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Exterior component integration presents competing performance challenges for balanced exterior styling, safety, ‘structural feel’ [1] and durability. Industry standard practices utilize noise and vibration mode maps and source-path-receiver [2] considerations for component mode frequency placement. This modal frequency placement has an influence on ‘structural feel’ and durability performance. Challenges have increased with additional styling content, geometric overhang from attachment points, component size and mass, and sensor modules. Base excitation at component attachment interfaces are increase due to relative positioning of the suspension and propulsion vehicle source inputs. These components might include headlamps, side mirrors, end gates, bumpers and fascia assemblies. Here, we establish basic expectations for the behavior of these systems, and ultimately consolidate existing rationales that are applied to these systems.

The human-robot interaction (HRI) is involved in a lift assistant system of manufacturing assembly line. The admittance model is applied to control the end effector motion by sensing intention from force of applied by a human operator. The variable admittance including virtual damping and virtual mass can improve the performance of the systems. But the tuning process of variable admittance is un-convenient and challenging part during the real test for designers, while the offline simulation is lack of learning process and interaction with human operator. In this paper, the Iterative learning algorithm is proposed to emulate the human learning process and facilitate the variable admittance control design. The relationship between manipulate force and object moving speed is demonstrated from simulation data. The effectiveness of the approach is verified by comparing the simulation results between two admittance control strategies.

This article covers an application of third-generation advanced high-strength steel (3GAHSS) grades to vehicle lightweight body structure development. Design optimization of a vehicle body structure using a multi-scale material model is discussed. The steps in the design optimization and results are presented. Results show a 30% mass reduction potential over a baseline mid-size sedan body side structure with the use of 3GAHSS.

Third generation advanced high strength steels (3GAHSS) are increasingly used in automotive for light weighting and safety body structure components. However, high material strength usually introduces higher springback that affects the dimensional accuracy. The ability to accurately predict springback in simulations is very important to reduce time and cost in stamping tool and process design. In this work, tension and compression tests were performed and the results were implemented to generate Isotropic/Kinematic hardening (I/KH) material models on a 3GAHSS steel with 980 MPa minimum tensile strength. Systematic material model parametric studies and evaluations have been conducted. Case studies from full-scale industrial parts are provided and the predicted springback results are compared to the measured springback data. Key variables affecting the springback prediction accuracy are identified.

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.

The purge pump is used to pull evaporative gases from canister and send to engine for combustion in Turbocharged engines. The purge pump with impeller at one end and electric motor at the other end is supported by the ball bearing assembly. A bearing kinematic model to predict forcing function due to defect in ball bearing arrangement, coupled with bearing dynamic model of rotor because of rotating component, is proposed in this paper to get accumulated effect on transmitted force to the purge pump housing. Rotor dynamic of purge pump rotor components only produces certain order forcing responses which can be simulated into the multibody software environment, knowing the ball bearing geometry parameters hence providing stiffness parameter for rotor system.

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies.

The piston temperature has to be carefully controlled to achieve effective and efficient thermal management in the internal combustion engines. One of the common methods to cool piston is by injecting oil from the crankcase underside to the piston under-crown area. In the present study, a novel 3-D multiphase thermal-fluid coupled model was developed using the commercial CFD software SimericsMP+ to study the piston cooling using the oil jet. In this model, an algorithm was proposed to couple the fluid and solid computation domain to account for the different timescale of heat transfer in the fluid and solid due to the high thermal inertia of the solid piston. The heat transfer coefficient (HTC) and reference temperature were mapped to the piston top surface and the liner temperature distribution was also used as the boundary condition. The temperature-dependent material properties, piston motion, and thermal contact resistance between the ring and piston were also accounted for.

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.

