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Cast austenitic stainless steels, such as 1.4837Nb, are widely used for turbo housing and exhaust manifolds which are subjected to elevated temperatures. Due to assembly constraints, geometry limitation, and particularly high temperatures, thermomechanical fatigue (TMF) issue is commonly seen in the service of those components. Therefore, it is critical to understand the TMF behavior of the cast steels. In the present study, a series of fatigue tests including isothermal low cycle fatigue tests at elevated temperatures up to 1100°C, in-phase and out-of-phase TMF tests in the temperature ranges 100-800°C and 100-1000°C have been conducted. Both creep and oxidation are active in these conditions, and their contributions to the damage of the steel are discussed.

General Motors (GM) is working towards a future world of zero crashes, zero emissions and zero congestion. It’s “Ultium” platform has revolutionized electric vehicle drive units to provide versatile yet thrilling driving experience to the customers. Three variants of traction power inverter modules (TPIMs) including a dual channel inverter configuration are designed in collaboration with LG Magna e-Powertrain (LGM). These TPIMs are integrated with other power electronics components inside Integrated power electronics (IPE) to eliminate redundant high voltage connections and increase power density. The developed power module from LGM has used state-of-the art sintering technology and double-sided cooled structure to achieve industry leading performance and reliability. All the components are engineered with high level of integration skills to utilize across TPIM variants.

Zinc-based electrogalvanized (EG) and hot-dip galvanized (HDGI) coatings have been widely used in automotive body-in-white components for corrosion protection. The formability of zinc coated sheet steels depends on the properties of the sheet and the interactions at the interface between the sheet and the tooling. The frictional behavior of zinc coated sheet steels is influenced by the interfacial conditions present during the forming operation. Friction behavior has also been found to deviate from test method to test method. In this study, various lubrication conditions were applied to both bending under tension (BUT) test and a draw bead simulator (DBS) test for friction evaluations. Two different zinc coated steels; electrogalvanized (EG) and hot-dip galvanized (HDGI) were included in the study. In addition to the coated steels, a non-coated cold roll steel was also included for comparison purpose.

As passenger vehicle design evolves and accelerates, the use of multi-body dynamics solvers has proven to be invaluable in the engineering workflow. MBD solvers allow engineers to build virtual vehicle models that can accurately simulate vehicle responses and calculate internal forces, which previously could only be assessed using physical prototype builds with hundreds of measurement transducers. Evaluation and selection of solvers within an engineering environment is inherently a multi-dimensional activity that can include ease of use, retention of previously developed expertise, accuracy, speed, and integration with existing analysis processes. We discuss here some of the challenges present in developing capability and accumulating data to support each of these criteria. Developing a pilot model that is capable of being applied to a comprehensive set of use cases, and then verifying those use cases, required significant project management activity.

Cast aluminum cylinder blocks are frequently used in gasoline and diesel internal combustion engines because of their light-weight advantage. However, the disadvantage of aluminum alloys is their relatively low strength and fatigue resistance which make aluminum blocks prone to fatigue cracking. Engine blocks must withstand a combination of low-cycle fatigue (LCF) thermal loads and high-cycle fatigue (HCF) combustion and dynamic loads. Reliable computational methods are needed that allow for accurate fatigue assessment of cylinder blocks under this combined loading. In several publications, the mechanism-based thermomechanical fatigue (TMF) damage model DTMF describing the growth of short fatigue cracks has been extended to include the effect of both LCF thermal loads and superimposed HCF loadings. This approach is applied to the finite life fatigue assessment of an aluminum cylinder block. The required material properties related to LCF are determined from uniaxial LCF tests.

Semi-Permanent Mold (SPM) cast aluminum alloy cylinder heads are commonly used in gasoline and diesel internal combustion engines. The cast aluminum cylinder heads must withstand severe cyclic mechanical and thermal loads throughout their lifetime. The casting process is inherently prone to introducing casting defects and microstructural heterogeneity. Porosity, which is one of the most dominant volumetric defects in such castings, has a significant detrimental effect on the fatigue life of these components since it acts as a crack initiation site. A reliable analytical model for Thermo-Mechanical Fatigue (TMF) life prediction must take into account the presence of these defects. In previous publications, it has been shown that the mechanism-based TMF damage model (DTMF) is able to predict with good accuracy crack locations and the number of cycles to propagate an initial defect into a critical crack size in aluminum cylinder heads considering ageing effects.

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.

Remote Function Actuator (RFA) systems are widely used as the standard solution for conveniently accessing vehicles by remote control. To accelerate product development cycles and reduce engineering costs of physical test, a computer aided engineering (CAE) method has been developed to predict transmission range of the RFA system. Firstly, the detailed computational electromagnetic (CEM) models of the transmitting and receiving antennas were developed. Secondly, the articulated human model and the full vehicle meshed model were introduced to the CEM models to reflect the physical test environment. Lastly, the RFA system range model was built by including both the key fob held by an articulated human body and RFA module installed in the fully meshed vehicle. The transmission range could be extracted when the simulated received power reached the receiving sensitivity of the RFA module.

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.

Most of the applications of magnesium in lightweighting commercial cars and trucks are die castings rather than sheet metal, and automotive applications of magnesium sheet have typically been experimental or low-volume serial production. The overarching objective of this collaborative research project organized by the United States Automotive Materials Partnership (USAMP) was to develop new low-cost magnesium alloys, and demonstrate warm-stamping of magnesium sheet inner and outer door panels for a 2013 MY Ford Fusion at a fully accounted integrated component cost increase over conventional steel stamped components of no more than $2.50/lb. saved ($5.50/kg saved). The project demonstrated the computational design of new magnesium (Mg) alloys from atomistic levels, cast new experimental alloy ingots and explored thermomechanical rolling processes to produce thin Mg sheet of desired textures.

