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Predicting the optimum performance parameters of an automotive cyclone particle separator (separation efficiency and pressure drop) using computational fluid dynamics by varying its geometrical parameters is challenging and a time consuming process due to the highly swirling nature of the flow. This study presents results of three investigations of the performance and design of a cyclone separator: a sensitivity analysis, deterministic optimization and a reliability based design optimization. All three cases involved variation of four geometric parameters that characterize the design of the cyclone.

In reliability design, often, there is scarce data for constructing probabilistic models. It is particularly challenging to model uncertainty in variables when the type of their probability distribution is unknown. Moreover, it is expensive to estimate the upper and lower bounds of the reliability of a system involving such variables. A method for modeling uncertainty by using Polynomial Chaos Expansion is presented. The method requires specifying bounds for statistical summaries such as the first four moments and credible intervals. A constrained optimization problem, in which decision variables are the coefficients of the Polynomial Chaos Expansion approximation, is formulated and solved in order to estimate the minimum and maximum values of a system’s reliability. This problem is solved efficiently by employing a probabilistic re-analysis approach to approximate the system reliability as a function of the moments of the random variables.

The paper studies the distributions of residual stresses in auto components after induction hardening. Three prototype parts are analyzed in this paper. Firstly, the temperature fields of the analyzed parts are quantitatively simulated during quenching by simulating surface heating to the austenitization temperature of the material. Secondly, the formation and states of the residual stresses are predicted. Therefore the distribution of residual stress is simulated and shows compressive stresses on the surface of components so that the strength can be improved. The simulated results by computer are compared with experimental results. The good comparison indicates that the results obtained by the FEA analysis are reliable. Thus, it can be concluded that the FEA (Finite element analysis) program is effectively developed to simulate heating and quenching processes and residual stresses distribution.

With increased demand for improvements in the efficiency and operation of all automotive engine components, including those in the engine cooling system, there is a need to develop a set of virtual tools that can aid in both the evaluation and design of automotive components. In the case of automotive radiators, improvements are needed in the overall pressure drop as well as the coolant flow homogeneity across all radiator tubes. The latter criterion is particularly important in the reduction of premature fouling and failure of heat exchangers. Rather than relying on ad hoc geometry changes with the goal of improving the performance of radiators, the coupling of CFD flow simulations with numerical shape optimization methods could assist in the design and testing of automotive heating and cooling components.

The hole stretchability of two Aluminum Alloys (AA6111 and AA6022) are studied by using a two stages integrated finite element framework where the edge geometry and edge damages from the hole piercing processes were considered in the subsequent hole expansion processes. Experimentally it has been found that AA6022 has higher hole expansion ratios than those of AA6111. This observation has been nicely captured by finite element simulations. The main cause of differences have been identified to the volume fractions of the random distributed second phase hard particles which play a critical role in determining the fracture strains of the materials.

Advanced high-strength steel (AHSS) is gaining popularity in the automotive industry due to its higher final part strength with the better formability compares to the conventional steel. However, the edge fracture occurs during the forming procedure for the pre-strained part. To avoid the edge fracture that happens during the manufacturing, the effect of pre-strain on edge cracking limit needs to be studied. In this paper, digital image correlation (DIC), as an accurate optical method, is adopted for the strain measurement to determining the edge cracking limit. Sets of the wide coupons are pre-strained to obtain the samples at different pre-strain level. The pre-strain of each sample is precisely measured during this procedure using DIC. After pre-straining, the half dog bone samples are cut from these wide coupons. The edge of the notch in the half dog bone samples is created by the punch with 10% clearance for the distinct edge condition.

Vehicle weight reduction is a significant challenge for the modern automotive industry. In recent years, the amount of vehicular components constructed from aluminum alloy has increased due to its light weighting capabilities. Automotive manufacturing processes, predominantly those utilizing various stamping applications, require a thorough understanding of aluminum fracture predictions methods, in order to accurately simulate the process using Finite Element Method (FEM) software or use it in automotive engineering manufacture. This paper presents the strain distribution of A5182 aluminum samples after punch impact under various conditions by Digital Image Correlation (DIC) system, its software also measured the complete strain history, in addition to sample curvature after it was impacted; therefore obtaining the data required to determine the amount of side-wall-curl (Aluminum sheet springback) present after formation.

