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Cameras and image processing hardware are rapidly evolving technologies, which enable real-time applications for passenger cars, ground robots, and aerial vehicles. Visual odometry (VO) algorithms estimate vehicle position and orientation changes from the moving camera images. For ground vehicles, such as cars, indoor robots, and planetary rovers, VO can augment movement estimation from rotary wheel encoders. Feature-based VO relies on detecting feature points, such as corners or edges, in image frames as the vehicle moves. These points are tracked over frames and, as a group, estimate motion. Not all detected points are tracked since not all are found in the next frame. Even tracked features may not be correct since a feature point may map to an incorrect nearby feature point. This can depend on the driving scenario, which can include driving at high speed or in the rain or snow.

Atmospheric pressure plasma sources are new devices for modifying the surface condition of engineering materials such as thermoplastic and thermoset-based composites. Because they operate at ambient conditions, these plasma systems can be used on a production line as a pre-treatment solution prior to painting or adhesive bonding to significantly improve adhesion strength. However, their efficient use requires sound understanding on how they modify the surface state of materials and, by the same token, how these modifications can be detected and quantified as regards their ability to provide high-strength adhesive joints. Polypropylene, since it is one of the most difficult-to-bond thermoplastic polymers and, at the same time, one of the most interesting polymers for the automotive industry (due to low cost, widespread use in the formulation of composites, lightweight and recyclability), was used in this paper as a model polymer.

The need of weight reduction for fuel reduction and CO₂ regulations enforces the use of light-weight materials for structural parts also. The importance of reinforced composites will grow in this area. While the structural behavior and the simulation up to high strain-rate processes for those materials have been in the focus of investigation for many years, nowadays the simulation of high cycle fatigue behavior is getting important as well. Efficient fatigue analysis for metals was developed by understanding the microscopic behavior (crack nucleation and initiation) and bringing it to the macroscopic level by combining it with the matching test data (SN curves, etc.). Similar approaches can be applied to composite materials as well.

The advances in computer applications have been increasing in the recent years. The applications of Artificial Intelligence (AI) in the product design software, and the advancements in the Direct Digital Manufacturing (DDM) such as Additive Manufacturing have been leading the aerospace and automobile industries into the next level. AI assisted generative design helps the designers to reduce the weight of the structures without compensating the strength of the structures. The topology optimization is one of the subsets of the generative design which commonly used by the designers to design and redesign many components for weight reduction. This article studies the applications of topology optimization for an aerospace and an automobile components for design modification and weight reduction. The prototype of the optimized components are printed using FDM 3D printing for examining the shape optimization. The stress analyses of the components are analyzed using FEM.

The 3D Printing (3DP) technology due to its greatest strength, resistance to wear and corrosion to oxidizing agents and has good temperature resistance with durable one. The present article describes the effect of print orientation and infill density of 3DP route on mechanical and tribological properties of PETG filament. The 3DP parameters like layer thickness, slicing, speed, feed are kept as constant and by varying the print orientation (X, Y, Z) with infill density (50%, 75%, 100%) was printed to check the effect of it on mechanical and tribological properties like hardness, impact strength, ultimate tensile strength, flexural strength, wear rate and coefficient of friction. The results shows that all the tested mechanical and tribological properties increase by around 30-60% when the orientation is in the X direction at infill density of 100%.

Wire Electrical Discharge Machining (WEDM) is a contemporary approach of material removal which is conceived from the concept of Electrical Discharge Machining process. Wire Spark Erosion Machining which is known as WEDM, predominantly employed for removing material from hard materials and also especially used for making intricate shapes on any electrically conductive work material with irrespective of the hardness. Composite materials offers improved mechanical properties depends upon the constituents to be added. Graphene is identified as outstanding reinforcing element which provide support to enhance the desired properties of aluminium metal matrix composites in a considerable manner. In this present exploration an analysis has been performed on WEDM of Al-GNP composites. Pulse on time (μs), pulse off time (μs) and servo voltage (V) are deemed as input process parameters in this present exploration.

The objective of the study is to investigate the effect of current pulsation frequency on the weld bead microstructure, segregation and hardness of Hastelloy C-2000 weldments. Bead on Plate (BoP) welds were made by using Pulsed Current Gas Tungsten Arc Welding method (PCGTAW) at eleven different frequencies. The weld bead width and depth of penetration was measured with the help of Dinolite macro analyzer. The microstructure of weldments are further examined through optical microscope and Scanning Electron Microscopy (SEM) to identify the type of grain, grain coarsening and extent of the Heat Affected Zone (HAZ). The grain structure turn into finer and equiaxed in all cases and there was an optimum frequency range over which the significant grain refinement was observed. Microsegregation of alloying elements were computed with the aid of Energy Dispersive X-ray Spectroscopy (EDS). Vickers Hardness Tester was used to measure the hardness of the weld samples at ambient conditions.

