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The AutoDrive Challenge competition sponsored by General Motors and SAE gives undergraduate and graduate students an opportunity to get hands-on experience with autonomous vehicle technology and development as they work towards their degree. Michigan Technological University has participated in the AutoDrive Challenge since its inception in 2017 with students participating through MTU’s Robotic System Enterprise. The MathWorks Simulation Challenge has been a component of the competition since its second year, tasking students with the development of perception, control and testing algorithms using MathWorks software products. This paper presents the pedagogical approach graduate student mentors used to enable students to build their understanding of autonomous vehicle concepts using familiar tools. This approach gives undergraduate students a productive experience with these systems that they may not have encountered in coursework within their academic program.

Auto industry has faced constant challenges in the economic, technology and global trend in the recent years. This is changing the corporative mindset to find creative and innovative processes and methods to evolve the product development system to adjust and deliver competitive products that satisfy customers expectations. Integrating the work from different teams in an organization has been moving from simple roles and responsibilities definition with effective communication channels to a new vision where teamwork progresses in harmony and embraces change to satisfy customers as part of the process. The path to evolve work in engineering that relies on several computational tools continues. In this article, it is presented an integration of different tools to manage vehicle program changes using model-based systems engineering, the present work improves the reaction capabilities of the teams and enables to adjust to changes in the development of a vehicle.

Squeak and rattle are two undesirable occurrences during component operation and during vehicle driving condition, resulting in one of the top complains from costumers. One common grievance could happen during the user exterior handle operation and during side door closing. The exterior handle system during the operation could generate a squeak between interface parts, if materials and geometric tolerances was not been carefully designed. Also, vibration generated during door closing effort, might generate squeak between parts since the reinforcement for exterior handle touches the outer sheet metal internally. For this reason several guidelines might be included to avoid potential noise condition for this system during vehicle lifetime as correct material reduce friction between parts, taking into consideration the geometric condition between parts. Plus, coupling system on handles two pieces should also be evaluated to avoid squeak during use.

Real-world second-by-second vehicle driving cycle data is very important for vehicle research and development. A project solely dedicated to generating such information would be tremendously costly and time consuming. Alternatively, we developed such a database by utilizing two publicly available passenger vehicle travel surveys: 2004-2006 Puget Sound Regional Council (PSRC) Travel Survey and 2011 Atlanta Regional Commission (ARC) Travel Survey. The surveys complement each other - the former is in low time resolution but covers driver operation for over one year whereas the latter is in high time resolution but represents only one-week-long driving operation. After analyzing the PSRC survey, we chose 382 vehicles, each of which continuously operated for one year, and matched their trips to all the ARC trips. The matching is carried out based on trip distance first, then on average speed, and finally on duration.

Sound intensity measurement techniques that used a two-microphone configuration, were first developed in the late 1970s. Originally, the focus was on improving precision during testing or post-processing. However, with the advent of modern, sophisticated equipment, the focus has shifted to the apparatus. Availability of phase-matched microphones has made post-test correction obsolete as the microphones eliminate a majority of the errors before the data is even collected. This accuracy, however, comes at a cost, as phase-matched microphones are highly priced. This paper discusses employing the method of improving post-processing precision, using inexpensive, current equipment. The phase error of the system is corrected using a simple calibration technique and a handheld phase calibrator that is similar to the one used for amplitude calibration of microphones.

The use of finite element modeling (FEM) tools is part of the most of the current product development projects of the automotive industry companies, replacing an important part of the physical tests with lower costs, higher speed and with increasing accuracy by each day. In addition to this, computer-aided engineering (CAE) tools can be either used after the product is released, at any moment of the product life, in many different situation as a new feature release, to validate a more cost-efficient design proposal or to help on solving some manufacturing problem or even a vehicular field issue. Different from the phase where the product is still under development, when standard virtual test procedures are performed in order to validate the vehicle project, in this case, where engineers expertise plays a very important role, before to proceed with any standard test it is fundamental to understand the physics of the phenomena that is causing the unexpected behavior.

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 work 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.

This paper focus on the design approach of mapping the equivalent bead to the physical bead geometry. In principle, the physical character and geometry of equivalent bead is represented as restraining force (N/mm) and a line (bead center line). During draw development, the iterations are performed to conclude the combination of restraining force that obtains the desired strain state of a given panel. The objective of physical bead design to determine a bead geometry that has the capacity to generate the same force as specified in 2D plane strain condition. The software package ABAQUS/CAE/Isight with python script is utilized as primary tool in this study. In the approach, the bead geometry is sketched and parameterized in ABAQUS/CAE and optimized with Isight to finalize the bead geometry.

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.

Modern vehicles become increasingly software intensive. Software development therefore is critical to the success of the manufacturer to develop state of the art technology. Standards like ISO 26262 recommend requirement-based verification and test cases that are derived from requirements analysis. Agile development uses continuous integration tests which rely on test automation and evaluation. All these drove the development of a new model-based software verification environment. Various aspects had to be taken into account: the test case specification needs to be easily comprehensible and flexible in order to allow testing of different functional variants. The test environment should support different use cases like open-loop or closed-loop testing and has to provide corresponding evaluation methods for continuously changing as well as for discrete signals.

