Dynamic rupture and crushing of an extruded tube using artificial neural network(ANN)approximation method

A numerical study of the crushing of thin-walled circular aluminum tubes has been carried out to investigate the crashworthiness behaviors under axial impact loading. These kinds of tubes are usually used in automobile and train structures to absorb the impact energy. Previous researches show that thin-walled circular tube has the highest energy absorption under axial impact amongst different structures. In this work, the crushing between two rigid flat plates and the tube rupture by 4 and 6 blades cutting tools is modeled with the help of ductile failure criterion using the numerical method. The tube material is aluminum EN AW-7108 T6 and its length and diameter are 300 mm and 50 mm, respectively. Using the artificial neural network(ANN), the most important surfaces of energy absorption parameters, including the maximum displacement of the striker, the maximum axial force, the specific energy absorption and the crushing force efficiency in terms of impact velocity and tube thickness are obtained and compared to each other. The analyses show that the tube rupture by the 6 blades cutting tool has more energy absorption in comparison with others. Furthermore, the results demonstrate that tube cutting with the help of multi-blades cutting tools is more stable, controllable and predictable than tube folding.

Design and energy absorption enhancement of vehicle hull under high dynamic loads

V-shape hulls are widely used in peacekeeping efforts such as demining vehicles in order to deflect the blast energy and reduce the effects of mine blast. Blast resistant design and energy absorption enhancement of V-shape plates were carried out using finite element analysis package ABAQUS. Various geometries of V-shape plates with and without interlayer of materials like Al-foams and honeycomb were employed to analyze their effects on the deformation of the plate and applied stresses and strains. The results obtained show that application of metallic foams leads to better response of the plate and consequently results in more energy dissipation, less dame to vehicle and enhances crew survivability.

Crop diagnostic system:A robust disease detection and management system for leafy green crops grown in an aquaponics facility

Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle,just like traditional agriculture.The early detection of diseases is crucial to witnessing the efficiency and progress of the aquaponics system.Aquaponics combines recirculating aquaculture and soilless hydroponics methods and promises to ensure food security,reduce water scarcity,and eliminate carbon footprint.For the large-scale imple-mentation of this farming technique,a unified system is needed that can detect crop diseases and support re-searchers and farmers in identifying potential causes and treatments at early stages.This study proposes an automatic crop diagnostic system for detecting biotic stresses and managing diseases in four leafy green crops,lettuce,basil,spinach,and parsley,grown in an aquaponics facility.First,a dataset comprising 2640 images is con-structed.Then,a disease detection system is developed that works in three phases.The first phase is a crop clas-sification system that identifies the type of crop.The second phase is a disease identification system that determines the crop's health status.The final phase is a disease detection system that localizes and detects the diseased and healthy spots in leaves and categorizes the disease.The proposed approach has shown promising results with accuracy in each of the three phases,reaching 95.83%,94.13%,and 82.13%,respectively.The final dis-ease detection system is then integrated with an ontology model through a cloud-based application.This ontol-ogy model contains domain knowledge related to crop pathology,particularly causes and treatments of different diseases of the studied leafy green crops,which can be automatically extracted upon disease detection allowing agricultural practitioners to take precautionary measures.The proposed application finds its significance as a de-cision support system that can automate aquaponics facility health monitoring and assist agricultural practi-tioners in decision-making processes regarding crop and disease management.

Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system

Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features.These techniques are also being integrated into modern farming systems,such as aquaponics,to address the challenges hindering its commercialization and large-scale implementation.Aquaponics is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture,that promises to address food security issues.To complement the current research efforts,a methodology is proposed to automatically measure the morphological traits of crops such as width,length and area and estimate the effective plant spacing between grow channels.Plant spacing is one of the key design parameters that are dependent on crop type and its morphological traits and hence needs to be monitored to ensure high crop yield and quality which can be impacted due to foliage occlusion or overlapping as the crop grows.The proposed approach uses Mask-RCNN to estimate the size of the crops and a mathematical model to determine plant spacing for a self-adaptive aquaponics farm.For common little gem romaine lettuce,the growth is estimated within 2 cm of error for both length and width.The final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics system.The relevant knowledge about crop characteristics and optimal plant spacing is extracted from ontology and compared with results obtained from the final model to suggest further actions.The proposed application finds its signifi-cance as a decision support system that can pave the way for intelligent system monitoring and control.