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The History of Laminated Steel

Antiphon/C&A/ AAP (retired)-Peter Jackson
General Motors (retired)-James Shedlowsky
  • Technical Paper
  • 2019-01-1578
To be published on 2019-06-05 by SAE International in United States
This paper discusses the background and history of “laminated steel” (commonly called “noiseless steel” or MPM). It provides the early development, where it came from, and how it was introduced to North America as a new tool for engineering acoustical solutions. A progressive timeline shows laminated steel from its earliest inception in Europe to its current role in today’s global market. Case histories along with examples of successful applications detail its important contribution in advancing the technology for component damping. Many manufacturing sources as well as end users have been impacted over the decades since it was first introduced. Some of those companies will be noted. The background information for this paper is provided by many of the individuals who were involved in the very early stages of its introduction as well those who are currently working to utilize the technology of laminated steel.

Machine Learning with Decision Trees and Multi-Armed Bandits: An Interactive Vehicle Recommender System

Carnegie Mellon University-Tong Yu, Ole Mengshoel
Ford Motor Co., Ltd.-Dominique Meroux, Zhen Jiang
Published 2019-04-02 by SAE International in United States
Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences. With these preferences as prior information, the multi-armed bandit algorithm, using Thompson sampling, efficiently leverages the user’s feedback to improve the recommendations in an online fashion. The empirical results show that…
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Prediction of Human Actions in Assembly Process by a Spatial-Temporal End-to-End Learning Model

Clemson University-Zhujun Zhang, Weitian Wang, Yi Chen, Yunyi Jia
Harbin Institute of Technology-Zhujun Zhang, Gaoliang Peng
Published 2019-04-02 by SAE International in United States
It’s important to predict human actions in the industry assembly process. Foreseeing future actions before they happened is an essential part for flexible human-robot collaboration and crucial to safety issues. Vision-based human action prediction from videos provides intuitive and adequate knowledge for many complex applications. This problem can be interpreted as deducing the next action of people from a short video clip. The history information needs to be considered to learn these relations among time steps for predicting the future steps. However, it is difficult to extract the history information and use it to infer the future situation with traditional methods. In this scenario, a model is needed to handle the spatial and temporal details stored in the past human motions and construct the future action based on limited accessible human demonstrations. In this paper, we apply an autoencoder-based deep learning framework for human action construction, merging into the RNN pipeline for human action prediction. This contrasts with traditional approaches which use hand-crafted features and different domain outputs. We implement the proposed framework on a…
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Accuracy and Sensitivity of Yaw Speed Analysis to Available Data

MEA Forensic Engineers & Scientists-Bradley Heinrichs, Janice Lee, Cole Young
Published 2019-04-02 by SAE International in United States
Accident reconstructionists rarely have complete data with which to determine vehicle speed, and so the true value must be bracketed within a range. Previous work has shown the effect of friction uncertainty in determining speed from tire marks left by a vehicle in yaw. The goal of the current study was to assess improvements in the accuracy of vehicle speed estimated from yaw marks using progressively more scene and vehicle information. Data for this analysis came from staged S-turn maneuvers that in some cases led to rollover of sport utility vehicles. Initial speeds were first calculated using the critical curve speed (CCS) formula on the yaw marks from the first portion of the S-maneuver. Then computer simulations were performed with progressively more input data: i) the complete tire marks from the whole S-maneuver, ii) measured vehicle mass, iii) measured suspension stiffness and damping, and iv) measured steering history. Simulations based on the complete tire marks reduced the average error compared with the CCS equation if measured accelerations were also matched. Adding the remaining input data…
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Deriving Strain Based Local Structural Element Concept for the Fatigue Assessment of Additively Manufactured Structures

Fraunhofer Institute LBF-Rainer Wagener, Benjamin Möller, Tobias Melz, Matilde Scurria
Published 2019-04-02 by SAE International in United States
Additive manufacturing offers new options for lightweight design for safety parts under cyclic loading conditions. In order to utilize all advantages and exploit the full potential of additive manufactured parts, the main impact factors on the cyclic material behavior not only have to be identified and quantified but also prepared for the numerical fatigue assessment. This means in case of the AlSi10Mg aluminum alloy to consider influences related to the exposure strategy, heat treatment, microstructure, support structures and the surface conditions, as well as the influence of the load history and finally the interaction of these influences in order to perform a high quality fatigue assessment. Due to these reasons, and with respect to the numerical effort, the cyclic material behavior of additively manufactured AlSi10Mg produced by selective laser melting will be discussed. With respect to the microstructure, as well as the manufacturing process, a local structure element will be derived for the fatigue assessment. Thus, an influence that may be directly induced by the manufacturing process, like pores and different microstructures, can be considered…
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Reconstructing Vehicle Dynamics from On-Board Event Data

