Energy absorption is a key performance criterion for several engineering structures. Lightweight lattice structures are better suited for this purpose. The convolute design patterns that exist in nature are proven effective for several engineering applications. In this paper, a George lily flower leaf is considered to build a novel 3D open lattice pattern for specific energy absorption (SEA) purposes. A multi-cellular specimen is designed and fabricated using Vat photopolymerization 3D printing process. Quasi-static compression tests have been conducted and the performance of proposed structure is compared with 2.5D closed thin-walled structures and found the proposed 3D open lattice structure has shown significant improvement in SEA over other thin-walled structures.

The implementation of artificial intelligence techniques is increasing rapidly in recent years to solve numerous engineering problems. Assembly sequence planning is one of the prominent complex combinatorial problem draw attention of industrial engineers to economize the overall manufacturing cost by minimizing the assembly time and energy. Due to large search space and multiple assembly predicate criteria, researchers are motivated towards efficient utilization of AI techniques to address the problem. Literature review on various artificial intelligence techniques for obtaining the optimal assembly sequence planning are analyzed and the limitations of the existed methodologies are discussed in detail. This review provides an outlook for the researchers on various artificial intell1igent techniques which will be useful to carry out research for obtaining the optimum assembly sequence planning while qualifying various assembly predicate criteria.

In recent days, many interacted shape products have been developed by manufacturing industries for different applications in various fields such as defense, aerospace, and space centers. In manufacturing, 30% of time consumption is due to assembly operation compared with the remaining processes in manufacturing. It is very difficult to get optimal sequence because assembly sequence planning is a multimodel optimization problem. As the number of parts in the assembly increases, the possible number of sequences increases exponentially therefore obtaining the optimal assembly sequence becomes more difficult and time consuming. There exist many mathematical algorithms to obtain optimal assembly sequences. But, recent studies state that they perform poorly when it comes to multiobjective optimal assembly sequence. In recent years, researchers have developed several soft computing-based algorithms for solving assembly sequence problems. In this paper, assembly subset detection method has been introduced. The proposed method is applied for the first time to solve assembly sequence problems. This method eliminates those assembly sets that have more directional changes and require more energy. The method is compared with other algorithms, namely, genetic algorithm (GA), enhanced GA, ant colony optimization (ACO), memetic algorithm, imperialistic harmonic search algorithm, and flower pollination algorithm (FPA), and is found to be successful in achieving the optimal assembly sequence for an industrial product with smaller number of iterations.

Novel Cellular lattice structures with lightweight designs are gaining more interest in the automobile and aerospace sectors. Additive manufacturing technologies have focused on designing and manufacturing cellular structures in recent years, increasing the versatility of these structures because of the significant benefits like high strength-to-weight ratio. In this research, a novel hybrid type of cellular lattice structure is designed, bio-inspired from the circular patterns seen in the bamboo tree structure and the overlapping patterns found on the dermal layers of fish-like species. The unit lattice cell with varied overlapping areas with a unit cell wall thickness of 0.4 to 0.6 mm. Fusion 360 software models the lattice structures with a constant volume of 40 × 40 × 40 mm. Utilizing the stereolithography (SLA) process and a vat polymerization type three-dimensional printing equipment is used to fabricate the 3D printed specimens. A quasi-static compression test was carried out on all 3D printed specimens, and the energy absorption capacity of each structure was calculated. Machine learning technique like the Artificial neural network (ANN) with Levenberg–Marquardt Algorithm (ANN-LM) was applied to the present research to predict the energy absorption of the lattice structure with parameters such as overlapping area, wall thickness, and size of the unit cell. The k-fold cross-validation technique was applied in the training phase to get the best training results. Overall, the results obtained using the ANN tool are validated and can be a favourable tool for lattice energy prediction with available data.

The 2.5-dimensional (2.5D) infill structures are gaining more attention due to their better energy absorption capabilities and lightweight. The additive manufacturing eases the fabrication of these structures and made them accessible for industrial and transport applications. The present work is focused on enhancing energy absorption capability, and mean crush force (MCF) for a novel nature-inspired 2.5D infilled structure through nature-inspired 2.5D geometries. Six nature-inspired geometries are considered for a comparative analysis with the proposed novel snowflake-inspired 2.5D infilled structure to sustain under quasi-static out-plane loading. These structures are cylindrical shells filled with different infill configurations and maintained at constant volume. The structures are fabricated using three-dimensional printing through the digital light processing technique with photopolymer resin and tested under compressive out-plane quasi-static loading conditions. Numerical simulations were performed by ANSYS and compared with experimental results to corroborate the reliability. The effect of infill configurations and supported ribs on the crushing behaviour is discussed in detail. The results reveal a significant improvement in MCF, specific energy absorption, and confined crushing deformation for the proposed lightweight 2.5D infill structures.