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2017-04-26 ...
  • April 26-27, 2017 (8:30 a.m. - 4:30 p.m.) - Troy, Michigan
  • October 18-19, 2017 (8:30 a.m. - 4:30 p.m.) - Troy, Michigan
Training / Education Classroom Seminars
Organizations are becoming increasingly aware of the importance of developing secure software. This seminar introduces students to the concepts of software assurance which have direct application in all software industries, including automotive and aerospace sectors. The intent of this seminar is to give students an appreciation of the technical challenges associated with software assurance while developing the technical skills necessary to engineer secure software. Performing input validation is imperative when developing secure software systems as this seminar will demonstrate.
2017-04-24 ...
  • April 24-25, 2017 (8:30 a.m. - 4:30 p.m.) - Troy, Michigan
  • October 16-17, 2017 (8:30 a.m. - 4:30 p.m.) - Troy, Michigan
Training / Education Classroom Seminars
Embedded hardware is everywhere you look today from your vehicle’s infotainment system to refrigerator to medical devices and everything else in-between. With so much exposure one would think that such devices are secure against attack; however, sadly for a large number of devices this is not the case. For proof, just look no further than your local news reports. They are full of reports on devices being hacked into. So, as engineers, how do we go about first identifying and mitigating (or capitalizing) the potential security vulnerabilities within these devices?
2017-04-06
Event
The Mobility History Committee has as its mission to link the lessons of the past to the present and, with such an understanding, to support the platform for future developments.
2017-04-05
Event
Internet of Things joins everything: the customer, the OEMs, the Suppliers, hardware, software, safety, security, networks, storage, cloud and anything else you can think of connected to the internet. There are changes in paradigms, products, business models, sheer volume of data and the speed in which everything works. Learn about the technologies, strategies, integration, standards, products, needs, predictions and societal changes that IoT is driving. Meet the people that make it happen.
2017-04-05
Event
Business Modeling/Operation Research/Big Data Analytics are key enablers for the next wave of innovation and growth across most industries and will address complex issues and systems that involve multiple objectives, alternatives, trade-offs, and large amounts of data and situations involving uncertainty or risk. This session will address new technical advances in these areas and provide valuable insights through the applications of real-world case studies.
2017-04-05
Event
Business Modeling/Operation Research/Big Data Analytics are key enablers for the next wave of innovation and growth across most industries and will address complex issues and systems that involve multiple objectives, alternatives, trade-offs, and large amounts of data and situations involving uncertainty or risk. This session will address new technical advances in these areas and provide valuable insights through the applications of real-world case studies.
2017-04-04
Event
Business Modeling/Operation Research/Big Data Analytics are key enablers for the next wave of innovation and growth across most industries and will address complex issues and systems that involve multiple objectives, alternatives, trade-offs, and large amounts of data and situations involving uncertainty or risk. This session will address new technical advances in these areas and provide valuable insights through the applications of real-world case studies.
2017-04-04
Event
Business Modeling/Operation Research/Big Data Analytics are key enablers for the next wave of innovation and growth across most industries and will address complex issues and systems that involve multiple objectives, alternatives, trade-offs, and large amounts of data and situations involving uncertainty or risk. This session will address new technical advances in these areas and provide valuable insights through the applications of real-world case studies.
2017-04-03 ...
  • April 3-4, 2017 (8:30 a.m. - 4:30 p.m.) - Detroit, Michigan
Training / Education Classroom Seminars
The ability to write concise and unambiguous reports, proposals, manuals, or other technical documents is a key skill for any high-functioning engineer or technical staff person in the mobility industries. Through a combination of class discussions, interactive workshop activities, assignments, checker teams (review teams) and job aids, this course delivers real-life technical writing techniques and tools that can be immediately applied. Attendees discover the importance of knowing their audiences and how to communicate technical information in a "user-friendly" style.
2017-03-28
Technical Paper
2017-01-0894
Nishant Singh
Improving fuel economy has been a key focus across automotive and truck industry for several years if not decades. In heavy duty commercial vehicles, the benefits from small gains in fuel economy lead to significant savings for fleets as well as owners and operators. Additionally, the regulations require vehicles to meet certain GHG levels which closely translate to vehicle fuel economy. For current state of the art FE technologies, incremental gains are so small that they are hard to measure on an actual vehicle. Engineers are challenged with high level of variability to make informed decisions. In such cases, highly controlled tests on Engine and Powertrain dynos are used, however, there is an associated variability even with these tests due factors such as part to part differences, fuel blends and quality, dyno control capabilities and so on.
2017-03-28
Technical Paper
2017-01-0234
Assam Alzookery
In today’s era, real time data tagging is significant for research and development of new automotive technology. The ability to tag and classify data increases operational efficiency in terms of improvements of algorithms and enhancements of sensors used in current and future vehicle technology. Real Time Data Tagging Software (RT-DTS) application was developed to use in conjunction with the data acquisition system. In addition, this application will help the user to tag quickly and efficiently, identify data that are unique and significant enough to undergo extensive analyses to further enhance and improve algorithms and features that correlates to increased safety, which is the paramount goal across the automotive industry. The application currently runs in iOS and can be installed on Apple devices.