MEA Forensic Engineers & Scientists-Brandon Tsuge, Mike Yang, Thomas Flynn, Peter Xing, Jonathan Lawrence, Bradley Heinrichs, Gunter Siegmund
Published 2019-04-02 by SAE International in United States
Modern vehicles record dynamic data from a number of on-board sensors for events that could precede a crash. These data can be used to reconstruct the behavior of a vehicle, although the accuracy of these reconstructions has not yet been quantified. Here, we evaluated various methods of reconstructing the vehicle kinematics of a 2017 and a 2018 Toyota Corolla based on Vehicle Control History (VCH) data from overlapping events generated by the pre-collision system (PCS), sudden braking (SB) and anti-lock brake (ABS) activation. The vehicles were driven towards a stationary target at 32-64 km/h (20-40 mph) and then after the pre-collision alarm sounded the vehicle was steered sharply right or left and braked rapidly to rest. VCH data for PCS event were recorded at 2 Hz and for the sudden braking and ABS activation events at 6.7 Hz. The steering wheel angle and the vehicle’s longitudinal acceleration, lateral acceleration, and angular rate data were extracted and used to predict the vehicle position and heading over the duration of the VCH data record preceding the vehicle…
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The History of Human Factors in Seating Comfort at SAE’s World Congress: 1999 to 2018

Ford Motor Co., Ltd.-Michael Kolich
Published 2019-04-02 by SAE International in United States
In many fields of technology, examinations of the past can provide insights into the future. This paper reviews the last 20 years of automotive seat comfort development and research as chronicled by SAE’s session titled “Human Factors in Seating Comfort”. Records suggest that “Human Factors in Seating Comfort” has existed as a separate session at SAE’s World Congress since 1999. In that time there have been 148 unique contributions (131 publications). The history is fascinating because it reflects interests of the time that are driven by technology trends, customer wants and needs, and new theories. The list of contributors, in terms of authors and their affiliations, is also telling. It shows shifts in business models and strategies around collaboration. The paper ends with a discussion of what can be learned from this historical review and the major issues to be addressed. One of the more significant contributions of this paper is the reference list. It contains all 131 of SAE’s “Human Factors in Seating Comfort” published papers in a single location. These references represent the foundation…
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Powering back to the FUTURE

Lindsay Brooke, Erika Anden
  • Magazine Article
  • 19AUTP04_04
Published 2019-04-01 by SAE International in United States

Rotaries, gas turbines, hybrids-even nukes! Propulsion tech from the past, present, and beyond the horizon is revealed at the SAE Mobility History display at WCX'19.

Credit Leonardo DaVinci for creating what many historians believe was the first depiction of a self-powered vehicle. It was powered by springs. And in the centuries since then, engineers have found increasingly complex and interesting solutions for vehicle propulsion.

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Development of the Next Generation Flexible Tooling System

Larry Kirby, Ronald Weddle
Published 2019-03-19 by SAE International in United States
Flexible Tooling Systems have been developed as a reconfigurable part support system to enable trimming of multiple part geometries utilizing a single router or waterjet. The driver for this development has been improved part quality, elimination of ergonomic issues associated with manually trimming, and the elimination of cost for part number specific hard tooling and the associated cost for manufacturing, maintenance, and storage. This paper will briefly trace the evolution of aerospace parts trimming history. The remainder of the report will focus on the technical objectives associated with the development of the Next Generation Flexible Tooling System, how they were achieved including the process for validation of each support location in aircraft coordinates. This system is designed to increase part holding accuracy with specific support location validation, and significantly reduce system maintenance costs in wet or dry environments.
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Theoretical Modeling of the Mechanical Degradation of Polymer Composites due to Moisture/Water Absorption and Damage Progression

Mississippi State University-Ge He, Yucheng Liu
Published 2019-03-19 by SAE International in United States
The moisture/water absorption and microvoids/cracks progression are two well-understood mechanisms that have significant degradation effects on the mechanical properties/behaviors of the polymer-based composites. To theoretically investigate the effects of above two mechanisms, we develop a simple fiber reinforced polymer composites model by employing the internal state variable (ISV) theory. The water content and the anisotropically distributed damage of the composites are considered as two ISVs (the water content is described by a scalar variable and the damage is defined as a second order tensor) whose histories are governed by two specific physically-based evolution equations. The proposed model can be easily cast into a general theoretical framework to capture more polymer composites behaviors such as viscoelasticity, viscoplasticity and the thermal effect. The results predicted by current model are in good agreement with the experimental data, which allows us to implement the model into FEA for future structural analysis.
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