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Abstract A number of studies have shown that driving an unfamiliar vehicle has the potential to introduce additional risk, especially for novice drivers. However, such studies have generally used statistical methods based on analyzing crash and near-crash data from a range of driver groups, and therefore the evaluation has the potential to be subjective and limited. For a more objective perspective, this study suggests that it would be worthwhile to consider vehicle dynamic signals obtained from the Controller Area Network (CAN-Bus) and smartphones. This study, therefore, is focused on the effect of driver experience and vehicle familiarity for issues in driver modeling and distraction. Here, a group of 20 drivers participated in our experiment, with 13 of them having participated again after a one-year time lapse in order for analysis of their change in driving performance.

Abstract Introduction: This study evaluates driving ability in those with Charcot Marie Tooth Disease Type 1A, a hereditary peripheral neuropathy. Methods: Individuals with Charcot Marie Tooth Disease Type 1A (n = 18, age = 42 ± 7) and controls (n = 19; age = 35 ± 10) were evaluated in a driving simulator. The Charcot Marie Tooth Neuropathy Score version 2 was obtained for individuals. Rank Sum test and Spearman rank correlations were used for statistical analysis. Results: A 74% higher rate of lane departures and an 89% higher rate of lane deviations were seen in those with Charcot Marie Tooth Disease Type 1A than for controls (p = 0.005 and p < 0.001, respectively). Lane control variability was 10% higher for the individual group and correlated with the neuropathy score (rS = 0.518, p = 0.040), specifically sensory loss (rS = 0.710, p = 0.002) and pinprick sensation loss in the leg (rS = 0.490, p = 0.054).

Abstract Real-time reconstruction of 3D environment attributed with semantic information is significant for a variety of applications, such as obstacle detection, traffic scene comprehension and autonomous navigation. The current approaches to achieve it are mainly using stereo vision, Structure from Motion (SfM) or mobile LiDAR sensors. Each of these approaches has its own limitation, stereo vision has high computational cost, SfM needs accurate calibration between a sequences of images, and the onboard LiDAR sensor can only provide sparse points without color information. This paper describes a novel method for traffic scene semantic segmentation by combining sparse LiDAR point cloud (e.g. from Velodyne scans), with monocular color image. The key novelty of the method is the semantic coupling of stereoscopic point cloud with color lattice from camera image labelled through a Convolutional Neural Network (CNN).

Abstract In this paper, an efficient lane detection using deep feature extraction method is proposed to achieve real-time lane detection in diverse road environment. The method contains three main stages: 1) pre-processing, 2) deep lane feature extraction and 3) lane fitting. In pre-processing stage, the inverse perspective mapping (IPM) is used to obtain a bird's eye view of the road image, and then an edge image is generated using the canny operator. In deep lane feature extraction stage, an advanced lane extraction method is proposed. Firstly, line segment detector (LSD) is applied to achieve the fast line segment detection in the IPM image. After that, a proposed adaptive lane clustering algorithm is employed to gather the adjacent line segments generated by the LSD method. Finally, a proposed local gray value maximum cascaded spatial correlation filter (GMSF) algorithm is used to extract the target lane lines among the multiple lines.

Abstract The dynamic characteristics of tires are critical in the overall vibrations of vehicles because the tire-road interface is the only medium of energy transfer between the vehicle and the road surface. Obtaining the natural frequencies and mode shapes of the tire helps in improving the comfort of the passengers. The vibrational characteristics of structures are usually obtained by performing conventional impact hammer modal testing, in which the structure is excited with an impact hammer and the response of the structure under excitation is captured using accelerometers. However, this approach only provides the response of the structure at a few discrete locations, and it is challenging to use this procedure for rotating structures. Digital Image Correlation (DIC) helps in overcoming these challenges by providing the full-field response of the structure.

Abstract The use of nanomaterials and nanostructures have been revolutionizing the advancements of science and technology in various engineering and medical fields. As an example, Carbon Nanotubes (CNTs) have been extensively used for the improvement of mechanical, thermal, electrical, magnetic, and deteriorative properties of traditional composite materials for applications in high-performance structures. The exceptional materials properties of CNTs (i.e., mechanical, magnetic, thermal, and electrical) have introduced them as promising candidates for reinforcement of traditional composites. Most structural configurations of CNTs provide superior material properties; however, their geometrical shapes can deliver different features and characteristics. As one of the unique geometrical configurations, helical CNTs have a great potential for improvement of mechanical, thermal, and electrical properties of polymeric resin composites.

