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Understanding customer expectations is critical to satisfying customers. Holding customer clinics is one approach to set winning targets for the engineering functional measures to drive customer satisfaction. In these clinics, customers are asked to operate and interact with vehicle systems or subsystems such as doors, lift gates, shifters, and seat adjusters, and then rate their experience. From this customer evaluation data, engineers can create customer loss or preference functions. These functions let engineers set appropriate targets by balancing risks and benefits. Statistical methods such as cumulative customer loss function are regularly applied for such analyses. In this paper, a new approach based on the Taguchi method is proposed and developed. It is referred to as Taguchi Customer Loss Function (TCLF).

Connected and automated vehicle (CAV) technologies promise a substantial decrease in traffic accidents and traffic jams, and bring new opportunities for improving vehicle’s fuel economy. However, testing autonomous vehicles in a real world traffic environment is costly, and covering all corner cases is nearly impossible. Furthermore, it is very challenging to create a controlled real traffic environment that vehicle tests can be conducted repeatedly and compared fairly. With the capability of allowing testing more scenarios than those that would be possible with real world testing, simulations are deemed safer, more efficient, and more cost-effective. In this work, a full-scale simulation platform was developed to simulate the infrastructure, traffic, vehicle, powertrain, and their interactions. It is used as an effective tool to facilitate control algorithm development for improving CAV’s fuel economy in real world driving scenarios.

Disc brakes operate with very close proximity of the brake pads and the brake rotor, with as little as a tenth of a millimeter of movement of the pads required to bring them into full contact with the rotor to generate braking torque. It is usual for a disc brake to operate with some amount of residual drag in the fully released state, signifying constant contact between the pads and the rotor. With this contact, every miniscule movement of the rotor pushes against the brake pads and changes the forces between them. Sustained loads on the brake corner, and maneuvers such as cornering, can both produce rotor movement relative to the caliper, which can push it steadily against one or both of the brake pads. This can greatly increase the residual force in the caliper, and increase drag. This dependence of drag behavior on the movement of the brake rotor creates some vehicle-dependent behavior.

Flexible molded polyurethane foams are widely used in automotive industry. As porous-elastic materials, they can be used as decoupler layers in conventional sound insulation constructions or as sound absorbers in vehicle trim parts. Flexible molded polyurethane foams are produced by reacting of liquid Isocyanate (Iso) with a liquid Polyol blend, catalysts, and other additives. Their acoustic performance can be changed by varying the mixing ratio, the weight proportion of two components: Iso and Polyol. Consequently, the sound insertion loss (IL) of barrier/foam constructions and acoustic absorption of a single foam layer will vary. In this paper, based on one industry standard flexible molded polyurethane foam process, the relationship between foam mixing ratio and foam acoustic performance is studied in terms of IL and sound absorption test results.

This paper summarizes the validation of prototype vehicle-to-infrastructure (V2I) safety applications based on Dedicated Short Range Communications (DSRC) in the United States under a cooperative agreement between the Crash Avoidance Metrics Partners LLC (CAMP) and the Federal Highway Administration (FHWA). After consideration of a number of V2I safety applications, Red Light Violation Warning (RLVW), Curve Speed Warning (CSW) and Reduced Speed Zone Warning with Lane Closure Warning (RSZW/LC) were developed, validated and demonstrated using seven different vehicles (six passenger vehicles and one Class 8 truck) leveraging DSRC-based messages from a Road Side Unit (RSU). The developed V2I safety applications were validated for more than 20 distinct scenarios and over 100 test runs using both light- and heavy-duty vehicles over a period of seven months. Subsequently, additional on-road testing of CSW on public roads and RSZW/LC in live work zones were conducted in Southeast Michigan.

The engine operating point (EOP), which is determined by the engine speed and torque, is an important part of a vehicle's powertrain performance and it impacts FC, available propulsion power, and emissions. Predicting instantaneous EOP in real-time subject to dynamic driver behaviour and environmental conditions is a challenging problem, and in existing literature, engine performance is predicted based on internal powertrain parameters. However, a driver cannot directly influence these internal parameters in real-time and can only accommodate changes in driving behaviour and cabin temperature. It would be beneficial to develop a direct relationship between the vehicle-level parameters that a driver could influence in real-time, and the instantaneous EOP. Such a relationship can be exploited to dynamically optimize engine performance.

Fabric materials have diverse applications in the automotive industry which include upholstery, carpeting, safety devices, and interior trim components. The textile industry has invested substantial effort toward development of standard testing techniques for characterizing mechanical properties of different fabric types (e.g. woven and knitted). However, there are presently no standards for determination of Young's modulus, Poisson's ratio and tensile stress-strain properties required for the detailed modeling of fabric materials in vehicle structural simulations. This paper presents results from uniaxial tensile tests of different automotive seat cover fabric materials. Digital image correlation, a full field optical method for measuring surface deformation, was used to determine tensile properties in both the warp/wale and the weft/course directions. The fabrics were tested with and without the foam backing.

Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

Commercially available Generation 3 (GEN3) advanced high strength steels (AHSS) have inherent capability of replacing press hardened steels (PHS) using cold stamping processes. 980 GEN3 AHSS is a cold stampable steel with 980 MPa minimum tensile strength that exhibits an excellent combination of formability and strength. Hot forming of PHS requires elevated temperatures (> 800°C) to enable complex deep sections. 980 GEN3 AHSS presents similar formability as 590 DP material, allowing engineers to design complex geometries similar to PHS material; however, its cold formability provides implied potential process cost savings in automotive applications. The increase in post-forming yield strength of GEN3 AHSS due to work and bake hardening contributes strongly toward crash performance in energy absorption and intrusion resistance.

