial robotic arm applications.

Collaborative robots are vital in Industry 5.0 operations. They are utilized to perform tasks in collaboration with humans or other robots to increase overall production efficiency and execute complex tasks. Aiming at a comprehensive approach to assembly processes and highlighting new applications of collaborative robots, this paper presents the development of a digital twin (DT) for the design, monitoring, optimization and simulation of robots’ deployment in assembly cells. The DT integrates information from both the physical and virtual worlds to design the trajectory of collaborative robots. The physical information about the industrial environment is replicated within the DT in a computationally efficient way that aligns with the requirements of the path planning algorithm and the DT’s objectives. An enhanced artificial fish swarm algorithm (AFSA) is utilized for the 4D path planning optimization, taking into account dynamic and static obstacles. Finally, the proposed framework is utilized for the examination of a case in which four industrial robotic arms are collaborating for the assembly of an industrial component.