Abstract The use of simulation is a long-standing industry standard at every level of flight training. Historically, given the acquisition and maintenance costs associated with such equipment, full-motion devices have been reserved for advanced corporate and airline training programs. The Motion Cueing Seat (MCS) is a relatively inexpensive alternative to full-motion flight simulators and has the potential to enhance the fixed-base flight simulation in primary flight training. In this article, we discuss the results of an evaluation of the effect of motion cueing on pilot workload and performance during primary instrument training. Twenty flight students and instructors from a collegiate flight training program participated in the study. Each participant performed three runs of a basic circuit using a fixed-base Advanced Aviation Training Device (AATD) and an MCS.

Abstract A key problem of the construction of fully electric aircraft is the limited energy density of battery packs. It is generally accepted that this can only be overcome via new, denser battery chemistry together with a further increase in the efficiency of power utilization. One appealing approach for achieving the latter is using laser-assisted filler-based joining technologies, which offers unprecedented flexibility for achieving battery cell connections with the least possible electrical loss. This contribution presents our results on the effect of various experimental and process parameters on the electrical and mechanical properties of the laser-formed bond.

Abstract The present study aims to join the dissimilar materials such as Dual-Phase (DP) 600 Steel and AA6082-T6 Aluminum (Al) alloy via the friction stir welding (FSW) process with a reduced intermetallic compound (IMC) layer. The five different tool tilt angles of 0°, 0.5°, 1°, 1.5°, and 2° were selected to fabricate the joints. The weld characteristics such as tensile strength, hardness, macrostructure, and microstructure were analyzed. The weld interface was studied by employing an optical microscope and scanning electron microscope (SEM) equipped with energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) techniques. The joint produced with a 0.5° tilt angle has achieved the highest ultimate tensile strength (UTS) of 240 MPa. The IMCs were identified as Fe2Al8 and FeAl2 from the joint interface studies.

Abstract Vaporizing foil actuator welding (VFAW) created nominally solid-state spot welds between high-strength DP980 steel and 6022 T4 aluminum. The effects of varying the impact velocity and angle between the Al flyer and target steel sheets on the structure and properties of the joints were evaluated using photonic Doppler velocimetry (PDV), scanning electron microscopy (SEM), fractography, and energy-dispersive spectroscopy (EDS) analysis. The incident angle and velocity of the flyer plate were quantified using PDV, and their relations to the structure and properties of the joint were assessed with microscopy and strength testing. Impact velocity and average impact angle increase with the increasing standoff. Lower impact angles and higher impact velocities promoted interfacial failure due to increased melting, higher intermetallic thickness, and lower wave amplitude and wavelength.

Abstract The causes of failure due to cracking in the resistance spot welding of the advanced high-strength steels dual-phase 590 (DP590) were investigated using scanning electron microscopy (SEM), optical microscopy, and the tensile-shear test. The results showed that by increasing the current amount, the formation of the melting zone occurred in the heat-affected zone, leading to the cracking in this area, reducing the tensile strength and decreasing the mechanical properties; the initiation and growth of cracking and failure in this region also happened. In the heat-affected zone, by increasing the current amount with the softening phenomenon, the recrystallized coarse grains also occurred, eventually resulting in the loss of mechanical properties. The results of the tensile-shear test also indicated that by increasing the current up to 12 kA, the strength was raised, but the ductility was reduced.

Abstract Raceway Brinell damage is one major cause of wheel bearing (hub unit) noise during driving. Original Equipment Manufacturer (OEM) customers have asked continuously for its improvement to the wheel bearing supply base. Generally, raceway Brinelling in a wheel hub unit is a consequence of metallic yielding from high external loading in a severe environment usually involving a side impact to the wheel and tire. Thus, increasing the yielding strength of steel can lead to higher resistance to Brinell damage. Both the outer ring and hub based on Generation 3 (Gen. 3) wheel unit are typically manufactured using by AISI 1055 bearing quality steel (BQS); these components undergo controlled cooling to establish the core properties then case hardening via induction hardening (IH). This paper presents a modified grade of steel and its IH design that targets longer life and improves Brinell resistance developed by ILJIN AMRC (Advanced Materials Research Center).

Abstract Surface roughness is one of the important aspects in producing quality die. Wire Electrical Discharge Machine (WEDM) is commonly used in tool and die fabrication, since the die material is usually difficult to cut using traditional metal removal processes. Selection of optimal WEDM cutting parameters is crucial to obtain quality die finish. In this study, 2379 steel which equivalent to SKD 11 is selected as the die material. Four main WEDM cutting parameters, namely, pulse duration (A), pulse interval (B), servo voltage (C), ignition pulse current (D), were experimentally evaluated for both main cut and multiple trim cuts using Taguchi Method. Taguchi’s L9 orthogonal array is employed for experimental design and analysis of variance (ANOVA) was used in recognizing levels of significance of WEDM cutting parameters.