The automotive industry is continuously striving to reduce vehicle mass by reducing the mass of components including wheel bearings. A typical wheel bearing assembly is mostly steel, including both the wheel and knuckle mounting flanges. Mass optimization of the wheel hub has traditionally been accomplished by reducing the cross-sectional thickness of these components. Recently bearing suppliers have also investigated the use of alternative materials. While bearing component performance is verified through analysis and testing by the supplier, additional effects from system integration and performance over time also need to be comprehended. In a recent new vehicle architecture, the wheel bearing hub flange was reduced to optimize it for low mass. In addition, holes were added for further mass reduction. The design met all the supplier and OEM component level specifications.

Arc brazing welding (ABW) is widely used in automotive vehicle body and chassis structure along with Arc welding - MIG (Metal Inert Gas) or TIG (Tungsten Inert Gas) and spot welds. MIG welding or ABW (Arc Brazing welding) fracture in vehicle development process is one of the critical phenomena in quasi static structural simulation, like Roof Strength, Seat/Belt Anchorage and Child Restraint Anchorage (CRS). MIG/ABW Fracture has an impact on structural performance. Advantages of ABW over MIG weld is made at relatively lower temperatures. Significant advantage is welding thin sheet metal, no melting of parent metal and retains significant physical properties. This characteristic of ABW enables selection of ABW against MIG welded joint on automotive thin sheet metals. Good ABW joint can be as strong or stronger than MIG welded joint. Joint efficiency (JE) is defined as the ratio between the fracture strength of the joint and the fracture strength of parent metal.

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.

An experimental piston compounded engine was designed with guidance from thermodynamic modeling, then was built and tested to compare the model predictions to measured results. The piston-compounded concept has shown great potential for improvements in efficiency over current state-of-the-art light-duty engines through the use of an efficient second expansion process to more fully recover energy still present in the exhaust gasses, and was further developed into the Downsized Boosted Dilute Combustion, Exhaust Compounded (DBDC+EC) engine presented here. This paper documents some of the more unique design elements of this engine as well as a performance comparison between test data and modeling expectations. Ultimately, an experimental stoichiometric spark-ignited piston compounded engine was designed, five blocks were built, and collectively they were run for thousands of hours.

Automotive manufacturers relentlessly explore engine technology combinations to achieve reduced fuel consumption under continued regulatory, societal and economic pressures. For example, technologies enabling advanced combustion modes, increased expansion to effective compression ratio and reduced parasitics continue to be developed and integrated within conventional and hybrid propulsion strategies across the industry. A high-efficiency gasoline engine capable for use in conventional or hybrid electric vehicle platforms is highly desirable. This paper is the second of two papers describing the multi-cylinder integration of a technology package combining lean-stratified combustion with Miller cycle for downsized boosted applications. The first paper describes the design, analysis and single-cylinder testing conducted to down-select the combustion system deployed to the multi-cylinder engine.

Third generation advanced high strength steels (AHSS) that rely on the transformation of austenite to martensite have gained growing interest for implementation into vehicle architectures. Previous studies have identified a dependency of the rate of austenite decomposition on the amount of strain and the associated strain path imposed on the sheet. The rate and amount of austenite transformation can impact the work hardening behavior and tensile properties. However, a deeper understanding of the impact on toughness, and thus crash performance, is not fully developed. In this study, the strain path and strain amounts were systematically controlled to understand the associated correlation to impact toughness in the end application condition (strained and baked). Impact toughness was evaluated using an instrumented Charpy machine with a single sheet v-notch sample configuration.

Third generation advanced high strength steels (3GAHSS) are increasingly used in automotive for light weighting and safety body structure components. However, high material strength usually introduces higher springback that affects the dimensional accuracy. The ability to accurately predict springback in simulations is very important to reduce time and cost in stamping tool and process design. In this work, tension and compression tests were performed and the results were implemented to generate Isotropic/Kinematic hardening (I/KH) material models on a 3GAHSS steel with 980 MPa minimum tensile strength. Systematic material model parametric studies and evaluations have been conducted. Case studies from full-scale industrial parts are provided and the predicted springback results are compared to the measured springback data. Key variables affecting the springback prediction accuracy are identified.

This study evaluated the interactions between sheet steel, lubricant and measurement system under typical sheet forming conditions using a fixed draw bead simulator (DBS). Deep drawing quality mild steel substrates with bare (CR), electrogalvanized (EG) and hot dip galvanized (HDG) coatings were tested using a fixed DBS. Various lubricant conditions were targeted to evaluate the coefficient of friction (COF) of the substrate and lubricant combinations, with only rust preventative mill oil (dry-0 g/m2 and 1 g/m2), only forming pre-lube (dry-0 g/m2, 1 g/m2, and >6 g/m2), and a combination of two, where mixed lubrication cases, with incremental amounts of a pre-lube applied (0.5, 1.0, 1.5 and 2.0 g/m2) over an existing base of 1 g/m2 mill oil, were analyzed. The results showed some similarities as well as distinctive differences in the friction behavior between the bare material and the coatings.

In the recent decades, tremendous effort has been made in automotive industry to reduce vehicle mass and development costs for the purpose of improving fuel economy and building safer vehicles that previous generations of vehicles cannot match. An accurate modeling approach of sheet metal fracture behavior under plastic deformation is one of the key parameters affecting optimal vehicle development process. FLD (Forming Limit Diagram) approach, which plays an important role in judging forming severity, has been widely used in forming industry, and localized necking is the dominant mechanism leading to fracture in sheet metal forming and crash events. FLD is limited only to deal with the onset of localized necking and could not predict shear fracture. Therefore, it is essential to develop accurate fracture criteria beyond FLD for vehicle development.

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.