In recent years, dual phase (DP) Advanced High Strength Steels (AHSS) and Ultra High Strength Steels (UHSS) are considered as prominent materials in the automotive industry due to superior structural performance and vehicle weight reduction capabilities. However, these materials are often sensitive to trimmed edge cracking if stretching along sheared edge occurs in such processes as stretch flanging. Another major issue in the trimming of UHSS is tool wear because of higher contact pressures at the interface between cutting tools and sheet metal blank caused by UHSS’s higher flow stresses and the presence of a hard martensitic phase in the microstructure. The objective of the current paper is to study the influence of trimming conditions and tool wear on quality and stretchability of trimmed edge of DP980 steel sheet. For this purpose, mechanically trimmed edges were characterized for DP980 steel and compared with other steels such as HSLA 350 and BH210.

Advanced high strength steels (AHSS) are increasingly used in automotive industry. A major issue for AHSS stamping is edge cracking. This failure mode is difficult to predict by conventional forming limit curve (FLC). The material edge stretchability is mainly evaluated using the hole expansion test. In this study, digital Image Correlation (DIC) is applied for strain measurement. DIC is a non-contact, full field, high accuracy and direct measurement technique that provides more detailed information for the evolution of strains on the sheet surface. Tests were conducted for five AHSS and nine cases. This paper will explain in detail the DIC technique and its results.

Scuffing is one of the major problems that influence the life cycle and reliability of several auto components, including engine cylinder kits, flywheels, camshafts, crankshafts, and gears. Ferrous casting materials, such as gray cast iron, ductile cast iron and austempered ductile cast iron (ADI) are widely applied in these components due to their self-lubricating characteristics. The purpose of this research is to determine the scuffing behavior of these three types of cast iron materials and compare them with 1050 steel. Rotational ball-on-disc tests were conducted with white mineral oil as the lubricant under variable sliding speeds and loads. The results indicate that the scuffing initiation is due to either crack propagation or plastic deformation. It is found that ADI exhibits the highest scuffing resistance among these materials.

The availability of computational resources has enabled an increased utilization of Design of Experiments (DoE) and metamodeling (response surface generation) for large-scale optimization problems. Despite algorithmic advances however, the analysis of systems such as water jackets of an automotive engine, can be computationally demanding in part due to the required accuracy of metamodels. Because the metamodels may have many inputs, their accuracy depends on the number of training points and how well they cover the entire design (input) space. For this reason, the space-filling properties of the DoE are very important. This paper utilizes a new group-based DoE algorithm with space-filling groups of points to construct a metamodel. Points are added sequentially so that the space-filling properties of the entire group of points is preserved. The addition of points is continuous until a specified metamodel accuracy is met.

A multi-sensor Digital Image Correlation (DIC) system is employed to measure the deformation of metal specimens during tensile tests. The multi-sensor DIC system is capable of providing high quality contour and deformation data of a 3D object. Methodology and advantages of the multi-sensor DIC system is introduced. Tests have been done on steel and aluminum specimens to prove the performance of the system. With the help of the multi-sensor DIC system, we proposed our approaches to determine the forming limit based on shape change around the necking area instead of calculate the FLD based on the in-plane strains. With the employed system, all measurements are done post-deformation, no testing controlling mechanism, such as load force control or touching control, is required. The extracted data is analyzed and the result shows a possibility that we may be able to improve current technique for Forming Limit Diagram (FLD) measurement.

In this work, a multi-camera Digital Image Correlation (DIC) system is applied to measure the material properties of aluminum (5754) specimens. Such tests are usually done using 2D (one-camera) or 3D (two-camera) DIC systems. A multi-camera DIC system includes three or more cameras and inherits all the advantages of a conventional 3D DIC system (with two cameras) such as, full-field measurement, high accuracy and high speed. In addition, this system further improves the measured results by including redundant data. In this work, we will show the potential of this system to measure a variety of material properties at one time.