Injection molding tools are expensive and the fatigue failure during production would result in very costly rework on the tool and downtime. Currently, mold designs are mostly based on expert experience without a careful stress analysis and the mold tool life cycle relies largely on rough estimates. The industry state of the art applies averaged temperature change and peak pressure load on the mold tool. The static analysis is then performed. Mold temperature history and thermal shock are not considered in the durability analysis. In this paper, a transient thermal analysis of the tool is performed in conjunction with the injection molding process simulation. The spatial and temporal variation of temperature, pressure and clamping forces are exported from Moldflow simulation. These histories of temperature and pressure are converted to appropriate loading curves and mapped into Abaqus FEA model.

This paper highlights and discusses the development and deployment of an enterprise-level tool infrastructure that fits into the production build environment of Ford Motor Company. A particular focus is on navigating bottlenecks and pitfalls that arise with the adoption of a model-based software development process. This includes provisions to support centralized data and architecture artifact management (including version control across the lifecycle of the software), support to integrate and manage legacy software artifacts, support to archive and bookshelf development milestones, and last but not least, built-in intelligence to spot potential sources of software defects early in the development stage.

The die wear up to 80,800 hits on a prog-die setup for bare DP1180 steel was investigated in real production condition. In total, 31 die inserts with the combination of 11 die materials and 9 coatings were evaluated. The analytical results of die service life for each insert were provided by examining the evolution of surface wear on inserts and formed parts. The moments of appearance of die defects, propagation of die defects, and catastrophic failure were determined. Moreover, the surface roughness of the formed parts for each die insert was characterized using Wyko NT110 machine. The objectives of the current study are to evaluate the die durability of various tooling materials and coatings for flange operations on bare DP 1180 steel and update OEM tooling standards based on the experimental results. The current study provides the guidance for the die material and coating selections in large volume production for next generation AHSSs.

The air suspension development and application has becoming increasingly applied also in commercial vehicles, offering to the driver more dynamic comfort as well as contributing to the reduction of impact loads on highways. Through this project pursuit show the analysis and application of an air suspension system for commercial tractor vehicles application. A special focus was given to pneumatic actuation system, responsible for leveling and control of suspension′s stiffness under different conditions of usage, laden and unladen. The project was conducted starting with the vehicle dynamic performance analysis, evaluating the pneumatic suspension circuit modifications in order to obtain the vehicle dynamic behavior improvement, ensuring directional stability under different maneuvering conditions. For entire development were also used quality tools, considering the possible failure modes and effects as well as virtual simulation tools (Adams) and bench validations.

Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

The United States Department of Transportation (USDOT) and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications 2 (CAMP-VSC2) Consortium (Ford, General Motors, Honda, Mercedes-Benz, and Toyota) initiated, in December 2006, a three-year collaborative effort in the area of wireless-based safety applications under the Vehicle Safety Communications-Applications (VSC-A) Project. The VSC-A Project developed and tested Vehicle-to-Vehicle (V2V) communications-based safety systems to determine if Dedicated Short Range Communications (DSRC) at 5.9 GHz, in combination with vehicle positioning, would improve upon autonomous vehicle-based safety systems and/or enable new communications-based safety applications.

Custom functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. Typical examples include mathematical functions, filtering routines and lookup tables. The custom routines are very efficient and have been in production for many years [ 1 ]. These hand-crafted functions can be reused in new control algorithm designs being developed using Model Based Design (MBD) tools. The next generation of vehicle control software may contain a mix of both automatically generated software and manually developed code. At Ford Motor Company, the code is automatically generated from control algorithm models that are developed using The MathWorks tool chain. Depending on the project-specific needs, the control algorithm models are automatically translated to efficient C code using either The Math Works Real-Time Workshop Embedded Coder (RTW-EC) or dSPACE TargetLink production code generators.

Wavelets are a powerful mathematical tool used to multi-resolution time-frequency decomposition of signals, in order to analyze them in different scales and obtain different aspects of the information. Despite being a relatively new tool, wavelets have being applied in several areas of human knowledge, especially in signal processing, with emphasis in encoding and compression of image, video and audio. Based on a previous successful applications (FRAZIER, 1999) together a commitment to quality results, this paper evaluates the use of the Discrete Wavelet Transform (DWT) as an compression algorithm to reduce the amount of data collected in road load signals (load history) which are used by the durability engineering teams in the automotive industry.

Until recently, tool & die making was a very traditional industry, relying on extensive know-how accumulated over decades of practice. Essentially, it remained a two stage-process: engineering/manufacture, followed by tryout/productionization. Improvements focused on engineering and production methods, but tryout remained the exclusive domain of the die maker. At last, advances in computer modeling methods and the adoption of aggressive lean management principles have brought transformational changes to the tryout phase. At the same time, new safety and weight imperatives have increased the penetration of advanced materials, whose formability characteristics are quite different from mild steels. This paper will explore how these advanced materials affect this transformation.

The primary coating layer that inhibits salt spray induced corrosion on vehicle bodies is electrocoat. The application of electrocoat involves the electrodeposition of a polymer film on all metallic components of the vehicle body after body construction. Particularly challenging in the electrocoat process is the deposition of the coating in recessed areas of the vehicle due to material and electrical current access constraints to those regions. Currently the verification of correct electrocoat coverage requires the use of costly tear-down prototypes. A simulation tool, called EPD, has been developed that predicts the electrocoat coverage on the full vehicle body. The tool allows engineers to identify areas where there may be issues with electrocoat coverage and to see the effect of vehicle design or process modifications on coverage. A challenge in the development of any simulation tool is computational speed.