Dearborn, MI, United States

Ford Motor Company

www.ford.com
Dearborn, MI, United States

The Ford Motor Company is an American multinational automaker headquartered in Dearborn, Michigan, a suburb of Detroit. It was founded by Henry Ford and incorporated on June 16, 1903. The company sells automobiles and commercial vehicles under the Ford brand and most luxury cars under the Lincoln brand. Ford also owns Brazilian SUV manufacturer, Troller, and Australian performance car manufacturer FPV. In the past it has also produced tractors and automotive components. Ford owns a 2.1% stake in Mazda of Japan, an 8% stake in Aston Martin of the United Kingdom, and a 49% stake in Jiangling of China. It also has a number of joint-ventures, two in China , one in Thailand , one in Turkey , and one in Russia . It is listed on the New York Stock Exchange and is controlled by the Ford family, although they have minority ownership. It is described by Forbes as "the most important industrial company in the history of the United States."Ford introduced methods for large-scale manufacturing of cars and large-scale management of an industrial workforce using elaborately engineered manufacturing sequences typified by moving assembly lines; by 1914 these methods were known around the world as Fordism. Ford's former UK subsidiaries Jaguar and Land Rover, acquired in 1989 and 2000 respectively, were sold to Tata Motors in March 2008. Ford owned the Swedish automaker Volvo from 1999 to 2010. In 2011, Ford discontinued the Mercury brand, under which it had marketed entry-level luxury cars in the United States, Canada, Mexico, and the Middle East since 1938.Ford is the second-largest U.S.-based automaker and the fifth-largest in the world based on 2010 vehicle sales. At the end of 2010, Ford was the fifth largest automaker in Europe. Ford is the eighth-ranked overall American-based company in the 2010 Fortune 500 list, based on global revenues in 2009 of $118.3 billion. In 2008, Ford produced 5.532 million automobiles and employed about 213,000 employees at around 90 plants and facilities worldwide.The company went public in 1956 but the Ford family, through special Class B shares, still retain 40 percent voting rights. Wikipedia.

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Filho A.C.C.N.,Ford Motor Company
SAE Technical Papers | Year: 2015

In the design of automotive structural components is common scaling of the data for the "worst case", i.e. a condition of the component of least resistance (stress) and maximum load conditions applied (strength). However, in a real situation, it is not possible to determine with absolute certainty these amounts due to the random nature of the parameters involved. Thus, this design should be treated in a probabilistic manner, where the parameters involved could be considered as random variables, and the project could be qualified for a desired condition of reliability. This paper presents a proposed process (flowchart) for performing computational experiments for reliability analysis in automotive structural components regarding stochastic conditions of involved parameters. The process showed itself as able to identify the most adequate method of predicting reliability to solve problems of stress -strength interference in a design of structural automotive component. Copyright © 2015 SAE International.

Document Keywords (matching the query): automobile manufacture, reliability analysis, structural reliability analysis, automotive structural component, automotive component.


Verster J.C.,University Utrecht | Roth T.,Ford Motor Company
Psychopharmacology | Year: 2012

Rationale There are various methods to examine driving ability. Comparisons between these methods and their relationship with actual on-road driving is often not determined. Objective The objective of this study was to determine whether laboratory tests measuring driving-related skills could adequately predict on-the-road driving performance during normal traffic. Methods Ninety-six healthy volunteers performed a standardized on-the-road driving test. Subjects were instructed to drive with a constant speed and steady lateral position within the right traffic lane. Standard deviation of lateral position (SDLP), i.e., the weaving of the car, was determined. The subjects also performed a psychometric test battery including the DSST, Sternberg memory scanning test, a tracking test, and a divided attention test. Difference scores from placebo for parameters of the psychometric tests and SDLP were computed and correlated with each other. A stepwise linear regression analysis determined the predictive validity of the laboratory test battery to SDLP. Results Stepwise regression analyses revealed that the combination of five parameters, hard tracking, tracking and reaction time of the divided attention test, and reaction time and percentage of errors of the Sternberg memory scanning test, together had a predictive validity of 33.4%. Conclusion The psychometric tests in this test battery showed insufficient predictive validity to replace the on-the-road driving test during normal traffic. © 2011 Springer-Verlag.

Document Keywords (matching the query): predictive validity, automobile driving, predictive value of tests.


Eroglu S.,Ford Motor Company | Duman I.,Product Development Engineering | Ergenc A.,Yildiz Technical University | Yanarocak Ri.,Design Verification and Test Engineer
SAE Technical Papers | Year: 2016

The exhaust manifold bridges the gap between the engine structure and the hot-end after-treatment system, the burned in-cylinder gases are disposed through the manifold. The automotive exhaust manifolds are designed and developed for providing a smooth flow with low/least back pressure and must be able to withstand extreme heating under very high temperatures and cooling under low temperatures. The paper presents a theoretical study aiming to investigate the feasibility of three different CAE approaches and techniques used for the simulation of exhaust manifold fluid flow and the accompanying thermal distribution. The main difficulty emanates from the pulsating nature of fluid flow inside the engine exhaust manifold. To verify the outcome of each solution experimental measurements of the manifold temperatures have been performed. The predicted metal temperature is then used to carry out the thermo-structure durability analysis to evaluate the design of HD diesel engine exhaust manifold which is out of the scope of this study. © 2016 SAE International.

