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Model-guided development of drivetrain control and calibration is a key enabler of robust and efficient vehicle design process. A number of CAE tools are available today for modeling hydro-mechanical systems. Automatic transmission behaviors are well understood to effectively tune the model parameters for targeted applications. Drivetrain models provide physical insight for understanding the effects of component interactions on system behaviors. They are also widely used in HIL/SIL environments to debug control strategies. Nonetheless, it is still a challenge to predict shift quality, especially during a sequence of multiple events, with enough accuracy to support model-guided control design and calibration. The inclusion of hydraulic circuits in simulation models often results in challenges for numerical simulation.

In this paper, a CAE methodology based on a multiphysics approach for engine gear noise evaluation is reviewed. The method comprises the results and outputs from several different analytical domains to perform the noise risk assessment. The assessment includes the source-path analysis of the gear-induced rattling and whining noise. The vibration data from the exterior surface of the engine is extended through acoustic analysis to perform the engine noise simulation and to identify acoustic hot spots contributing to the noise. The study includes simulations under different engine loading conditions with results presented in both time and frequency domains. Various sensitivity analyses involving different gear geometries and micro-geometries are investigated as well. Finally, the simulation results from three different engines are compared vis-a-vis.

An eleven-group taxonomy was created to classify real-world side crashes from the Crashworthiness Data System (CDS) component of the National Automotive Sampling System (NASS). Three steps were taken to develop the classification scheme: (1) side-impact towaway crashes were identified by examining 1987-2016 model year light passenger vehicles with Collision Deformation Classification (CDC) data from the 1997-2015 calendar years of NASS; (2) case reviews, engineering judgments, and categorization assessments were conducted on these data to produce the eleven-group taxonomy; and (3) taxonomic groups were reviewed relative to regulated crash test procedures. Two of the taxonomic groups were found to have the most frequent crash types, each contributing approximately 22% to the total, followed closely by a third taxonomic group contributing approximately 19%.

The risk of skid lines for Class A panels has to be assessed before releasing the die development for hard tooling. Criteria are needed to predict skid lines in the formability evaluation stage to avoid expensive changes to tooling and process for resolving skid line issue in production. In this study, criteria using three different measured parameters were developed and validated. A draw-stretch-draw (DSD) test procedure was developed to generate skid lines on lab samples for the physical evaluation. This was done using tooling with various die entry radii and different draw beads. The skid line severity of lab samples was rated by specialists in the inspection of automotive outer panel surface quality. The skid line rating was correlated with geometric measurements of the lab samples after the DSD test. The sensitivity of the appearance of skid lines to tooling and process parameter variations was identified.

The failure of an A/C system often results in the introduction of contaminants to the A/C system. The sources of the contaminants include debris from damaged components and debris from the surrounding environment. Returning the A/C system to service requires the removal of these contaminants from any reused components. The recommended approach to cleaning contaminated components and systems is to flush with a solvent flushing machine. Previous internal studies have concluded that solvent flushing will remove all contaminants, restoring component and system performance. Many commercial refrigerant recovery and recharge machines include a refrigerant “flush” feature which can flush oil from the system and components with the systems refrigerant. The effectiveness of using the “flush” feature of a refrigerant recovery and recharge machine with an added in-line filter to remove contaminants is investigated.

In a separate SAE paper (Cylinder Head Design Process to Improve High Cycle Fatigue Performance), cylinder head high cycle fatigue (HCF) analysis approach and damage calculation method were developed and presented. In this paper, the HCF damage calculation method is used for risk assessment related to customer drive cycles. Cylinder head HCF damage is generated by repeated stress alternation under different engine operation conditions. The cylinder head high cycle fatigue CAE process can be used as a transfer function to translate engine operating conditions to cylinder head damage/life. There are many inputs, noises, and design parameters that contribute to the cylinder head HCF damage CAE transfer function such as cylinder pressure, component temperature, valve seat press fit, and cylinder head manufacturing method. Material properties and the variation in material properties are also important considerations in the CAE transfer function.

For aluminum automotive cylinder head designs, one of the concerning failure mechanisms is thermo-mechanical fatigue from changes in engine operating conditions. After an engine is assembled, it goes through many different operating conditions such as cold start, through warm up, peak power, and intermediate cycles. Strain alternation from the variation in engine operation conditions change may cause thermo-mechanical fatigue (TMF) failure in combustion chamber and exhaust port. Cylinder heads having an integrated exhaust manifold are especially exposed to this failure mode due to the length and complexity of the exhaust gas passage. First a thermo-mechanical fatigue model is developed to simulate a known dynamometer/bench thermal cycle and the corresponding thermo-mechanical fatigue damage is quantified. Additionally, strain state of the cylinder head and its relation to thermo-mechanical fatigue are discussed. The bench test was used to verify the TMF analysis approach.

A transmission system model is developed at various complexities in order to capture the transient behaviors in drivability and fuel economy simulations. A large number of model parameters bring more degree of freedom to correlate with vehicular test data. However, in practice, it requires extensive time and effort to tune the parameters to satisfy the model performance requirements. Among the transmission model, a hydraulic clutch actuator plays a critical role in transient shift simulations. It is particularly difficult to tune the actuator model when it is over-parameterized. Therefore, it is of great importance to develop a hydraulic actuator model that is easy to adjust while retaining sufficient complexity for replicating realistic transient behaviors. This paper describes a systematic approach for reducing the hydraulic actuator model into a piecewise 1st order representation based on piston movement.