There is a continual need to apply heat treatment processes in innovative ways to optimize material performance. One such application studied in this research is carburizing followed by austempering of low carbon alloy steels, AISI 8620, AISI 8822 and AISI 4320, to produce components with high strength and toughness. This heat treatment process was applied in two steps; first, carburization of the surface of the parts, second, the samples were quenched from austenitic temperature at a rate fast enough to avoid the formation of ferrite or pearlite and then held at a temperature just above the martensite starting temperature to partially or fully form bainite. Any austenite which was not transformed during austempering, upon further cooling formed martensite or was present as retained austenite.

Design optimization often relies on computational models, which are subjected to a validation process to ensure their accuracy. Because validation of computer models in the entire design space can be costly, we have previously proposed an approach where design optimization and model validation, are concurrently performed using a sequential approach with variable-size local domains. We used test data and statistical bootstrap methods to size each local domain where the prediction model is considered validated and where design optimization is performed. The method proceeds iteratively until the optimum design is obtained. This method however, requires test data to be available in each local domain along the optimization path. In this paper, we refine our methodology by using polynomial regression to predict the size and shape of a local domain at some steps along the optimization process without using test data.

The objective of this paper is to assess the effect of snow density on tire-snow interaction in the presence of uncertainty. The snow-depth dependent finite element analysis (FEA) and semi-analytical models we have developed recently can predict tire-snow interfacial forces at a given density under combined slip conditions. One drawback of the models is that they are only applicable for fresh, low-density snow due to the unavailability of a density-dependent snow model. In reality, the snow density on the ground can vary between that of fresh snow to heavily compacted snow that is similar to ice. Even for fresh snow on the ground, as a vehicle moves forward, the rear wheels experience higher snow densities than the front wheels. In addition, being a natural material, snow's physical properties vary significantly even for the same density.

Existing methods for design optimization under uncertainty assume that a high level of information is available, typically in the form of data. In reality, however, insufficient data prevents correct inference of probability distributions, membership functions, or interval ranges. In this article we use an engine design example to show that optimal design decisions and reliability estimations depend strongly on uncertainty characterization. We contrast the reliability-based optimal designs to the ones obtained using worst-case optimization, and ask the question of how to obtain non-conservative designs with incomplete uncertainty information. We propose an answer to this question through the use of Bayesian statistics. We estimate the truck's engine reliability based only on available samples, and demonstrate that the accuracy of our estimates increases as more samples become available.

Light-weighting vehicles cause an increase in Aluminum Alloy stamping processes in the Automotive Industry. Surface finish and lubricants of aluminum alloy (AA) sheet play an important role in the deep drawing processes as they can affect the friction condition between the die and the sheet. This paper aims to develop a reliable and practical laboratory test method to experimentally investigate the influence of surface finish, lubricant conditions, draw-bead clearances and pulling speed on the frictional sliding behavior of AA 6XXX sheet metal. A new double-beads draw-bead-simulator (DBS) system was used to conduct the simulated test to determine the frictional behavior of an aluminium alloy with three surface lubricant conditions: mill finish (MF) with oil lube, electric discharge texture (EDT) finish with oil lube and mill finish (MF) with dry lube (DL).

Improvements in the pressure drop across and flow homogeneity in the tubes of automotive radiators are needed to reduce the power demands on the vehicle water pump and increase the lifetime of the radiator. The goal of this ongoing work is to develop a set of virtual tools coupling CFD flow simulations with numerical shape optimization methods to assist in the design and testing process of automotive heating and cooling components. In SAE paper 2002-01-0952, “Towards Shape Optimization of Radiator Cooling Tanks,” the authors developed and evaluated optimization criteria for pressure drop and mass flow rate distribution in a water-to-air automotive heat exchanger. In this follow-up paper, results based on the implementation of these optimization criteria are presented. More specifically, results concerning the placement of radiator inlets and outlets are addressed.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.