Document Keywords (matching the query): automotive exhaust, thermoanalysis, automobile engine manifolds.


Eroglu S.,Ford Motor Company | Duman I.,Product Development Engineering | Guzel A.H.,Ford Motor Company | Yilmaz R.,Ford Motor Company
SAE Technical Papers | Year: 2016

The exhaust manifold is one of the engine components which is used to collect the burned gases from the cylinder head and send it to the exhaust hot end aftertreatment system with low engine backpressure. The main purpose of the automotive exhaust manifolds are providing a smooth flow field and must be able to endure thermo-mechanical loadings. The present paper explains the CAE analysis method to assess the design of exhaust manifold of a heavy duty diesel engine. Coupled computational fluid dynamics (CFD) analyses were performed to solve the flow field within the exhaust system and surface convection loading prediction at fluid side and obtain temperature distribution at solid region of exhaust manifold domain. The metal temperature prediction provided by thermal model is used to carry out the durability analysis of the structure. A transient nonlinear Finite Element Analysis (FEA) is undertaken to simulate the inelastic deformation and predict thermo-mechanical fatigue (TMF) failure. Gasket sealing prediction is another analysis concern which is driven by FEA in exhaust manifolds since any gas leakage affects the engine performance. The use of such CAE approach allows the design and analyses engineers to diagnose critical locations or to find the root cause of manifold failures in an early stage of development and to meet measures in order to remove local structural weaknesses. This minimizes the need for expensive hardware testing, also reducing the overall product development cycle time and cost. © Copyright 2016 SAE International.

Document Keywords (matching the query): computational fluid dynamics analysis, nonlinear finite element analyses fea, automobile engine manifolds.


Ng J.C.,University of Michigan | Luckey S.G.,Ford Motor Company | Kridli G.T.,University of Michigan | Friedman P.A.,Ford Motor Company
Journal of Materials Processing Technology | Year: 2011

The automotive industry has recently begun using the superplastic forming (SPF) process to fabricate complex aluminum and magnesium alloy panels that cannot be formed at room temperature due to insufficient formability. One of the manufacturing problems encountered during SPF is excessive thinning in the form of a localized neck; which can lead to fracture. Localized necking can be predicted with the use of continuum elements in finite element analysis (FEA); however, the use of these elements in simulating SPF of large automotive panels is computationally intensive and often computationally prohibitive due to convergence issues. This paper examines the use of a modified material model (developed by engineers at Livermore Software Technology Corporation (LSTC) that can be used with conventional Belytschko-Tsay shell elements. This model considers normal stresses during SPF, which is needed to predict necking locations. The paper reports the results on investigating means for improving computational efficiency with this new formulation (i.e. element size, mass scaling, and adaptive meshing) and compares the performance of the normal stress element formulation with that of Belytschko-Tsay shell element in simulating the SPF process. The findings indicate that the newly developed formulation can be used for predicting localized thinning under SPF conditions. © 2011 Elsevier B.V. All rights reserved.

Document Keywords (matching the query): automotive industry, automotive panel, predictive accuracy, finite element analysis.


Hou H.,Ford Motor Company
SAE Technical Papers | Year: 2014

When a window opens to provide the occupant with fresh air flow while driving, wind throb problems may develop along with it. This work focuses on an analytical approach to address the wind throb issue for passenger vehicles when a front window or sunroof is open. The first case of this paper pertains to the front window throb issue for the current Ford Escape. Early in a program stage, CAA (Computational Aeroacoustics) analysis predicted that the wind throb level exceeded the program wind throb target. When a prototype vehicle became available, the wind tunnel test confirmed the much earlier analytical result. In an attempt to resolve this issue, the efforts focused on a design proposal to implement a wind spoiler on the side mirror sail, with the spoiler dimension only 6 millimeters in height. This work showed that the full vehicle CAA analysis could capture the impact of this tiny geometry variation on the wind throb level inside the vehicle cabin. The independent wind tunnel effort came to the same conclusion, and the difference between the analysis and testing is only about 1 dB. With the implementation of this spoiler, the program target was finally met. The second case of this paper deals with the sunroof throb issue for an SUV. The work concentrates on the modeling method of wind deflectors made of meshed fabric material and carrying out CAA analysis to access the sunroof wind throb level. The result shows that CAA can predict very well the impact of the wind deflector made of meshed fabric material on the wind throb level, in line with the subjective evaluation on proving ground. In summary, this work manifests that CAA is a very effective tool for wind throb prevention design when hardware prototypes are not available. Copyright © 2014 SAE International.

