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A state of the art proprietary method for aluminum-to-aluminum joining in the automotive industry is Resistance Spot Welding. However, with spot welding (1) structural performance of the joint may be degraded through heat-affected zones created by the high temperature thermal joining process, (2) achieving the double-sided access necessary for the spot welding electrodes may limit design flexibility, and (3) variability with welds leads to production inconsistencies. Self-piercing rivets have been used before; however they require different rivet/die combinations depending on the material being joined, which adds to process complexity. In recent years the introductions of screw products that combine the technologies of friction drilling and thread forming have entered the market. These types of screw products do not have these access limitations as through-part connections are formed by one-sided access using a thermo-mechanical flow screwdriving process with minimal heat.

The ever-growing concern to reduce the impact of transportation systems on environment has pushed automotive industry towards fuel-efficient and sustainable solutions. While several approaches have been used to improve fuel efficiency, the light-weighting of automobile components has proven broadly effective. A substantial effort is devoted to lightweighting body-in-white which contributes ~35% of total weight of vehicle. Closure systems, however, have been often overlooked. Closure systems are extremely important as they account for ~ 50% of structural mass and have a very diverse range of requirements, including crash safety, durability, strength, fit, finish, NVH, and weather sealing. To this end, a carbon fiber-reinforced thermoplastic composite door is being designed for an OEM’s mid-size SUV, that enables 42.5% weight reduction. In this work, several novel composite door assembly designs were developed by using an integrated design, analysis and optimization approach.

Fracture strain data provide essential information for material selection and serve as an important failure criterion in computer simulations of crash events. Traditionally, the fracture strain was measured by evaluating the thinning at fracture using tools such as a microscope or a point micrometer. In the recent decades, digital image correlation (DIC) has evolved as an advanced optical technique to record full-field strain history of materials during deformation. Using this technique, a complete set of the fracture strains (including major, minor, and thickness strains) can be approximated for the material. However, results directly obtained from the DIC can be dependent on the experiment setup and evaluation parameters, which potentially introduce errors to the reported values.

This paper presents development of a multi-scale material model for a 980 MPa grade transformation induced plasticity (TRIP) steel, subject to a two-step quenching and partitioning heat treatment (QP980), based on integrated computational materials engineering principles (ICME Model). The model combines micro-scale material properties defined by the crystal plasticity theory with the macro-scale mechanical properties, such as flow curves under different loading paths. For an initial microstructure the flow curves of each of the constituent phases (ferrite, austenite, martensite) are computed based on the crystal plasticity theory and the crystal orientation distribution function. Phase properties are then used as an input to a state variable model that computes macro-scale flow curves while accounting for hardening caused by austenite transformation into martensite under different straining paths.

The strain-induced diffusionless shear transformation of retained austenite to martensite during straining of transformation induced plasticity (TRIP) assisted steels increases strain hardening and delays necking and fracture leading to exceptional ductility and strength, which are attractive for automotive applications. A novel technique that provides the retained austenite volume fraction variation with strain with improved precision is presented. Digital images of the gauge section of tensile specimens were first recorded up to selected plastic strains with a stereo digital image correlation (DIC) system. The austenite volume fraction was measured by synchrotron X-ray diffraction from small squares cut from the gage section. Strain fields in the squares were then computed by localizing the strain measurement to the corresponding region of a given square during DIC post-processing of the images recorded during tensile testing.

Compared to traditional materials, carbon fiber reinforced polymers (CRFPs) have allowed designers to design stiff, light-weight structures, but at the cost of increased complexity in the design process. In this paper, the automation and optimization of the composite design process and how it affects design space exploration are evaluated. Specifically investigated is the design process for CFRP sandwich structures using the third-party optimization software modeFRONTIER. For given surface geometry and load cases, the approach aims to explore the Pareto frontier for the minimization of mass while constraining stiffness parameters. In this approach, the problem is framed as a single integrated optimization problem. In each optimization iteration, this method updates the CAD geometry and discretization of plies across the structure before exporting the model for Finite Element Analysis (FEA).

Tensile behavior of advanced high strength steel (AHSS) grades with strengths up to 980 MPa has been extensively studied. However, limited data is found in literature on the tensile behavior of steels with tensile strengths of the order of 1180 MPa, especially at nominal strain rates up to 500/s. This paper examines tensile flow behavior to fracture of four different 1180 MPa grade steels at strain rates of 0.005/s, 0.5/s, 5/s, 50/s and 500/s using an experimental methodology that combines a servo-hydraulic tester and high speed digital image correlation. Even though the strength increase with the strain rate is consistent between the four different materials, the total elongation increase with the strain rate varies widely. Some insights as to why this occurs from examination of the steel microstructure and variation of retained austenite with strain are offered.

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.

Fatigue analysis of pistons is reliant on an accurate representation of the high temperatures to which they are exposed. It can be difficult to represent this accurately, because instrumented tests to validate piston thermal models typically include only measurements near the piston crown and there are many unknown backside heat transfer coefficients (HTCs). Previously, a methodology was proposed to aid in the estimation of HTCs for backside convection boundary conditions of a stratified charge compression ignition (SCCI) piston. This methodology relies on Bayesian inference of backside HTC using a co-simulation between computational fluid dynamics (CFD) and finite element analysis (FEA) solvers. Although this methodology primarily utilizes the more computationally efficient FEA model for the iterations in the calibration, this can still be a computationally expensive process.

A major concern for a high-power density, heavy-duty engine is the durability of its components, which are subjected to high thermal loads from combustion. The thermal loads from combustion are unsteady and exhibit strong spatial gradients. Experimental techniques to characterize these thermal loads at high load conditions on a moving component such as the piston are challenging and expensive due to mechanical limitations. High performance computing has improved the capability of numerical techniques to predict these thermal loads with considerable accuracy. High-fidelity simulation techniques such as three-dimensional computational fluid dynamics and finite element thermal analysis were coupled offline and iterated by exchanging boundary conditions to predict the crank angle-resolved convective heat flux and surface temperature distribution on the piston of a heavy-duty diesel engine.

In military applications, diesel engines are required to achieve high power outputs and therefore must operate at high loads. This high load operation leads to high piston component temperatures and heat rejection rates limiting the packaged power density of the powertrain. To help predict and understand these constraints, as well as their effects on performance, a thermodynamic engine model coupled to a finite element heat conduction solver is proposed and validated in this work. The finite element solver is used to calculate crank angle resolved, spatially averaged piston temperatures from in-cylinder heat transfer calculations. The calculated piston temperatures refine the heat transfer predictions as well requiring iteration between the thermodynamic model and finite element solver.