This work puts forward an original autonomous planning and control framework addressing inherent modeling complexity limit through efficient heterosis between latency-connective graph estimation and generative exploration with an aim to enhance trajectory quality and resiliency in unpredicted conditions. The holistic approach encompasses state and cost prediction facilitated via morphable signature mechanism utilizing anti-cloak characteristics derived from environmental graph. In principle, a dynamic graph neural network is proposed with regards to adaptively capture essential influence caused by interactive agents and reciprocal belief augmentation. Moreover, high efficiency exploration is concerted with signature-enhanced prediction system for non-ideal perception conditions. The exploration scheme takes advantage of confidence optimization function to generate trajectory refinement over non-conventional operating circumstances.
To satisfy recent stringent exhaust gas regulations, large amounts of Rh and Pd have been often employed in three-way catalysts (TWCs) as main active components. However, application of Pt-based TWCs are limited due to their lower thermal stability than Pd. Previously, we found that Pt-based TWCs with a small amount of CeO2 showed high catalytic performance in gasoline vehicles test. Especially, calcined CeO2 at high temperature before Pt loading (cal-CeO2) showed higher catalytic activity than untreated CeO2 after endurance at 1000 degree centigrade. This result could be attributed to higher redox performance and Pt dispersion derived from strong interaction between Ce and Pt. Even though cal-CeO2 has low specific surface area (SSA) given by preliminary calcination, it shows strong effects on catalytic performance. In other word, improvement of its SSA could be the most powerful way to prepare highly active Pt catalysts.
The microstructure and mechanical properties of the Al-Si-Mg alloy with bulk and lattice structure produced by Laser-powder bed fusion additive manufacturing were systematically investigated. And then, the microstructure behavior of Al-Si-Mg alloys according to As-built and heat treatment was closely analyzed. Firstly, through grain size analysis, the cause of mechanical properties higher than casting materials and similar to forging materials could be analyzed. Secondly, mechanical changes according to the Mg2Si reinforced phase and cell-wall morphology after heat treatment were investigated. The Al-Si-Mg bulk and lattice structures are composed of a cell structure consisting of α-Al and eutectic Si. With heat treatment, needle-shape Mg2Si precipitates in the α-Al matrix. Simultaneously, collapse of the cell-wall morphology occurs.
Austenitic stainless steel (1.4837Nb) is widely used for turbo housing and other components which are subjected to elevated temperature conditions. Due to assembly constraints, geometry limitation, and particularly high temperatures, thermomechanical fatigue (TMF) issue is commonly seen in the service of the components. Therefore, it is critical to understand the TMF behavior of the steel. In the present study, a series of fatigue tests including isothermal low cycle fatigue (LCF) test at elevated temperatures up to 1000°C, in-phase and out-of-phase TMF tests in different temperature ranges have been conducted. Both creep and oxidation are active in these conditions, and their contributions to the damage of the steel are evaluated. A Chaboche viscoplasticity model for constitutive simulation, and a DTMF damage model for life prediction are developed and validated at specimen level.
As a key tool to maintain urban cleanliness and improve the road environment, road cleaning vehicles play an important role in improving the quality of life of residents. However, the traditional road cleaning vehicle requires the driver to monitor the road garbage situation at all times and manually control the cleaning process, resulting in an increase in the driver's work intensity. To solve this problem, this paper proposes a garbage recognition algorithm based on improved YOLOv5, which aims to reduce labor consumption and improve the efficiency of road cleaning.Firstly, the lightweight network MobileNet-V3 is used to replace the backbone feature extraction network of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed.
As a key technology of intelligent transportation system, vehicle type recognition plays an important role in ensuring traffic safety, optimizing traffic management and improving traffic efficiency, which provides strong support for the development of modern society and the intelligent construction of traffic system. Aiming at the problems of large number of parameters, low detection efficiency and poor real-time performance in existing vehicle recognition algorithms, this paper proposes an improved vehicle recognition algorithm based on YOLOv5. Firstly, the lightweight network model MobileNet-V3 is used to replace the backbone feature extraction network CSPDarknet53 of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed.
