Browse Topic: Logistics
In the future, autonomous drones could be used to shuttle inventory between large warehouses. A drone might fly into a semi-dark structure the size of several football fields, zipping along hundreds of identical aisles before docking at the precise spot where its shipment is needed.
Topology optimization (TO) in electrochemical systems has recently attracted many researchers. Previous studies suggested minimal performance differences between 2D and 3D designs, indicating that 2D models suffice to enhance performance, especially in unidirectional flow scenarios. A later study found that the concentration distribution in an optimized 2D flow system differed from that in a unidirectional flow system. We posited that pulsating flow could further enhance the performance of such systems. First, we initiated TO for a diffusion-reaction system in a steady state. The optimized structure obtained from this process served as the foundation for subsequent investigations involving a pulsating flow source in convection-diffusion-reaction systems. We introduced two different systems with distinct flow natures: one characterized by a flow nature of 1D and the other by a flow nature of 2D. The results demonstrated that the optimized structure with a heterogeneous distribution
Fatigue design is invariably of prior concern for the automotive industry, no matter of the evolution of the mobility market: at first because carmakers must stay compliant with general structural integrity requirements for reliability, notably applicable to the chassis system, then due to the endless competition for lightweighting in order to mitigate product costs and/or enhance vehicle efficiency. In the past, this key performance was often tackled by basic reference load cases, making use of the simplest signal content, e.g. sinus functions, to practice constant amplitude loads on test rigs and for computations, respectively. Nowadays, full time series coming from proving ground measurements, or any corresponding virtual road load data computations, may be applied to feed complex vehicle computations for virtual assessment and complex test facilities for final approval, under variable amplitude loads. In between, the concept of load spectra (i.e. distribution of amplitudes with
In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers and relevant personnel at the OEM, enabling proactive measures to address any Quality deviations. 100% data is visualized in an integrated dashboard which acts as a
Accurate object pose estimation refers to the ability of a robot to determine both the position and orientation of an object. It is essential for robotics, especially in pick-and-place tasks, which are crucial in industries such as manufacturing and logistics. As robots are increasingly tasked with complex operations, their ability to precisely determine the six degrees of freedom (6D pose) of objects, position, and orientation, becomes critical. This ability ensures that robots can interact with objects in a reliable and safe manner. However, despite advancements in deep learning, the performance of 6D pose estimation algorithms largely depends on the quality of the data they are trained on.
This paper presents a new regression model-based method for accurate predictions of stiffness of different glass laminate constructions with a point-load bending test setup. Numerical FEA models have been developed and validated with experimental data, then used to provide training data required for the statistical model. The multi-variable regression method considered six input variables of total glass thickness, thickness ratio of glass plies as well as high-order terms. Highly asymmetrical, hybrid laminates combining a relatively thick soda-lime glass (SLG) ply joined with a relatively thin Corning® Gorilla® Glass (GG) ply were analyzed and compared to standard symmetrical SLG-SLG constructions or a monolithic SLG with the same total glass thickness. Both stiffness of the asymmetrical laminates and the improvement percentage over the standard symmetrical design can be predicted through the model with high precision.
Battery cell aging and loss of capacity are some of the many challenges facing the widespread implementation of electrification in mobility. One of the factors contributing to cell aging is the dissimilarities of individual cells connected in a module. This paper reports the results of several aging experiments using a mini-module consisting of seven 5 Ah 21700 lithium-ion battery cells connected in parallel. The aging cycle comprised a constant current-constant voltage charge cycle at a 0.7C C-rate, followed by a 0.2C constant current discharge, spanning the useful voltage range from minimum to maximum according to the cell manufacturer. Charge and discharge events were separated by one-hour rest periods and were repeated for four weeks. Weekly reference performance tests were executed to measure static capacity, pulse power capability and resistance at different states of charge. All diagnostics were normalized with respect to their starting numbers to achieve a percentage change
Electric vehicle (EV) growth may be stagnant in certain market sectors amid pushback on EV mandates along with lingering infrastructure and TCO concerns, but in terminal tractor operations electrification is growing in demand. As part of its initial Run on Less - Electric demonstration, the North American Council for Freight Efficiency (NACFE) concluded that terminal tractors are “one of the best, if not the best, paths for heavy-duty tractor fleets to learn about and implement a BEV in a fleet operation.” Fleets operating in ports, intermodal yards and other logistics hubs have a new option from which to choose now that Kalmar officially began sales of its Ottawa T2 EV electric terminal tractor. The order book opened at the Technology & Maintenance Council's (TMC) 2025 Transportation Technology Exhibition in Nashville, Tennessee, in March. Kalmar states that select customers have already placed preorders. Initial deliveries will begin in Q2 2025.
In an era where technological advancements are rapid and constant, the U.S. Army will need a more agile and efficient approach to modernizing systems on succeeding generations of Army vehicles. Legacy platforms like Abrams, Stryker, and Bradley vehicles use multiple mission computers tied to individual sensors that often required the addition of “boxes” to accommodate new capabilities, which could take years to deploy and drove sustainment costs up due to vendor lock. In addition, this antiquated approach doesn't leverage data to converge effects across the formation in a multi-domain environment. Centralized, common computing as detailed in GCIA would help solve this problem, potentially linking all major subsystems and providing higher-speed processing to assess large datasets in real time with AI and ML algorithms. By using a common, open architecture computer, the Army will be able to rapidly integrate new capabilities inside one box, versus adding multiple boxes. This pivotal
In an era where technological advancements are rapid and constant, the U.S. Army will need a more agile and efficient approach to modernizing systems on succeeding generations of Army vehicles. Legacy platforms like Abrams, Stryker, and Bradley vehicles use multiple mission computers tied to individual sensors that often required the addition of “boxes” to accommodate new capabilities, which could take years to deploy and drove sustainment costs up due to vendor lock. In addition, this antiquated approach doesn’t leverage data to converge effects across the formation in a multi-domain environment. Centralized, common computing as detailed in GCIA would help solve this problem, potentially linking all major subsystems and providing higher-speed processing to assess large datasets in real time with AI and ML algorithms. By using a common, open architecture computer, the Army will be able to rapidly integrate new capabilities inside one box, versus adding multiple boxes. This pivotal
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