In the current work, light weight epoxy bio‐composites are created for low‐cost technological applications using luffa aegyptiaca fiber and biochar particles derived from coco husk (CHB). This study aims to evaluate the effects of CHB particles added at different concentrations (3 vol% and 5 vol%) on the dynamic mechanical, ballistic and tribology behavior of epoxy composites constructed from luffa aegyptiaca fiber with different fiber loading (20%, 30% and 40 vol%). The composites are prepared using hand layup process provided with post curing operation. The combination of 3 vol% CHB particles and 40 vol% luffa aegyptiaca fiber having the improved viscoelastic properties by means of high storage modulus (5.05 GPa) and low loss factor (0.31). Moreover, this composite shows better ballistic properties in terms of low velocity impact energy (17.1 J). The optical image of impact damage behavior shows minimum damage of impactor on the composite and penetration effect found to be lower. This luffa aegyptiaca fiber reinforced epoxy composite also shows the lowest value of coefficient of friction (COF) with 0.48 and the lowest specific wear rate of 0.011 mm3/Nm. These epoxy composites made from luffa aegyptiaca fiber and CHB particles may be useful in a variety of engineering applications that can use materials for manufacture sporting goods, home furnishings, food packaging, and transportation.

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Additive manufacturing (AM) is a leading technology used in many fields, such as medicine and aerospace, to make prototypes and functional part fabrication. The energy requirements of the AM process are considerable and have serious consequences for environmental health and long-term viability. Research in both the private and public sectors has shifted its attention to the problem of predicting and optimising the amount of energy that AMs use.Material state, process operation, part and process design, working environment, and other factors all play a role in this problem. Existing research shows that the design-relevant aspects have a significant role in AM energy consumption (EC) modelling in reality, although this topic has not received enough attention. As a result, this research starts by analysing the design relevant features (DRFs) from the perspective of energy modelling.Before production, these features are normally decided by part designer (PD)and process operator (PO).An ANN driven cluster-aware enhanced spider monkey optimization algorithm (CAESMOA) is suggested to improve the energy utility relying on the novel modelling methodology. Deep learning is used to improve the global best of CAESMOA and solve a number of concerns, including speeding up search times. In order to verify the accuracy of the suggested modelling technique, DRFsare obtained from a functioning AM system in the production line. In our research, we use a normalisation strategy to filter out extraneous information. At the same time, optimization has been performed to direct PD and PO towards design and decision modifications that lessen the energy requirements of the specified AM system under investigation.The effectiveness of the suggested approach is examined, and the efficiency is also contrasted with that of other current methods. These statistics showed that our approach to energy optimization in AM delivered the most trustworthy outcomes.

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