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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?
2017-04-05
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.
2017-04-04
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 objective, many alternatives, trade-offs between competing effects, 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
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.
2017-04-03 ...
  • 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
2017-01-0242
Yakov Fradkin, Michel Cordonnier, Andrew Henry, David Newton
Ford Motor Company’s assembly plants build vehicles in a certain sequence. The planned sequence for the plant’s trim and final assembly area is developed centrally and is sent to the plant several days in advance. In this talk we present the study of two cases where the plant changes the planned sequence to cope with production constraints. In one case, a plant pulls ahead two-tone orders that require two passes through the paint shop. This is further complicated by presence in the body shop area of a unidirectional rotating tool that allows efficient build of a sequence “A-B-C” but heavily penalizes a sequence “C-B-A”. The plant changes the original planned sequence in the body shop area to the one that satisfies both pull-ahead and rotating tool requirements. In the other case, a plant runs on lean inventories. Material consumption is tightly controlled down to the hour to match with planned material deliveries.
2017-03-28
Technical Paper
2017-01-0243
Zhenghui Sha, Veronica Saeger, Mingxian Wang, Yan Fu, Wei Chen
For achieving viable mass customization of products, product configuration is often performed that requires deep understanding on the impact of product features and feature combinations on customers’ purchasing behaviors. Existing literature has been traditionally focused on analyzing the impact of common customer demographics and engineering attributes with discrete choice modeling approaches. This paper aims to expand discrete choice modeling through the incorporation of optional product features, such as customers’ positive or negative comments and their satisfaction ratings of their purchased products, beyond those commonly used attributes. The paper utilizes vehicle as an example to highlight the range of optional features currently underutilized in existing models. First, data analysis techniques are used to identify areas of particular consumer interest in regards to vehicle selection. 
2017-03-28
Technical Paper
2017-01-0246
Sentao Miao, Xiuli Chao, Michael Tamor, Yan Fu, Margaret Strumolo
Most of the greenhouse gas (GHG) emissions in the United States come from the transportation and electricity generation sectors. In this paper, we analyzed the possibility of cross-sector cooperation to cost-efficiently reduce these emissions. Specifically, we built a bi-level optimization model with renewable energy certificate (REC) purchasing to evaluate the effectiveness of the REC purchasing policy. This policy allows the transportation sector to purchase RECs, which are created by renewable generators built by the electricity generation sector, in order to gain extra emission allowance. We conclude from simulations that REC purchasing policy helps to lower the total cost to society while reducing GHG emissions significantly. Simulation results also show that REC purchasing policy can create electricity capacity beyond demand, which can potentially be used to make clean fuel and further cut emissions from existing fossil fuel powered vehicles.
2017-03-28
Technical Paper
2017-01-0247
N. Khalid Ahmed, Jimmy Kapadia
Electrified vehicles including Battery Electric Vehicles (BEVs) and Plug-In Hybrid Vehicles (PHEVs) made by Ford Motor Company are fitted with a telematics modem to provide customers with the means to communicate with their vehicles and, at the same time, receive insight on their vehicle usage. These services are provided through the “MyFord Mobile” website and phone applications, simultaneously collecting information from the vehicle for different event triggers. In this work, we study this data by using Big Data Methodologies including a Hadoop Database for storing data and HiveQL and Pig Latin scripts to perform analytics. We present electrified vehicle customer behaviors including geographical distribution, trip distances, daily distances and annual usage factor. We also compare temperature distribution of trips with the EPA-MOVES database. We discuss the process of extracting information from this data that can be used to further refine future design.
2017-03-28
Technical Paper
2017-01-0236
Zhigang Wei
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.
2017-03-28
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
2017-01-0237
Jonas Biteus, Tony Lindgren
Maintenance planning of trucks at Scania have previously been done using static cyclic plans with fixed sets of maintenance points, determined by mileage, calendar time, and some data driven physical models. Flexible or condition based maintenance have improved the maintenance program with the addition of general data driven expert rules and the ability to move sub-sets of maintenance points between maintenance occasions. Meanwhile, successful modelling with machine learning on big data and automatic planning using constraint programming 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. One maintenance point for a component have been selected and operational data have been collected showing how each truck have been used over its lifetime and if it have had a repair of the component or not.
2017-03-28
Technical Paper
2017-01-0233
Weihong Guo, Shenghan Guo, Hui Wang, Xiao Yu, Annette Januszczak, Saumuy Suriano
The wide applications of automatic sensing devices and data acquisition systems in automotive manufacturing have resulted in a data-rich environment, which demands new data mining methodologies for effective data fusion and information integration to support decision-making. This paper presents a new methodology for developing a diagnostic system using manufacturing system data for high-value assets in automotive manufacturing. The key issues studied in this paper include optimal feature extraction using descriptive analysis, optimal feature subset selection using statistical hypothesis testing, machine fault prediction using multivariate process control chart, and diagnostic performance assessment using process trend detection. The performance of the developed diagnostic system can be continuously improved as the knowledge of machine faults is automatically accumulated during production.
2017-03-28
Technical Paper
2017-01-0249
Jia Mi
With the development of the Internet for vehicles, the Car-sharing has been developed rapidly in recent years. This paper focuses on the network programming and distribution for Car-sharing, which helps to clarify the characteristics and basic law of Car-sharing network development, as well as the main approaches to construct it. Firstly, by analyzing the expanding ways of Car-sharing network, characteristics of the development of Car-sharing industry and its network, as well as main Car-sharing users and services, the influence factors of Car-sharing demand and the main demand points in a city are summarized. Secondly, in order to better evaluate the network programming and distribution for Car-sharing, this paper proposes an optimization decision method of the car-sharing network planning by evaluating the possible alternatives in a same scale. The assessment index of Car-sharing network planning is constructed.
2017-03-28
Technical Paper
2017-01-0240
Yanli Zhao, Hao Zhou, Yimin Liu
Ride Hailing service and Dynamic Shuttle is one of key smart mobility practices, which provide on-demand door-to-door ride-sharing service to customers through smart phone apps. On the other hand, some big companies spend millions of dollars yearly in third party vendors to offer employee shuttle services to pick up and drop off employees from designated locations and provide daily commutes for employees to and from work. Efficient routing algorithms and analytics are the key ingredients for operation efficiency behind these commercial services. They can significantly reduce operation costs by shortening bus routes and reducing bus number, while maintaining given the same quality of service. The study will develop an off-line optimization routing methods for employee shuttle services in some regions. First, based on the historical demand data collected from a factory in Thailand, we develop a constraint programming model to compute the optimal routes and number of shuttles needed.
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
2017-01-0244
Joshua Lyon, Junheung Park, Yakov Fradkin, Jeff Tornabene
This paper describes a tool developed by Ford Motor Company to help business analysts revise sourcing plans when business conditions change. A common scenario is demand increasing beyond installed capacity – how should the business respond? Likewise, how should production change when demand is lower than expected? Sometimes the company can move production to different locations or outsource parts in order to reduce costs. This paper focuses on making such decisions for stamped sheet-metal parts. We describe an optimization tool used to periodically reassess where to stamp parts. The tool uses mathematical optimization to balance logistic and outsourcing costs. An important component is the user interface, which allows stamping experts to adjust the model in real time to reflect different constraints and competing objectives. This allows the algorithm to efficiently seek alternative solutions while the business expert guides for nuances that may be hard to represent mathematically.
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
2017-01-0241
Thiago B. Murari, Paulo Ungaretti, Marcelo A. Moret PhD
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
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
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
2017-01-0235
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.
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