In the present work, fabrication of similar Stainless Steel (SS) 304 joints by Nd-YAG Laser Welding Process (LWP) was done. A novel approach was attempted in this study. Welding was performed on dual sides of the plate (top and bottom) for a better mixture of micro powder particles in the weld pool region to achieve maximum depth of penetration, which is not easily possible in a single-sided LWP. High depth of penetration during fabrication of joints, significantly improved the mixture ratio of molten steel with reinforced micro powder particles. Al2O3 micro powder particles were reinforced in the weld pool region through the drilling process with varying depth ratios, and a moderate gap was maintained between each hole. The effects of Al2O3 on the microstructure and mechanical properties were studied and elaborated. Totally 12 samples were fabricated and joining was performed keeping the frequency as constant and varying laser power, travel speed for all the trials.

Castings by virtue of their manufacturing process route are prone to induce various defects which are likely to affect adversely during its service life, causing pre-mature failures. Multiple destructive and non-destructive evaluation techniques are practiced to analyze these defects and suitable measures are taken to ensure quality castings and avoid pre-mature failures. Accurate detection for each type of defect is key factor during analysis to adopt desired modifications in process and /or product stage.

In recent years, there has been a rapid growing demand for laser brazing in the transportation industry for automotive-Body in White (BIW), steel sheet assembly. Implementation of laser brazing is aimed primarily to improve productivity, quality of joints and cost. Laser brazing works by filling the opening amongst two substrates by melting the filler wire with the help of laser beam (used as a heat source), whereas in conventional resistance spot welding, contacting metal surface points are joined by the heat obtained from resistance to electric current. BIW is essentially a welded metal structure which is meant to provide durability and crashworthiness to the vehicle and is conventionally assembled using resistance spot welding process. The BIW structure comprises of various steel grades having varying thicknesses, compositions, microstructures and mechanical properties.

The Hybrid Cellular Automaton (HCA) algorithm is a generative design approach used to synthesize conceptual designs of crashworthy vehicle structures with a target mass. Given the target mass, the HCA algorithm generates a structure with a specific acceleration-displacement profile. The extended HCA (xHCA) algorithm is a generalization of the HCA algorithm that allows to tailor the crash response of the vehicle structure. Given a target mass, the xHCA algorithm has the ability to generate structures with different acceleration-displacement profiles and target a desired crash response. In order to accomplish this task, the xHCA algorithm includes two main components: a set of meta-parameters (in addition target mass) and surrogate model technique that finds the optimal meta-parameter values. This work demonstrates the capabilities of the xHCA algorithm tailoring acceleration and intrusion through the use of one meta-parameter (design time) and the use of Kriging-assisted optimization.

Simultaneous Localization and Mapping (SLAM) algorithms are extensively utilized within the field of autonomous navigation. In particular, numerous open-source Robot Operating System (ROS) based SLAM solutions, such as Gmapping, Hector, Cartographer etc., have simplified deployments in application. However, establishing the accuracy and precision of these ‘out-of-the-box’ SLAM algorithms is necessary for improving the accuracy and precision of further applications such as planning, navigation, controls. Existing benchmarking literature largely focused on validating SLAM algorithms based upon the quality of the generated maps. In this paper, however, we focus on examining the localization accuracy of existing 2-dimensional LiDAR based indoor SLAM algorithms. The fidelity of these implementations is compared against the OptiTrack motion capture system which is capable of tracking moving objects at sub-millimeter level precision.

The windshield is an integral part of almost every modern passenger car. Combined with current developments in the automotive industry such as electrification and the integration of lightweight material systems, the reduction of interior noise caused by stochastic and transient wind excitation is deemed to be an increasing challenge for future NVH measures. Active control systems have proven to be a viable alternative compared to traditional passive NVH measures in different areas. However, for windshield actuation there are neither comparative studies nor actually established actuation concepts available to the automotive industry. This paper illustrates a comparative conceptual study on windshield actuation for the active control of wind noise in a passenger car. Making use of an experimental modal analysis of the windshield installed in a medium-sized vehicle, a reduced order numerical simulation model is derived.

