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The objective of this paper is to demonstrate that frequency domain methods for calculating structural response and fatigue damage can be more widely applicable than previously thought. This will be demonstrated by comparing results of time domain vs. frequency domain approaches for a series of fatigue/durability problems with increasing complexity. These problems involve both static and dynamic behavior. Also, both single input and multiple correlated inputs are considered. And most important of all, a variety of non-stationary loading types have been used. All of the example problems investigated are typically found in the automotive industry, with measured loads from the field or from the proving ground.

Ride Hailing service and Dynamic Shuttle are two key smart mobility practices, which provide on-demand door-to-door ride-sharing service to customers through smart phone apps. On the other hand, some big companies spend millions of dollars annually in third party vendors to offer shuttle services to pick up and drop off employees at fixed locations and provide them daily commutes for employees to and from work. Efficient fixed routing algorithms and analytics are the key ingredients for operating efficiency behind these services. They can significantly reduce operating costs by shortening bus routes and reducing bus numbers, while maintaining the same quality of service. This study developed an off-line optimization routing method for employee shuttle services including regular work shifts and demand based shifts (e.g. overtime shifts) in some regions.

In this paper, a simplified and systematic approach to integrate reliability and durability aspects in design process is presented. A six step process is explained with the help of examples. Two alternatives for gathering means and standard deviations for key parameters are discussed. First a DOE approach based on orthogonal arrays is presented. Second approach is based on Taylor Series expansion. An example of beam design is solved with both of these approaches. The Second example also considers the degradation with time in service.

Many descriptions of product development are based on a timeline of activity. Timelines typically do not characterize the underlying strategy and flexibility embodied in the technical activity that actually takes place between activity nodes. Timelines alone will inhibit evolving to a more rational approach to product development. The view of engineering described in this paper is a functional view of engineering. It is what engineers do. It is aligned with the technical tools used by engineers. It applies to both product development and manufacturing. It's purpose is to enhance understanding of the function of engineering activities, including reliability.

There are several reasons why it can be daunting to apply Six Sigma to product creation. Foremost among them, the functional performance of new technologies is unknown prior to starting a project. Although, Design For Six Sigma (DFSS) was developed to overcome this difficulty, a lack of applicable in-class case studies makes it challenging to train the product creation community. The current paper describes an in-class project which illustrates how Six Sigma is applied to a simulated product creation environment. A toy construction set (TCS) project is used to instruct students how to meet customer expectations without violating cost, packaging volume and design-complexity constraints.

Designing a durability test for an automatic transmission that appropriately reflects customer usage during the lifetime of the vehicle is a formidable task; while the transmission and its components must survive severe usage, overdesigning components leads to unnecessary weight, increased fuel consumption and increased emissions. Damage to transmission components is a function of many parameters including customer driving habits and vehicle and transmission characteristics such as weight, powertrain calibration, and gear ratios. Additionally, in some cases durability tests are required to verify only a subset of the total parameter space, for example, verifying only component modifications. Lastly, the ideal durability test is designed to impose the worst case loading conditions for the maximum number of internal components, be as short as practicable to reduce testing time, with minimal variability between tests in order to optimize test equipment and personnel resources.

Transmission system models require a high level of fidelity and details in order to capture the transient behaviors in drivability and fuel economy simulations. Due to model fidelity, manufacturing tolerances, frictional losses and other noise sources, parametrization and tuning of a large number of parameters in the plant model is very challenging and time consuming. In this paper, we used particle swarm optimization as the key algorithm to fast correlate the open-loop performance of an automatic transmission system plant model to vehicle launch and coast down test data using vehicle control inputs. During normal operations, the model correlated well with test data. For error states, due to the lack of model fidelity, the model cannot reproduce the same error state quantitatively, but provided a valuable methodology for qualitatively identifying error states at the early stages.