2017-03-28
Technical Paper
2017-01-0232
Fazal Syed, Joseph Supina, Hao Ying, Nizar Khemri
Realistic vehicle fuel economy studies require real-world vehicle driving behavior data along with various factors affecting the fuel consumption. Such studies require data with various vehicles usages for prolonged periods of time. A project dedicated to collecting such data is an enormous and costly undertaking. Alternatively, we propose to utilize two publicly available vehicle travel survey data sets. One is Puget Sound Travel Survey collected using GPS devices in 484 vehicles between 2004 and 2006. Over 750,000 trips were recorded with a 10-second time resolution. The data were obtained to study travel behavior changes in response to time-and-location-variable road tolling. The other is Atlanta Regional Commission Travel Survey conducted for a comprehensive study of the demographic and travel behavior characteristics of residents within the study area.
2017-03-28
Technical Paper
2017-01-0238
Velappan Shalini, Sridharan Krishnamurthy, Srinivasan Narasimhan
Automobile industry has been undergoing key transformations recently. There are very many complexities arise with respect to these transformations. The automobile data and its exponential growth along with its high dimensionality issues, distributional patterns apart from the normal distribution and its sparse attributes added to its complexities. These complexities possess a great challenge when it comes to dealing with flood of personalized automobile data available about the customer preferences, as well as general business and economic data in making informed decisions. On one hand, the science of machine learning comprises of a family of models has been known to have very many applications such as detection, classification and prediction amongst many. The application of the advanced machine learning techniques helps to leverage the existing automobile data in informed decision making.
2017-03-28
Technical Paper
2017-01-0235
Qiuming Gong, Jimmy Kapadia
Plug-in hybrid electric vehicles (PHEV) have an EV mode driving range which can cover a portion of customer daily driving. This EV mode range affects the refuel frequency substantially compared with conventional vehicle. For a conventional vehicle, generally the distance between fuel fill up is dependent on tank size and fuel economy, while for PHEVs, distance between fuel fill up is dependent on tank size, fuel economy, and EV driving range. Consequently, the EPA label range does not accurately represent real world driving range between fill-ups for PHEV. Furthermore, for PHEVs, the dependency on EV driving range varies greatly on customer trip lengths. Hence, it becomes critical to use real world customer usage pattern to estimate distance between fill up and size the fuel tank accordingly. This paper describes a methodology to use real world customer data to estimate distance between refueling for PHEVs.
2017-03-28
Journal Article
2017-01-0241
Thiago B. Murari, Paulo Ungaretti, Marcelo A. Moret
Geometric Dimensioning and Tolerancing is used to describe the allowed element variations regarding the product design. The product development engineering need to define the symbols to use on the Feature Control Frame of every component. Since the component function has an increment on its complexity year over year, it is not trivial to define these symbols anymore. Poor specifications can increase the production cost, require late product changes or lead to legal issues. In this scenario, we developed a method to classify component features and analyze the knowledge of automotive experts using fuzzy logic. Also, the method suggests the best set of symbols based on the feature geometry, assembly and function analysis.
2017-03-28
Journal Article
2017-01-0245
Kanna Akella, N. Venkatachalam, K. Gokul, Keunho Choi, Ramachandraprabhu Tyakal
Ford Motor company captures voice of customer through multiple connect points like surveys, warranty claims, social media, and so on. Data from voice of customer is used as Customer Advocacy to influence actions in manufacturing, customer service, marketing, and product development, thus enhancing customer experience. One of the challenges in this Customer advocacy project is to categorize natural language textual data into company-standard customer concern codes for assessing sentiments. These concern codes are categorized into focused function areas in the organization aimed at improving performance and customer experience. In this paper, we discuss some approaches towards addressing this challenge. In this work, verbatim inputs from transactional systems in quality office, warranty claims, issues matrix, customer surveys, and social media content are used. Due to free format and diverse sources, these textual comments pose challenges with content, language, and abbreviations.
2017-03-28
Technical Paper
2017-01-1141
Bashar Alzuwayer, Robert Prucka, Imtiaz Haque, Paul Venhovens
Abstract Fuel economy regulations have forced the automotive industry to implement transmissions with an increased number of gears and reduced parasitic losses. The objective of this research is to develop a high fidelity and a computationally efficient model of an automatic transmission, this model should be suitable for controller development purposes. The transmission under investigation features a combination of positive clutches (interlocking dog clutches) and conventional wet clutches. Simulation models for the torque converter, lock-up clutch, transmission gear train, interlocking dog clutches, wet clutches, hydraulic control valves and circuits were developed and integrated with a 1-D vehicle road load model. The integrated powertrain system model was calibrated using measurements from real-world driving conditions. Unknown model parameters, such as clutch pack clearances, compliances, hydraulic orifice diameters and clutch preloads were estimated and calibrated.