Aircraft components are commonly produced with 7000 series aluminum alloys (AA) due to its weight, strength, and fatigue properties. Auto Industry is also choosing more and more aluminum component for weight reduction. Current additive manufacturing (AM) methods fall short of successfully producing 7000 series AA due to the reflective nature of the material along with elements with low vaporization temperature. Moreover, lacking in ideal thermal control, print inherently defective products with such issues as poor surface finish alloying element loss and porosity. All these defects contribute to reduction of mechanical strength. By monitoring plasma with spectroscopic sensors, multiple information such as line intensity, standard deviation, plasma temperature or electron density, and by using different signal processing algorithm, AM defects have been detected and classified.

Residual stress prediction arising from manufacturing processes provides paramount information for the fatigue performance assessment of components subjected to cyclic loading. The determination of the material model to be applied in the numerical model should be taken carefully. This study focuses on the estimation of residual stresses generated after deep rolling of cast iron crankshafts. The researched literature on the field employs the available commercial material codes without closer consideration on their reverse loading capacities. To mitigate this gap, a single element model was used to compare potential material models with tensile-compression experiments. The best fit model was then applied to a previously developed crankshaft deep rolling numerical model. In order to confront the simulation outcomes, residual stresses were measured in two directions on real crankshaft specimens that passed through the same modeled deep rolling process.

In this research, ball-on-plate reciprocating sliding wear tests were utilized on austempered and quench-tempered gray cast iron samples with and without shot-peening treatment. The wear volume loss of the gray cast iron samples with different heat treatment designs was compared under equivalent hardness. The phase transformation in the matrix was studied using metallurgical evaluation and hardness measurement. It was found that thin needle-like ferrite became coarse gradually with increasing austempering temperature and was converted into feather-like shape when using the austempering temperatures of 399°C (750°F). The residual stress on the surface and sub-surface before and after shot-peening treatment was analyzed using x-ray diffraction. Compressive residual stress was produced after shot-peening treatment and showed an increasing trend with austempering temperature.

In order to explore approaches for improving object detection accuracy in intelligent vehicle system, we exploit super-resolution techniques. A novel method is proposed to confirm the conjecture whether some popular super-resolution networks used for environmental perception of intelligent vehicles and robots can indeed improve the detection accuracy. COCO dataset which contains images from complex ordinary environment is utilized for the verification experiment, due to it can adequately verify the generalization of each algorithm and the consistency of experimental results. Using two representative object detection networks to produce the detection results, namely Faster R-CNN and YOLOv3, we devise to reduce the impact of resizing operation. The two networks allow us to compare the performance of object detection between using original and super-resolved images. We quantify the effect of each super-resolution techniques as well.

As an alternative lightweight material, chopped carbon fiber reinforced Sheet Molding Compound (SMC) composites, formed by compression molding, provide a new material for automotive applications. In the present study, the monotonic and fatigue behavior of chopped carbon fiber reinforced SMC is investigated. Tensile tests were conducted on coupons with three different gauge length, and size effect was observed on the fracture strength. Since the fiber bundle is randomly distributed in the SMC plaques, a digital image correlation (DIC) system was used to obtain the local modulus distribution along the gauge section for each coupon. It was found that there is a relationship between the local modulus distribution and the final fracture location under tensile loading. The fatigue behavior under tension-tension (R=0.1) and tension-compression (R=-1) has also been evaluated.

Spot Welds are a category of welds used extensively in automotive structures, normally for metals. The fatigue analysis of such spot welds can be evaluated using (a) the Point 2 Point (P2P) method where a beam or bar is used to connect the 2 surfaces being joined, (b) a more modern approach where the 1D element is replaced with an “equivalent” brick element, or (c) a third approach that falls somewhere between where a “spider” and circular ring of elements, is used to represent the spot weld. In all 3 cases there is an assumption that the cross section is circular. For some specialist cases such as plastic connectors, the cross section is not circular so a new user defined weld is proposed. This paper will describe the approach that is based on the concept that a user generated tensor line can be used (equivalent to the theoretical Force/Moment to stress algorithms built into the P2P approach) along with special S-N curves create for different joint shapes.

Fused filament fabrication (FFF) 3D printing technology, one of the most accessible additive manufacturing technologies, can be used to create sensors based on different sensing principles, e.g.: capacitance, inductance, piezoelectricity, piezoresistivity. Piezoresistivity (strain-dependent electrical resistivity) has been predominantly used for the creation of static/quasistatic 3d printed sensors with relatively low sensitivity. This study researches the possibilities of a single-process 3d printing of a piezoresistive accelerometer. Initially, the methods for the axial and cross-axial identification of the piezoresistive properties are discussed. It is shown that the sensitivity is highly dependent on the printing parameters, especially the printing track orientation vs the mechanical load orientation. The research on the sensitivity of a 3D printed piezoresistive structure is extended with an inertial mass-based accelerometer design.