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The Particulate Matter Index (PMI) is a tool that provides an indication of a fuel’s tendency to produce Particulate Matter (PM) emissions. Currently, the index is being used by various fuel laboratories and the Automotive OEMs as a tool to understand the gasoline fuel’s impact on both PM from engine hardware and vehicle-out emissions. In addition, a newer index that could be used to give an indication of the PM tendency of the gasoline range fuels, called the Particulate Evaluation Index (PEI), is shown to have a good correlation to PMI. The data used in those indices are collected from chemical analytical methods. This paper will compare gas chromatography (GC) methods used by three laboratories and discuss how the different techniques may affect the PMI and PEI calculation.

This paper presents the design development of a SUV liftgate with the intention of minimizing low frequency noise. Structure topology optimization techniques were applied both to liftgate and body FEA models to reduce radiated power from the liftgate inner surface. Topology results are interpreted into structural changes to the original liftgate and body design. Favorable results of equivalent radiated power (ERP) performance with reduced cost and mass is shown compared to baseline liftgate and baseline with tuned vibration absorber (TVA). This simulation includes finite element modeling of coupled fluid/structure interaction between the interior air cavity volume and liftgate structure. In addition to ERP minimization, multi-model optimization (MMO) was used on separate models simultaneously to preserve liftgate structural performance for several customer usage load cases.

A ductile failure criterion (DFC), which defines the stretching failure at localized necking (LN) and treats the critical damage as a function of strain path and initial sheet thickness, was proposed in a previous study. In this study, the DFC is revisited to extend the model to the low stress triaxiality domain and demonstrates on modeling forming limit curve (FLC) of TRIP 690. Then, the model is used to predict stretching failure in a finite element method (FEM) simulation on a TRIP 690 steel rectangular cup draw process at room temperature. Comparison shows that the results from this criterion match quite well with experimental observations.

As material cleanliness and bearing lubrication have improved, wheel bearings are experiencing less raceway spalling failures from rotating fatigue. Warranty part reviews have shown that two of the larger failure modes for wheel bearings are contaminant ingress and Brinell damage from curb and pothole impacts. Warranty has also shown that larger wheels have higher rates of Brinell warranty. This paper discusses the Brinell failure mode for bearings. It reviews a vehicle test used to evaluate Brinell performance for wheel bearings. The paper also discusses a design of experiments to study the effects of factors such as wheel size, vehicle loading and vehicle position versus the bearing load from a vehicle side impact to the wheel. As the trend in vehicle styling is moving to larger wheels and low profile tires, understanding the impact load can help properly size wheel bearings.

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.

The market for electric vehicles and hybrid electric vehicles is expected to grow in the coming years, which is increasing interest in design optimization of electric motors for automotive applications. Under demanding duty cycles, the moving part within a motor, the rotor, may experience varying stresses induced by centrifugal force, a necessary condition for fatigue. Rotors contain hundreds of electrical steel laminations produced by stamping, which creates a characteristic edge structure comprising rollover, shear and tear zones, plus a burr. Fatigue properties are commonly reported with specimens having polished edges. Since surface condition is known to affect fatigue strength, an experiment was conducted to evaluate the effect of sample preparation on tensile and fatigue behavior of stamped specimens. Tensile properties were unaffected by polishing. In contrast, polishing was shown to increase fatigue strength by approximately 10-20% in the range of 105-107 cycles to failure.

Commercially available Generation 3 (GEN3) advanced high strength steels (AHSS) have inherent capability of replacing press hardened steels (PHS) using cold stamping processes. 980 GEN3 AHSS is a cold stampable steel with 980 MPa minimum tensile strength that exhibits an excellent combination of formability and strength. Hot forming of PHS requires elevated temperatures (> 800°C) to enable complex deep sections. 980 GEN3 AHSS presents similar formability as 590 DP material, allowing engineers to design complex geometries similar to PHS material; however, its cold formability provides implied potential process cost savings in automotive applications. The increase in post-forming yield strength of GEN3 AHSS due to work and bake hardening contributes strongly toward crash performance in energy absorption and intrusion resistance.

More than four decades ago, the concept of zero defects was coined by Phillip Crosby. It was only a vision at the time, but the introduction of Artificial Intelligence (AI) in manufacturing has since enabled it to become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few Defects Per Million of Opportunities (DPMO). Detecting these rare quality events is one of the modern intellectual challenges posed to this industry. Process Monitoring for Quality (PMQ) is an AI and big data-driven quality philosophy aimed at defect detection and empirical knowledge discovery. Detection is formulated as a binary classification problem, where the right Machine Learning (ML), optimization, and statistics techniques are applied to develop an effective predictive system.

Based on findings from micromechanical studies, a Ductile Failure Criterion (DFC) was proposed. The proposed DFC treats localized necking as failure and critical damage as a function of strain path and initial sheet thickness. Under linear strain path assumption, a method to predict Forming Limit Curve (FLC) is derived from this DFC. With the help of predetermined effect functions, the method only needs a calibration at uniaxial tension. The approach was validated by predicting FLCs for sixteen different aluminum and steel sheet metal materials. Comparison shows that the prediction matches quite well with experimental observations in most cases.