Disc brakes operate with very close proximity of the brake pads and the brake rotor, with as little as a tenth of a millimeter of movement of the pads required to bring them into full contact with the rotor to generate braking torque. It is usual for a disc brake to operate with some amount of residual drag in the fully released state, signifying constant contact between the pads and the rotor. With this contact, every miniscule movement of the rotor pushes against the brake pads and changes the forces between them. Sustained loads on the brake corner, and maneuvers such as cornering, can both produce rotor movement relative to the caliper, which can push it steadily against one or both of the brake pads. This can greatly increase the residual force in the caliper, and increase drag. This dependence of drag behavior on the movement of the brake rotor creates some vehicle-dependent behavior.

The Hybrid Cellular Automaton (HCA) algorithm is a generative design approach used to synthesize conceptual designs of crashworthy vehicle structures with a target mass. Given the target mass, the HCA algorithm generates a structure with a specific acceleration-displacement profile. The extended HCA (xHCA) algorithm is a generalization of the HCA algorithm that allows to tailor the crash response of the vehicle structure. Given a target mass, the xHCA algorithm has the ability to generate structures with different acceleration-displacement profiles and target a desired crash response. In order to accomplish this task, the xHCA algorithm includes two main components: a set of meta-parameters (in addition target mass) and surrogate model technique that finds the optimal meta-parameter values. This work demonstrates the capabilities of the xHCA algorithm tailoring acceleration and intrusion through the use of one meta-parameter (design time) and the use of Kriging-assisted optimization.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

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.

Over the past several years a task group within the SAE Automotive Corrosion and Protection (ACAP) Committee has conducted extensive on-vehicle field testing and numerous accelerated lab tests with the goal of establishing a standard accelerated test method for cosmetic corrosion evaluations of finished aluminum auto body panels. This project has been a cooperative effort with OEM, supplier, and consultant participation and was also supported in part by DOE through USAMP (AMD 309). The focus of this project has been the identification of a standardized accelerated cosmetic corrosion test that exhibits the same appearance, severity, and type of corrosion products that are exhibited on identical painted aluminum panels exposed to service relevant environments. Multi-year service relevant exposures were conducted by mounting panels on-vehicles in multiple locations in the US and Canada.

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.

This report describes a new driving simulator capability at General Motors (GM) Research and Development's (R&D) Vehicle Development Research (VDR) Laboratory and its application in an iterative HMI development process. The paper also provides an overview of three recent simulator usability tests supporting HMI development.

As the automotive industry quickly moves towards hybridized and electrified vehicles, the optimal integration of power electronics in these vehicles will have a significant impact not only on the cost, performance, reliability, and durability; but ultimately on customer acceptance and market success of these technologies. If properly executed with the right cost, performance, reliability and durability, then both the industry and the consumer will benefit. It is because of these interdependencies that the pace and scale of success, will hinge on effective collaboration. This collaboration will be built around the convergence of automotive and industrial technology. Where real time embedded controls mixes with high power and voltage levels. The industry has already seen several successful collaborations adapting power electronics to the automotive space in target vehicles.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

Vehicle certification requirements generally fall into 2 categories: self-certification and various forms of type approval. Self-certification requirements used in the United States under Federal Motor Vehicle Safety Standards (FMVSS) regulations must be objective and measurable with clear pass / fail criteria. On the other hand, Type Approval requirements used in Europe under United Nations Economic Commission for Europe (UNECE) regulations can be more open ended, relying on the mandated 3rd party certification agency to appropriately interpret and apply the requirements based on the design and configuration of a vehicle. The use of 3rd party certification is especially helpful when applying regulatory requirements for complex vehicle systems that operate dynamically, changing based on inputs from the surrounding environment. One such system is Adaptive Driving Beam (ADB).

In traditional FE based structure-borne noise analysis, interior trims are normally modeled as lump masses in the FE structure model and acoustic specific impedance of the trim is assigned to the FE acoustics model when necessary. This simplification has proven to be effective and sufficient for low frequency analysis. However, as the frequency goes into the mid-frequency range, the elastic behavior of the trim may impose some effects on the structural and acoustic responses. The approach described in this paper is based on the structural FE and acoustic SEA coupling analysis developed by ESI, aiming to improve the modeling efficiency for a possible quick turnaround in virtual assessments.