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To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

Efficient methods for simulating operators performing part handling tasks in manufacturing plants are needed. The simulation of part handling motions is an important step towards the implementation of virtual manufacturing for the purpose of improving worker productivity and reducing injuries in the workplace. However, industrial assembly tasks are often complex and involve multiple interactions between workers and their environment. The purpose of this paper is to present a series of industrial simulations using the Human Motion Simulation Framework developed at the University of Michigan. Three automotive assembly operations spanning scenarios, such as small and large parts, tool use, walking, re-grasping, reaching inside a vehicle, etc. were selected.

Fast-running metamodels (surrogates or response surfaces) that approximate multivariate input/output relationships of time-consuming CAE simulations facilitate effective design trade-offs and optimizations in the vehicle development process. While the cross-validated nonparametric metamodeling methods are capable of capturing the highly nonlinear input/output relationships, it is crucial to ensure the adequacy of the metamodel error estimates. Moreover, in order to circumvent the so-called curse-of-dimensionality in constructing any nonlinear multivariate metamodels from a realistic number of expensive simulations, it is necessary to reliably eliminate insignificant inputs and consequently reduce the metamodel prediction error by focusing on major contributors. This paper presents a robust data-adaptive nonparametric metamodeling procedure that combines a convergent variable screening process with a robust 2-level error assessment strategy to achieve better metamodel accuracy.

For the assessment of vehicle acoustics in the early design stages of a vehicle program, the use of full vehicle SEA models is becoming the standard analysis method in the US automotive industry. One benefit is that OEM's and Tier 1 suppliers are able to cascade lower level acoustic performance targets for NVH systems and components. Detailed SEA system level models can be used to assess the performance of systems such as dash panels, floors and doors, however, the results will be questionable until test data Is available. Correlation can be accomplished with buck testing, which is a common practice in the automotive industry for assessing the STL (sound transmission loss) of vehicle level components. The opportunity to conduct buck testing can be limited by the availability of representative bodies to be cut into bucks and the availability of a transmission loss suite with a suitably large opening.

The so-called Modified Martempering discussed in this work differs from the standard martempering by that the temperature of the quenching bath is below the Ms point. In spite of the fact the lower temperature increases the severity of quenching, this also usually avoids the bainite formation, and by this reason, it is possible to make a fair comparison between different processes, which result in different microstructures. The present study shows the results in terms of mechanical properties, impact resistance in special of a cold work tool steel class, after being heat treated by the isothermal modified martempering process, as well as a comparison with the conventional quenching and tempering process and the austempering as well.

The innovation term has been so widely misused that the confusion observed among the companies trying to get themselves into the innovation realm is a common and natural consequence. The lack of understanding of the innovation dynamics, flow and metrics generally culminate in a non-well-thought implementation of innovation processes and policies that are usually tragic in the short term. The most common consequences are the loss of credibility of the innovation process in general among leaders and employees, and the loss of credibility of the company as an innovative company among suppliers, partners and customers, causing these companies to abandon this powerful tool and, as consequence, to limit their capabilities to compete in the future. In order to prevent this from happening, companies that were not built upon innovation will need to grow capability and change cultural priorities to match the demands of the innovation process.

This paper describes the validation of RAMSIS, a 3-D CAD human model for ergonomic vehicle evaluation. At GM NAO, the model’s capability to correctly predict position and posture in vehicle CAD environments was tested. H- and Eye point locations between RAMSIS manikins and their human counterparts were compared. At GM/SAAB the model’s postural discomfort predictability was evaluated. Changes in postural discomfort predictions of the RAMSIS manikins were compared to that of the human subjects when they evaluated two different driving buck conditions. We concluded that RAMSIS has good position, posture and postural discomfort prediction capabilities and is a useful CAD ergonomic evaluation and design tool for vehicle interiors.

This paper describes the validation of RAMSIS, a 3-D CAD human model for ergonomic vehicle evaluation at General Motors (GM). The model’s capability to correctly predict position and posture in vehicle CAD environments was tested. H- and Eye point locations between RAMSIS manikins and their human counterparts were compared. We concluded that RAMSIS has good position and posture prediction capabilities and is a useful CAD ergonomic evaluation and design tool for vehicle interiors.

Reductions of hardware costs as well as implementations of new innovative functions are the main drivers of today's automotive electronics. Indeed more and more resources are spent on adapting existing solutions to different environments. At the same time, due to the increasing number of networked components, a level of complexity has been reached which is difficult to handle using traditional development processes. The automotive industry addresses this problem through a paradigm shift from a hardware-, component-driven to a requirement- and function-driven development process, and a stringent standardization of infrastructure elements. One central standardization initiative is the AUTomotive Open System ARchitecture (AUTOSAR). AUTOSAR was founded in 2003 by major OEMs and Tier1 suppliers and now includes a large number of automotive, electronics, semiconductor, hard- and software companies.

Suppliers and integrators are working with SAE’s HM-1 standards team to develop a mechanism to allow “Health Ready Components” to be integrated into larger systems to enable broader IVHM functionality (reference SAE JA6268). This paper will discuss how the design data provided by the supplier of a component/subsystem can be integrated into a vehicle reference model with emphasis on how each aspect of the model is transmitted to minimize ambiguity. The intent is to enhance support for the analytics, diagnostics and prognostics for the embedded component. In addition, we describe functionality being delegated to other system components and that provided by the supplier via syndicated web services. As a specific example, the paper will describe the JA6268 data submittal for a typical automotive turbocharger and other engine air system components to clarify the data modeling and integration processes.