Developing requirements for automotive electric/electronic systems is challenging, as those systems become increasingly software-intensive. Designs must account for unintended interactions among software features, combined with unforeseen environmental factors. In addition, engineers have to iteratively make architectural tradeoffs and assign responsibilities to each component in the system to accommodate new safety requirements as they are revealed. ISO 26262 is an industry standard for the functional safety of automotive electric/electronic systems. It specifies various processes and procedures for ensuring functional safety, but does not limit the methods that can be used for hazard and safety analysis. System Theoretic Process Analysis (STPA) is a new technique for hazard analysis, in the sense that hazards are caused by unsafe interactions between components (including humans) as well as component failures and faults.

The National Highway Traffic Safety Administration (NHTSA) recently published a Request for Comments regarding a potential upgrade to the US New Car Assessment Program (US NCAP) - a star-rating program pertaining to vehicle crashworthiness. Therein, NHTSA (a) cited two metrics for assessing head risk: Head Injury Criterion (HIC15) and Brain Injury Criterion (BrIC), and (b) proposed to conduct risk assessment via its risk curves for those metrics, but did not prescribe a specific method for applying them. Recent studies, however, have indicated that the NHTSA risk curves for BrIC significantly overstate field-based head injury rates. Therefore, in the present three-part study, a new set of BrIC-based risk curves was derived, an overarching head risk equation involving risk curves for both BrIC and HIC15 was assessed, and some additional candidate-predictor-variable assessments were conducted. Part 1 pertained to the derivation.

An eight-group taxonomy was created to classify real-world frontal crashes from the Crashworthiness Data System (CDS) component of the National Automotive Sampling System (NASS). Three steps were taken to develop the taxonomy: (1) frontal-impact towaway crashes were identified by examining 1985-2005 model year light passenger vehicles with Collision Deformation Classification (CDC) data from the 1995-2005 calendar years of NASS; (2) case reviews, engineering judgments, and categorization assessments were conducted on these data to produce the eight-group taxonomy; and (3) two subsets of the NASS dataset were analyzed to assess the consistency of the resulting taxonomic-group frequencies. “Full-engagement” and “Offset” crashes were the most frequent crash types, each contributing approximately 33% to the total. The group identified as “D, Y, Z No-Rail” was the most over-represented crash type for vehicles with at least one seriously-injured occupant.

Response surface methodologies (RSMs) have been proposed as surrogate models in vehicle design processes to gain insight and improve turnaround time for optimization and robust design. However, when studying the vehicle occupants during crash events, nonlinearities in responses, coupled with the relatively high dimensionality of vehicle design, can yield misleading results with little or no warning from the response surface algorithms. To ensure the accuracy and reliability of RSMs, fast and dependable error estimation procedures are essential for enlightening how well a response surface predicts highly nonlinear phenomena, given a limited number of model simulations. Such error estimation methods are also useful for providing guidance on how many simulation runs are needed for reliable RSM construction. In this paper, a fast cross validation error estimate procedure is first presented, applied to the multivariable adaptive regression spline (MARS) response surface method.

A theoretical, mathematical, risk assessment procedure was developed to estimate the fraction of drivers that incurred head and thoracic AIS3+ injuries in full-engagement frontal crashes. The estimates were based on numerical simulations of various real-world events, including variations of crash severity, crash speed, level of restraint, and occupant size. The procedure consisted of four steps: (1) conduct the simulations of the numerous events, (2) use biomechanical equations to transform the occupant responses into AIS3+ risks for each event, (3) weight the maximum risk for each event by its real-world event frequency, and (4) sum the weighted risks. To validate the risk assessment procedure, numerous steps were taken. First, a passenger car was identified to represent average field performance.

To support its recycling efforts, Ford Motor Company is using a Raman based instrument, the RP-1, co-developed with SpectraCode Inc. to identify unknown polymeric parts. Our recycling initiative involves detailed dismantling of our vehicles into individual parts, calculating the percentage recyclability and making recommendations for the future use of recycled polymers. While Ford has voluntarily adopted the SAE J1344 marking protocol for identifying part material composition, a large number of unmarked parts still exist and require identification. This identification is being done with the help of RP-1. To facilitate this identification, we have generated an accurate reference library of Raman spectra for comparison to those of unknown materials. This paper will describe the techniques that were used to develop and refine the RP-1 reference library to identify automotive polymers, especially black/dark plastics.

An improved defect detection system for painted surfaces has been developed which significantly increases topographic defect visibility (dirt-in-paint, sags/runs/drips, sealer-under-paint, spits, craters, etc.) for the final inspector / polisher. These minor defects can then be repaired before leaving the “spillout” deck. A new luminaire was designed to maximize the contribution of several applicable principles. The new process has significantly reduced the number of defects leaving the spillout area, doubled the number of “zero defect” vehicles, and increased energy savings from 25-40%. The improved Paint Inspection Lighting process was issued as a Uniform Process specification by the Ford Motor Company Vehicle Operations and was implemented in all of the Ford North American assembly plants.