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 objective of this research is to develop a component based enhanced production process after End of Line (EOL) testing. This process will add more quality validation evaluations, but will not require any disassembling of the parts or damage to them. It will help the suppliers to avoid scrap and rework parts as well as General Motors (GM) to reduce warranty and recalls. An Enhanced Production Process was implemented in March, 2016 at a supplier in Mexico. The Enhanced Audit Station implementation is to ensure that the supplier is satisfying the Production Part Approval Process (PPAP) requirements. The most important four components are: Touch Appearance Lighting and Color (TALC), Appearance Approval Report (AAR), Dimensional Checks, and Function Testing. Through statistics, a pilot study was conducted to correlate the selected variables to reduce warranty.

Flexible molded polyurethane foams are widely used in automotive industry. As porous-elastic materials, they can be used as decoupler layers in conventional sound insulation constructions or as sound absorbers in vehicle trim parts. Flexible molded polyurethane foams are produced by reacting of liquid Isocyanate (Iso) with a liquid Polyol blend, catalysts, and other additives. Their acoustic performance can be changed by varying the mixing ratio, the weight proportion of two components: Iso and Polyol. Consequently, the sound insertion loss (IL) of barrier/foam constructions and acoustic absorption of a single foam layer will vary. In this paper, based on one industry standard flexible molded polyurethane foam process, the relationship between foam mixing ratio and foam acoustic performance is studied in terms of IL and sound absorption test results.

Understanding customer expectations is critical to satisfying customers. Holding customer clinics is one approach to set winning targets for the engineering functional measures to drive customer satisfaction. In these clinics, customers are asked to operate and interact with vehicle systems or subsystems such as doors, lift gates, shifters, and seat adjusters, and then rate their experience. From this customer evaluation data, engineers can create customer loss or preference functions. These functions let engineers set appropriate targets by balancing risks and benefits. Statistical methods such as cumulative customer loss function are regularly applied for such analyses. In this paper, a new approach based on the Taguchi method is proposed and developed. It is referred to as Taguchi Customer Loss Function (TCLF).

General Motors (GM) vehicle design operations group has envisioned that all designers and Design Engineers (DEs) should be able to analyze simple and single components and produce robust subsystem parts to support full vehicle system analysis. This vision is achieved by developing the Smart Simulation Tool (SST) within the Siemens NX CAD system. This tool empowers the designers to take charge of simple parts and produce high quality parts first time. This tool will also make both design and engineering analysis organizations at General Motors more efficient and productive. This paper describes the Smart Simulation Tool that was developed to automate the pre and post processing tasks of the Siemens NX Advanced Simulation process. Generally, the simulation process consumes a lot of designer’s time for building the Finite Element Analysis (FEA) models, typically one to two hours and is very tedious and has the potential for errors.

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.

Elastomers are widely used in many automotive components such as seals, gaskets etc., for their hyperelastic properties. They can undergo large strain and can return to their original state with no significant deformation making them suitable for energy dissipation applications. Most common testing procedures include uniaxial tension, pure shear, biaxial tension and volumetric compression under quasi-static loading conditions. The results from these tests are used to generate material models for different finite element (FE) solvers, such as LS-DYNA. Commonly used material models for elastomers in LS-DYNA are the Ogden Material Model (MAT77), which uses parameter-based approach and the Simplified Rubber Material Model (MAT181), which uses tabulated input data. Prediction of rate dependent behavior of elastomers is gaining interest as, for example, during a crash simulation the components undergo impact under different strain rates.

Fatigue failure is one of the major failure modes for internal combustion engines, especially with reduction in engine size and increase in combustion pressure and operating temperature. Dynamometer tests are devised to ensure engine durability for high and low cycle fatigue. With the advent of CAE technology, the dynamometer test behavior can be simulated using CAE analysis and engine durability can be assessed. The data generated in CAE analyses can be used to predict failure of the engines or future engine design modifications. The present paper has two parts - first is running finite element analysis (FEA) to get stress, strain data and running high cycle fatigue analysis to get safety factors and second is creating a predictive tool to assess failures using data from the first part as inputs. Using advancements in the field of machine learning, the paper presents use of support vector machine (SVM) algorithm to predict failure of the engine based on inputs.

Twist spring-back would interfere with stamping or assembling procedures for advanced high strength steel. A “homeopathic” resolution for controlling the twist spring-back is proposed using unbalanced post-stretching configuration. Finite element forming simulation is applied to evaluate and compare the performance for each set of unbalanced post-stretching setup. The post-stretching is effectuated by stake bead application. The beads are separated into multiple independent segments, the height and radii of which can be adjusted individually and asymmetrically. Simulation results indicate that the twist spring-back can be effectively controlled by reducing the post-stretching proximate to the asymmetric part area. Its mechanism is qualitatively revealed by stress analyses, that an additional but acceptable cross-sectional spring-back re-balances the sprung asymmetrical geometry to counter the twist effect.

Given their high-power density, large range of speed change, and reputation of being quieter than counter-shaft gear sets, planetary gear sets (PGS) have advantages to be applied in electric vehicle (EV) applications. Since electric drive unit (EDU) designs are often subject to accelerated development timelines with more versatile gear set layouts than conventional automotive transmissions, accurate prediction of PGS load sharing is needed. In the past, PGS load sharing imbalance used to be considered as a gear set problem focusing only on the effect to gear performance. Finding a closed-form formula has been a focus in gear design. However, early bearing failure in wind turbine gearboxes exposed the limitation of this strategy. With extensive field and laboratory testing, engineers started to notice that load sharing imbalance is essentially a system issue. Non-torque loads on PGS should be considered in the estimation by a gearbox system model.