2017-03-28
Journal Article
2017-01-0237
Jonas Biteus, Tony Lindgren
Maintenance planning of trucks at Scania have previously been done using static cyclic plans with fixed sets of maintenance tasks, determined by mileage, calendar time, and some data driven physical models. Flexible maintenance have improved the maintenance program with the addition of general data driven expert rules and the ability to move sub-sets of maintenance tasks between maintenance occasions. Meanwhile, successful modelling with machine learning on big data, automatic planning using constraint programming, and route optimization are hinting on the ability to achieve even higher fleet utilization by further improvements of the flexible maintenance. The maintenance program have therefore been partitioned into its smallest parts and formulated as individual constraint rules. The overall goal is to maximize the utilization of a fleet, i.e. maximize the ability to perform transport assignments, with respect to maintenance.
2017-03-28
Technical Paper
2017-01-1651
Douglas Thornburg, John Schmotzer, MJ Throop
Onboard, embedded cellular modems are enabling a range of new connectivity features in vehicles and transmission of a rich, real-time data set from a vehicle’s internal network up to a cloud database is of particular interest. However, there is far too much information in a vehicle’s electrical state for every vehicle to upload all of its data in real-time. We are thus concerned with which data is uploaded and how that data is processed, structured, stored, and reported. Existing onboard data processing algorithms (e.g. for DTC detection) are hard-coded into critical vehicle firmware, limited in scope and cannot be reconfigured on the fly. Since many use cases for vehicle data analytics are still unknown, we require a system which is capable of efficiently processing and reporting vehicle deep data in real-time, such that data reporting can be switched on/off during normal vehicle operation, and that processing/reporting can be reconfigured remotely.
2017-03-28
Technical Paper
2017-01-1484
Giampiero Mastinu, Mario Pennati, Massimiliano Gobbi, Giorgio Previati, Federico Ballo
Evolution of the ride comfort of Alfa Romeo Cars since 1955 until 2005. The ride comfort of three Alfa Romeo cars, namely Giulietta (1955), Alfetta (1972) and 159 (2005) has been assessed both objectively and subjectively. The three cars belong to the same market segment. The aim is to let young engineers or graduated students understand how technology has evolved and eventually learn a lesson from the assessed trend. A number o cleats have been fixed at the ground and the three cars have traversed such uneven surface. The objective assessment of the ride comfort has been performed by means of accelerometers fixed at the seat rails, additioanlly a special dummy developed at Politecnico di Milano has been employed. The subjective assessment has been perfomed by a panel of passengers. The match between objective and subjective ratings is very good. Simple mathematical models have been employed to establish a (successful) comparison between experimental and computational results.
2017-03-28
Technical Paper
2017-01-1625
Rajeev Kalamdani, Chandra Jalluri, Stephen Hermiller, Robert Clifton
Use of sensors to monitor dynamic performance of machine tools at Ford's powertrain machining plants has proven to be effective. The traditional approach to convert sensor data to actionable intelligence consists of identifying single features from cycle based signatures and setting thresholds above acceptable performance limits based on trials. The thresholds are used to discriminate between acceptable and unacceptable performance during each cycle and raise alarms if necessary. This approach requires a significant amount of resource & time intensive set up work up-front and considerable trial and error adjustments. The current state does not leverage patterns that might be discernible using multiple features simultaneously. This paper describes enhanced methods for processing the data using supervised and unsupervised machine learning methods. The objective of using these methods is to improve the prediction accuracy and reduce up-front set up.
2017-03-28
Journal Article
2017-01-0236
Zhigang Wei, Kamran Nikbin
In the Big Data era, the capability in statistical and probabilistic data characterization, data pattern identification, data modeling and analysis is critical to understand the data, to find the trends in the data, and to make better use of the data. In this paper the fundamental probability concepts and several commonly used probabilistic distribution functions, such as the Weibull for spectrum events and the Pareto for extreme/rare events, are described first. An event quadrant is subsequently established based on the commonality/rarity and impact/effect of the probabilistic events. Level of measurement, which is the key for quantitative measurement of the data, is also discussed based on the framework of probability. The damage density function, which is a measure of the relative damage contribution of each constituent is proposed. The new measure demonstrates its capability in distinguishing between the extreme/rare events and the spectrum events.
2017-03-01
Book
Jay Meldrum
This collection is a resource for studying the history of the evolving technologies that have contributed to snowmobiles becoming cleaner and quieter machines. Papers address design for a snowmobile using the EPA test procedure and standard for off-road vehicles, along with more stringent U.S. National Park Best Available Technology (BAT) standards that are likened to those of the California Air Resourced Board (CARB). Innovative technology solutions include: • Standard application for diesel engine designs • Applications to address and test both engine and track noise • Benefits of the Miller cycle and turbocharging The SAE International Clean Snowmobile Challenge (CSC) program is an engineering design competition. The program provides undergraduate and graduate students the opportunity to enhance their engineering design and project management skills by reengineering a snowmobile to reduce emissions and noise.
Viewing 1 to 30 of 6297

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