Aluminum alloy has become an indispensable part of the automotive industry because of its excellent mechanical properties such as lightweight, high strength, high reliability, maintainability, and low cost. Aluminum alloy is used in automobiles, such as engine blocks, cylinder heads, intake manifolds, brake components, and fuel tanks. Fatigue and fracture are the main reasons for its engineering failure. Surface strengthening techniques, such as ultrasonic shot peening (USP), are often used to improve the fatigue resistance of aluminum alloys. This article expounds on the working principle of ultrasonic shot peening and elucidates the influence of USP process parameters on the surface characteristics of aluminum alloy. Experimental results observed the effects of USP parameters on surface properties such as surface roughness, microhardness, and surface morphology.
The transition towards electrified vehicles marks the release of new products in the automotive market and a continuing effort to optimize their performance. The electric motor is a key component in the electric drive where a further optimization of power loss, power density and cost can be achieved. In the laminated core of the electric motor additional benefits can be realized. This paper presents a new method to produce laminated stacks with a new combination of processes that distinguishes it from conventional methods. The manufacturing sequence includes a unique order of precision blanking, dedicated heat treatment and gluing. The separate and combined effects of these processes are compared with available state-of-the-art solutions. Such solutions typically contain some of the individual features but not the combination that enhances the overall effect. The heat treatment reduces power losses by releasing process stresses which decreases hysteresis losses.
At the dawn of battery electric vehicles (BEVs), protection of automotive battery systems as well as passengers, especially from severe side impact, has become one of the latest and most challenging topics in the BEV crashworthiness designs. Accordingly, two material-selection concepts are being justified by the automotive industry: either heavy-gauge extruded aluminum alloys or light-gauge advanced high-strength steels (AHSSs) shall be the optimal materials to fabricate the reinforcement structures to satisfy both the safety and lightweight requirements. In the meantime, such a justification also motivated an ongoing C-STARTM (Cliffs Steel Tube as Reinforcement) Protection project, in which the all-AHSS(s) reinforcement beams, essentially a series of modularized steel tube assemblies, were demonstrated both experimentally and virtually to be more cost-efficient, sustainable, design-flexible, and manufacturable than the equivalent extruded aluminum alloy beams.
Strength, creep and fatigue of the chassis components are greatly influenced by the material used and its manufacturing process. Alloy wheel is one of the critical chassis components manufactured using the casting process. Secondary Dendrite Arm Spacing (SDAS) is one of the important microstructural parameters generated during the solidification stage of the casting process. SDAS has a significant role in altering the mechanical properties and the behavior of the component. Variation in solidification time and alloy composition will have a major impact in SDAS. The combined effect of SDAS with microstructural variations and the strength behavior has not been studied in earlier literature for an alloy wheel. The scope of this study is to perform casting simulation for an alloy wheel, predict the SDAS and capture the variation of mechanical properties (yield strength, ultimate tensile strength & elongation).
Leaf Springs are commonly used as a suspension in heavy commercial vehicle for higher load carrying capacity. The leaf springs connects the vehicle body with road profile through axle & tire assembly. It provides the relative motion between the vehicle body and road profile for improving the ride & handling performance. The leaf springs are designed to provide the linear stiffness and uniform strength characteristics throughout its travel. Leaf springs are generally subjected to dynamic loads which are induced due to different loads & driving patterns. Leaf spring design should be robust as any failure in leaf springs will put vehicle safety at risk and cost the vehicle manufacturer reputation. The design of a leaf spring based on the conventional methods predicts the higher stress levels at the leaf spring center clamp location and stress levels gradually reduce from the center to free ends of the leaf spring.
This specification covers a titanium alloy in the form of bars, wire, forgings, flash-welded rings, and stock for forgings or flash-welded rings up through 6.000 inches (152.40 mm) in nominal diameter or distance between parallel sides (see 8.6).
This specification covers an aluminum alloy in the form of extruded bars, rods, wire, profiles, and tubing, flash-welded rings fabricated from extruded stock, and stock for flash-welded rings (see 8.6).
This specification covers a corrosion- and heat-resistant nickel alloy in the form of sheet, strip, and plate 0.010 to 2.000 inches (0.25 to 50.80 mm), inclusive, in nominal thickness.