Adiabatic heating during plastic straining can slow the diffusionless shear transformation of austenite to martensite in steels that exhibit transformation induced plasticity (TRIP). However, the extent to which the transformation is affected over a strain rate range of relevance to automotive stamping and vehicle impact events is unclear for most third-generation advanced high strength TRIP steels. In this study, an 1180MPa minimum tensile strength TRIP steel with carbide-free bainite is evaluated by measuring the variation of retained austenite volume fraction (RAVF) in fractured tensile specimens with position and strain. This requires a combination of servo-hydraulic load frame instrumented with high speed stereo digital image correlation for measurement of strains and ex-situ synchrotron x-ray diffraction for determination of RAVF in fractured tensile specimens.

More than four decades ago, the concept of zero defects was coined by Phillip Crosby. It was only a vision at the time, but the introduction of Artificial Intelligence (AI) in manufacturing has since enabled it to become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few Defects Per Million of Opportunities (DPMO). Detecting these rare quality events is one of the modern intellectual challenges posed to this industry. Process Monitoring for Quality (PMQ) is an AI and big data-driven quality philosophy aimed at defect detection and empirical knowledge discovery. Detection is formulated as a binary classification problem, where the right Machine Learning (ML), optimization, and statistics techniques are applied to develop an effective predictive system.

Stamped components play an important role in supporting various sub-systems within a typical engine and transmission assembly. In some cases, the stamped components will not initially meet the design criteria, and material may need to be added to strengthen it. However, in other cases the component may be overdesigned, and there will be opportunities to reduce mass while still meeting all design criteria. In this latter case, multiple CAE simulations are often performed to enhance the component design by varying design parameters such as thickness, bend radius, material, etc., The conventional process will assess changes in one parameter at a time, while holding other parameters constant. Though this helps in meeting the design criteria, it is often very difficult to produce the best optimized design within the limited time span with this approach. With the aid of Altair-HyperMorph techniques, multiple design parameters can be varied simultaneously.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

Based on findings from micromechanical studies, a Ductile Failure Criterion (DFC) was proposed. The proposed DFC treats localized necking as failure and critical damage as a function of strain path and initial sheet thickness. Under linear strain path assumption, a method to predict Forming Limit Curve (FLC) is derived from this DFC. With the help of predetermined effect functions, the method only needs a calibration at uniaxial tension. The approach was validated by predicting FLCs for sixteen different aluminum and steel sheet metal materials. Comparison shows that the prediction matches quite well with experimental observations in most cases.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.

Twist spring-back would interfere with stamping or assembling procedures for advanced high strength steel. A “homeopathic” resolution for controlling the twist spring-back is proposed using unbalanced post-stretching configuration. Finite element forming simulation is applied to evaluate and compare the performance for each set of unbalanced post-stretching setup. The post-stretching is effectuated by stake bead application. The beads are separated into multiple independent segments, the height and radii of which can be adjusted individually and asymmetrically. Simulation results indicate that the twist spring-back can be effectively controlled by reducing the post-stretching proximate to the asymmetric part area. Its mechanism is qualitatively revealed by stress analyses, that an additional but acceptable cross-sectional spring-back re-balances the sprung asymmetrical geometry to counter the twist effect.

With the emphasis of electrification in automotive industry, tremendous efforts are made to develop electric motors with high efficiency and power density, and reduce noise, vibration and harshness (NVH). A multiphysics simulation workflow is used to predict the eccentricity-induced noise for GM’s Bolt EV motor. Both static and dynamic eccentricities are investigated along with axial tilt. Analysis results show that these eccentricities play a critical role in the NVH behavior of the motor assembly. Transient electromagnetic (EM) analysis is performed first by extruding 2D stator and rotor sections to form 3D EM models. Sector model is duplicated to form full 360-degree model. Stator is split into three rotated sections to characterize stator skew, and the skew between two sections of rotor and magnets are also modelled. Sinusoidal current is applied and lumped-sum forces on each stator tooth are computed.

Styrene-Butadiene Rubber (SBR), a copolymer of butadiene and styrene, is widely used in the automotive industry due to its high durability and resistance to abrasion, oils and oxidation. Some of the common applications include tires, vibration isolators, and gaskets, among others. This paper characterizes the dynamic behavior of SBR and discusses the suitability of a visco-elastic model of elastomers, known as the Kelvin model, from a mathematical and physical point of view. An optimization algorithm is used to estimate the parameters of the Kelvin model. The resulting model was shown to produce reasonable approximations of measured dynamic stiffness. The model was also used to calculate the self heating of the elastomer due to energy dissipation by the viscous damping components in the model. Developing such a predictive capability is essential in understanding the dynamic behavior of elastomers considering that their dynamic stiffness can in general depend on temperature.

A ductile failure criterion (DFC), which defines the stretching failure at localized necking (LN) and treats the critical damage as a function of strain path and initial sheet thickness, was proposed in a previous study. In this study, the DFC is revisited to extend the model to the low stress triaxiality domain and demonstrates on modeling forming limit curve (FLC) of TRIP 690. Then, the model is used to predict stretching failure in a finite element method (FEM) simulation on a TRIP 690 steel rectangular cup draw process at room temperature. Comparison shows that the results from this criterion match quite well with experimental observations.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.