Recently, papers have been published purporting to study the effect of rear axle tramp during tread separation events, and its effect on vehicle handling [1, 2]. Based on analysis and physical testing, one paper [1] has put forth a mathematical model which the authors claim allows vehicle designers to select shock damping values during the development process of a vehicle in order to assure that a vehicle will not experience axle tramp during tread separations. In the course of their work, “lumpy” tires (tires with rubber blocks adhered to the tire's tread) were employed to excite the axle tramp resonance, even though this method has been shown not to duplicate the physical mechanisms behind an actual tread belt separation. This paper evaluates the theories postulated in [1] by first analyzing the equations behind the mathematical model presented. The model is then tested to see if it agrees with observed physical testing.

Automated driving is currently one of the most active areas of research worldwide. While the general progress in developing specific algorithms for perception, planning and control tasks is very advanced, testing and validation of the resulting functions is still challenging due to the large number of possible scenarios and generation of ground-truth. Currently, real world testing and simulations are used in combination to overcome some of these challenges. While real world testing does not suffer from imperfect sensor models and environments, it is expensive, slow and not accurately repeatable and therefore unable to capture all possible scenarios. However, simulation models are not sophisticated enough to fully replace real world testing. In this paper, we propose a workflow that is capable of augmenting real sensor-level data with simulated sensor data.

There is an ongoing effort in the industry to develop an accelerated corrosion test for automotive heat exchangers. This has become even more important as automakers are focusing on corrosion durability of 15 years in the field versus current target of 10 years. To this end an acid immersion test was developed and reported in a previous paper for condensers (1). This paper extends those results to evaporators and establishes the efficacy of the test using these results and those reported in the literature. The paper also discusses variability in corrosion test results as observed in tests such as ASTM G85:A3 Acidified Synthetic Sea Water Test (SWAAT), and its relation to field durability.

An experimental study of the acoustic characteristics of automotive catalytic converters is presented. The investigation addresses the effects and relative importance of the elements comprising a production catalytic converter assembly including the housing, substrate, mat and seals. Attenuation characteristics are measured for one circular and one oval catalytic converter geometry, each having 400 cell per square inch substrates. For each geometry, experimental results are presented to address the effect of individual components in isolation, and in combination with other assembly components. Additional experiments investigate the significance of acoustic paths around the substrate and through the peripheral wall of the substrate. The experimental results are compared to address the significance of each component on the overall attenuation.

This paper describes the issues encountered in using conventionally acquired tire test data for dynamic total vehicle handling simulations and the need for improved methodology. It describes the new test procedure that was used to acquire all three forces and three moments in a transient mode for a matrix of loads, slip and camber angles. A study of the test data supports the premises that the overturning moment, Mx, should not be neglected in dynamic simulations, and that the effects of camber should not be treated as only an independent, linearly additive, camber thrust. Instead of the conventional application of a bi-cubic regression fit to a six region data division, a new algorithm is applied. The data is divided differently into five regions in the α - Fz plane, and a variable format regression equation is applied as appropriate. The resulting regression coefficients matrix is readily usable in dynamic simulations, and is shown to have a superior curve fit to the test data.

This paper investigates the use of an adaptive proportional-integral-derivative (APID) controller to reduce a combustion engine crankshaft speed pulsation. Both computer simulations and engine test rig experiments are used to validate the proposed control scheme. The starter/alternator (S/A) is used as the actuator for engine speed control. The S/A is an induction machine. It produces a supplemental torque source to cancel out the fast engine torque variation. This machine is placed on the engine crankshaft. The impact of the slowly varying changes in engine operating conditions is accounted for by adjusting the APID controller parameters on-line. The APID control scheme tunes the PID controller parameters by using the theory of adaptive interaction. The tuning algorithm determines a set of PID parameters by minimizing an error function. The error function is a weighted combination of the plant states and the required control effort.