Abstract Sound ductile iron (DI) welded joints were performed using developed coated electrode and optimized welding parameters including post weld heat treatment (PWHT).Weldments consisting of weld metal, partially melted zone (PMZ), heat affected zone (HAZ) and base metal were austenitized at 900 °C for 2 hour and austempered at 300 °C and 350 °C for three different holding time (1.5 hour, 2 hour and 2.5 hour). In as-weld condition, microstructures of weld metal and PMZ show ledeburitic carbide and alloyed pearlite, but differ with their amount. Whereas microstructure of HAZ shows pearlite with some ledeburitic carbide and base metal shows only ferrite.

Abstract In this work, optimal modeling parameters for self-pierce riveting (SPR) were determined using a factorial design of experiments (DOE). In particular, we show statistically how each of the calibrating parameters used in modeling the SPR process through nonlinear finite element modeling can drastically change the geometry of the joint. The results of this study indicate that the degree of interlock, which is a key feature of a sound joint, is largely influenced by the friction between the die and bottom sheet as well as the friction between the rivet and top sheet. Furthermore, this numerical study also helped elucidate the role of friction in SPR and sheds light on how coatings with diverse friction coefficients can affect material deformation and ultimately structural integrity of the joint.

Abstract The use of Abrasive Water Jet (AWJ) cutting technology can improve the edge stretchability in sheet metal forming. The advances in technology have allowed significant increases in working speeds and pressures, reducing the AWJ operation cost. The main objective of this work was to determine the effect of selected AWJ cutting parameters on the Hole Expansion Ratio (HER) for a DP800 (Dual-Phase) Advanced High-Strength Steel (AHSS) with s0 = 1.2 mm by using a fractional factorial design of experiments for the Hole Expansion Tests (HET). Additionally, the surface roughness and residual stresses were measured on the holes looking for a possible relation between them and the measured HER. A deep drawing quality steel DC06 with s0 = 1.0 mm was used for reference. The fracture occurrence was captured by high-speed cameras and by Acoustic Emissions (AE) in order to compare both methods.

Abstract Management of expressway networks has been mainly focused on defect management without looking at the correlations with accidental risks. This causes unsustainability in expressway infrastructure maintenance since such defects may not be a contributing factor toward public safety. Thus it is necessary to incorporate accidental events for decision-making in infrastructure management. This study has developed a novel approach to machine learning (ML) that incorporates actual primary data from the last 10 years of single-vehicle accidents (SVA) by collisions with motorway facilities, or so-called single-vehicle collisions with fixed objects. The ML is firstly aimed at identifying the influential factors of SVA in relation to finding effective countermeasures for accidents by integrating the correlation analysis, multiple regression analysis, and ML techniques. The study reveals that wet pavement conditions have a significant effect on SVA.

Abstract Performing an uncertainty analysis for complex measurement tasks, such as those found in engine research, presents unique challenges. Also, because of the excessive computational costs, modeling-based approaches, such as a Monte Carlo approach, may not be practical. This work provides a traditional statistical approach to uncertainty analysis that incorporates the uncertainty tree, which is a graphical tool for complex uncertainty analysis. Approaches to calculate the required sensitivities are discussed, including issues associated with numerical differentiation, numerical integration, and post-processing. Trimming of the uncertainty tree to remove insignificant contributions is discussed. The article concludes with a best practices guide in the Appendix to uncertainty propagation in experimental engine combustion post-processing, which includes suggested post-processing techniques and down-selected functional relationships for uncertainty propagation.

Abstract This study shows the development of low-temperature and high-temperature reactions in a gasoline-fuelled compression ignition (GCI) engine realizing partially premixed combustion for high efficiency and low emissions. The focus is how the ignition occurs during the low- to high-temperature reaction transition and how it varies due to single- and double-injection strategies. In an optically accessible, single-cylinder small-bore diesel engine equipped with a common-rail fuel injection system, planar laser-induced fluorescence (PLIF) imaging of formaldehyde (HCHO-PLIF), hydroxyl (OH-PLIF), and fuel (fuel-PLIF) has been performed. This was complemented with high-speed imaging of combustion luminosity and chemiluminescence imaging of cool flame and OH*.

Abstract Mechanical friction simulations offer a valuable tool in the development of internal combustion engines for the evaluation of optimization studies in terms of time efficiency. However, system modeling and evaluation of model performance may be highly complex. A high number of interacting submodels and parameters as well as a limited model transparency contribute to uncertainties in the modeling process. In particular, model calibration and validation are complicated by the unknown effect of parameters on the model output. This article presents an advanced and model-independent methodology for identifying sensitive parameters of engine friction. This allows the user to investigate an unlimited number of parameters of a model whose structure and properties are prior unknown.