Document Keywords (matching the query): analysis and testing.


Karim A.,Ford Motor Company | Miazgowicz K.,Ford Motor Company | Lizotte B.,Ford Motor Company
SAE Technical Papers | Year: 2015

The stable operation of turbocharger compressor at low flow rates is important to provide low end engine torque for turbocharged automotive engines. Therefore, it is important to be able to predict the lowest flow rates at different turbocharger speeds at which the surge phenomenon occurs. For this purpose, three-dimensional Computational Fluid Dynamics (CFD) simulations were performed on the turbocharger compressor including the entire compressor wheel and volute. The wheel consisted of six main and six splitter blades. Historically, flow bench and engine testing has been used to detect surge phenomenon. However a complete 3D CFD analysis can be performed upfront in the design to calculate low end compressor surge performance. The analyses will help understand the fundamental mechanisms of stalled flow, the surge phenomenon, and impact of compressor inlet conditions on surge. To reduce computational time a small volume of compressed air system, essentially a small 'B' system as introduced by Greitzer [1], is used in the CFD model. CFD results have been compared with gas stand compressor maps and have good agreements for the compressor map points. This paper presents a CFD analysis near the low flow region at constant turbo speed to predict automotive centrifugal compressor surge phenomenon. Copyright © 2015 SAE International.


Patent
Ford Motor Company | Date: 2014-07-16

This disclosure generally relates to an automotive drone deployment system that includes at least a vehicle and a deployable drone that is configured to attach and detach from the vehicle. More specifically, the disclosure describes the vehicle and drone remaining in communication with each other to exchange information while the vehicle is being operated in an autonomous driving mode so that the vehicles performance under the autonomous driving mode is enhanced.


This disclosure generally relates to a system, apparatus, and method for achieving a vehicle state-based hands free noise reduction feature. A noise reduction tool is provided for applying a noise reduction strategy on a sound input that uses machine learning to develop future noise reduction strategies, where the noise reduction strategies include analyzing vehicle operational state information and external information that are predicted to contribute to cabin noise and selecting noise reducing pre-filter options based on the analysis. The machine learning may further be supplemented by off-line training to generate a speech quality performance measure for the sound input that may be referenced by the noise reduction tool for further noise reduction strategies.

Claims which contain your search:

17. The method of claim 16, wherein the vehicle operational state information includes at least one of engine speed information, throttle position information, HVAC mode information, HVAC blower speed information, vehicle speed information, turn signal operational state information, wiper operational state information, car audio volume state information, window position information, spindle acceleration information, cabin acoustics information, cabin microphone position information, and/or seat position information for one or more seats within the vehicle.

2. The apparatus of claim 1, wherein the processor is configured to control the noise reduction on the sound input by: receiving the sound input; receiving training input data; analyzing the training input data in view of the pre-filter selection strategy; determining whether to select the pre-filter based on the analysis, and applying the pre-filter to the sound input when the pre-filter is selected.

12. The method of claim 11, wherein controlling the noise reduction on the sound input comprises: receiving, by the processor, the sound input; receiving, by the processor, training input data; analyzing, by the processor, the training input data in view of the pre-filter selection strategy; determining, by the processor, whether to select the pre-filter based on the analysis, and applying, by the processor, the pre-filter to the sound input when the pre-filter is selected.

7. The apparatus of claim 6, wherein the vehicle operational state information includes at least one of engine speed information, throttle position information, HVAC mode information, HVAC blower speed information, vehicle speed information, turn signal operational state information, wiper operational state information, car audio volume state information, window position information, spindle acceleration information, cabin acoustics information, cabin microphone position information, and/or seat position information for one or more seats within the vehicle.


This disclosure generally relates to a system, apparatus, and method for achieving an adaptive vehicle state-based hands free noise reduction feature. A noise reduction tool is provided for adaptively applying a noise reduction strategy on a sound input that uses feedback speech quality measures and machine learning to develop future noise reduction strategies, where the noise reduction strategies include analyzing vehicle operational state information and external information that are predicted to contribute to cabin noise and selecting noise reducing pre-filter options based on the analysis.

Claims which contain your search:

17. The method of claim 16, wherein the vehicle operational state information includes at least one of engine speed information, throttle position information, HVAC mode information, HVAC blower speed information, vehicle speed information, turn signal operational state information, wiper operational state information, car audio volume state information, window position information, spindle acceleration information, cabin acoustics information, cabin microphone position information, and/or seat position information for one or more seats within the vehicle.

7. The apparatus of claim 6, wherein the vehicle operational state information includes at least one of engine speed information, throttle position information, HVAC mode information, HVAC blower speed information, vehicle speed information, turn signal operational state information, wiper operational state information, car audio volume state information, window position information, spindle acceleration information, cabin acoustics information, cabin microphone position information, and/or seat position information for one or more seats within the vehicle.

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