One important design goal for spark-ignited engines is to minimize cyclic variability. A small amount of cyclic variability (slow burns) can produce undesirable engine vibrations. A larger amount of cyclic variability (incomplete burns) leads to increased hydrocarbon consumption/emissions. Recent studies have reported deterministic patterns in cyclic variability under extremely lean (misfiring) operating conditions. The present work is directed toward more realistic non-misfiring conditions. Production engine test results suggest that deterministic patterns in cyclic variability are the consequence of incomplete combustion, hence control algorithms based on the occurrence of these patterns are not expected to be of significant practical value.

Modular systems provide the ability to achieve product variety through the combination and standardization of components. The trend within the automotive industry is towards modular systems. The automotive manufacturers separate the vehicle into modular systems (chunks), which may be built and tested off line before assembled for vehicle installation. In this paper, software that utilizes a new clustering algorithm for development of integrated and modular vehicle architectures is presented. The algorithm and software tool presented in this paper allows the system designer to automate and optimize the vehicle system development process. A vehicle system model that identifies all the functional (data, and control) interfaces between vehicle system functions is the input to the software tool. The software tool is capable of analyzing this input data and determining the vehicle system architecture by optimally grouping (integrating) functions into physical modules.

Distinguishing physical modes from mathematical modes in the modal analysis of complex systems, such as full vehicle structures, is a difficult and time-consuming process. The major tools frequently used are stabilization diagrams, mode indicator functions, or modal participation factors. When closely spaced modes are to be identified, the stabilization diagrams and mode indicator functions are no longer effective. Even the reciprocities of mode shapes and modal participation factors cannot be well satisfied to indicate whether a mode is a physical one, when measurement errors are large. To overcome these difficulties, an optimization procedure is developed, whereby physical modes can be sorted out in a given frequency range while the error between measured and synthesized frequency responses is minimized. An optimal subset selection algorithm is used in this procedure.

In this paper, a novel mixed-integer programming model is developed to optimally assign the die sets to candidate plants to minimize the total costs. The total costs include freight shipping stamped parts to assembly plants, die set movement, outsourcing, and utilization. Therefore, the objective function is weighted multi-criteria and it takes into consideration some of the key constraints in the real-world condition including “must-move die sets”. An optimization tool has been developed that takes several inputs and feeds them as the input to the mathematical model and generates the optimal assignments with the directional costs as the output. The tool has been tested for several plants at Ford and has proved its robustness by saving millions of dollars. The developed tool can easily be applied to other manufacturing systems and original equipment manufacturers (OEMs).

We describe an optimization model developed by Ford Motor Company to reallocate stamped parts between facilities when business conditions change. How can the business meet new targets when demand starts to exceed existing capacity? Likewise, how can it respond when demand is lower than expected? Sometimes the business can reduce costs by transferring production to a different location or by outsourcing parts. We describe in this paper how mathematical optimization can identify solutions that balance both logistical and outsourcing costs. We explain the algorithm and demonstrate with a small example how it recommends sourcing plans that minimize cost.

In the early days of the automotive industry, durability and reliability were hit or miss affairs, with end-users often being the first to know about any durability problems - and in many cases forming an essential part of the development process. More recently, automotive companies have developed proving ground and laboratory test procedures that aim to simulate typical or severe customer usage. These test procedures have been used to develop the products through a series of prototypes and to prove the durability of the product prior to release in the marketplace. Now, commercial pressures and legal requirements have led to increasing reliance on CAE methods, with fatigue life prediction having a central role in the durability engineering process.

Historical driver behavior and drive style are crucial inputs in addition to V2X connectivity data to predict future events as well as fuel consumption of the vehicle on a trip. A trip is a combination of different maneuvers a driver executes to navigate a route and interact with his/her environment including traffic, geography, topography, and weather. This study leverages big data analytics on real-world customer driving data to develop analytical modeling methodologies and algorithms to extract maneuver-based driving characteristics and generate a corresponding maneuver distribution. The distributions are further segmented by additional categories such as customer group and type of vehicle. These maneuver distributions are used to build an aggressivity distribution database which will serve as the parameter basis for further analysis with traffic simulation models.