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Analysis of pressure variation in wheel with the aid of wheel speed sensor

Abhishek Mandhana
College of Engineering Pune-Rajiv basavarajappa PhD
  • Technical Paper
  • 2019-28-2450
To be published on 2019-11-21 by SAE International in United States
Objective: The Objective of the research is to detect drop in level of pressure in the wheel with respect to nominal pressure using data obtained from speed sensors. The research discusses the standard procedure of experimentation to obtain data which eventually used to produce results. This procedure is taken from principles Design of Experiments. Statistical tools are used to analyze and give determining factors for pressure variation. Methodology: To study idea, we made use of two-wheeler platform and collected data of wheel speed sensors on both wheels. The idea is when there is any change in tire pressure the radius of the wheel also changes and usually this relation is direct. Hence, change in tire pressure changes the angular velocity of the wheel. In this approach wheel speed sensors are used to measure the angular speed for standard and reduced pressure conditions. The data obtained from the wheel speed sensor is analyzed through statistical methods and different determining values are calculated. These determining parameters are compared to see the variations in the pressure. To obtain…
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Development of a Graphical User Interface (GUI) Based Tool for Vehicle Dynamics Evaluation

Mahindra & Mahindra Ltd-Saravanan Muthiah
Mahindra & Mahindra, Ltd.-Divyanshu Joshi
  • Technical Paper
  • 2019-28-2397
To be published on 2019-11-21 by SAE International in United States
Title Development of a Graphical User Interface (GUI) Based Tool for Vehicle Dynamics Evaluation Authors Mr. Shubham Kedia, Dr. Divyanshu Joshi, Dr. Muthiah Saravanan Mahindra Research Valley, Mahindra & Mahindra, Chennai Objective Objective metrics for evaluation of major vehicle dynamics performance attributes i.e. ride, handling and steering are required to compare, validate and optimize dynamic behavior of vehicles. Some of these objective metrics are recommended and defined by ISO and SAE, which involve data processing, statistical analysis and complex mathematical operations on acquired data, through simulations or experimental testing. Due to the complexity of operations and volume of data, evaluation is often time consuming and tedious. Process automation using existing tools such as MS Excel, nCode, Siemens LMS, etc. includes several limitations and challenges, which make it cumbersome to implement. In the current work, a GUI based post-processing tool is developed for automated evaluation of ride, handling and steering performance. Methodology This work is about development of a centralized platform for quantification, visualization and comparison of ride, handling and steering performance metrics from testing and…
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STATISTICAL ANALYSIS OF LOW CYCLE FATIGUE PROPERTIES IN METALS FOR ROBUST DESIGN

GM Global Technical Center, USA-Abolhassan Khosrovaneh
General Motors Technical Center India-Karthigan Ganesan, Biswajit Tripathy
  • Technical Paper
  • 2019-28-2576
To be published on 2019-11-21 by SAE International in United States
Objective: In ground vehicle industry, strain life approach is commonly used for predicting fatigue life. This approach requires use of fatigue material properties such as fatigue strength coefficient (σf'), fatigue strength exponent (b), fatigue ductility coefficient (εf'), fatigue ductility exponent (c), cyclic strength coefficient (K′) and cyclic strain hardening exponent (n′). These properties are obtained from stable hysteresis loop of constant amplitude strain-controlled uniaxial fatigue tests. Usually fatigue material properties represent 50th percentile experimental data and doesn't account possible material variation in the fatigue life calculation. However, for robust design of vehicle components, variation in material properties need to be taken into account. In this paper, methodology to develop 5th percentile (B5), 10th percentile (B10) and 20th percentile (B20) fatigue material properties are discussed. Possible material variation in fatigue life prediction is included as B5, B10 and B20 fatigue material properties. Methodology: Fatigue strength coefficient (σf') and fatigue strength exponent (b) are obtained by performing a linear regression on true stress amplitude (∆σ/2) versus reversals to failure (2Nf) in log-log scale. Fatigue ductility coefficient (εf')…
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Lane Line detection by Lidar intensity value interpolation

Kettering University-Viktor Ciroski, Jungme Park
  • Technical Paper
  • 2019-01-2607
To be published on 2019-10-22 by SAE International in United States
We present an approach to estimate a single lane line using a LiDAR unit for autonomous vehicles. By comparing the difference in elevation of LiDAR channels, a drivable region is defined. Further, by filtering and sorting intensity values, we are able to distinguish potential lane markings. By calculating the standard deviation of the lane markings in the y-axis the data can be further refined to specific points of interest. By applying a statistical approximation, to these points of interest, we can interpolate a linear approximation of the lane line.
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Unsettled Technology Domains in Industrial Metrology

Muelaner Engineering, Ltd.-Jody E. Muelaner
  • Research Report
  • EPR2019003
To be published on 2019-10-15 by SAE International in United States
Within manufacturing, measurements are used to make decisions related to product verification and process control. The selection of production machines and instruments involves a trade-off to achieve the required accuracy while minimizing cost. Similarly, deciding on the level of confidence at which products are rejected is a trade-off between the cost of rejecting acceptable parts and the cost of passing substandard products to the customer. These trade-offs can only be optimized if the uncertainties are fully understood. Currently multiple methodologies are used to understand uncertainties and variation within manufacturing, such as Measurement Systems Analysis (MSA), Statistical Process Control (SPC), and uncertainty evaluation. Industry lacks a unified approach that provides a complete understanding of uncertainty. This means that optimal decisions cannot be made to maximize the profitability of production systems. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution…
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Tool Condition Monitoring in Face Milling Process Using Decision Tree and Statistical Features of Vibration Signal

B S Abdur Rahman Crescent Institute of Science & Technology-Pradeep Kumar Durairaj, Muralidharan Vaithiyanathan
  • Technical Paper
  • 2019-28-0142
To be published on 2019-10-11 by SAE International in United States
In milling process, the quality of the machined component is highly influenced by the condition of the tool. Hence, monitoring the condition of the tool becomes essential. A suitable mechanism needs to be devised in order to monitor the condition of the tool. To achieve this, condition monitoring of milling tool is taken up for the study. In this work, the condition of the tool is classified as good tool and tool with common faults in face milling process such as flank wear, worn out and breakage of the tool based on machine learning approach using statistical feature and decision tree technique. Vibration signals of the milling tool are obtained during machining of mild steel. Statistical features are extracted from the obtained signal, in which the important features are selected using decision tree. The selected features are given as the input to the same algorithm. The output of the algorithm is utilized for classifying the different conditions of the tool. The experimental results show that the accuracy of decision tree technique is at the acceptable…
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Fault Detection in Single Stage Helical Planetary Gearbox Using Artificial Neural Networks (ANN) and Decision Tree with Histogram Features

BSACIST-Syed Shaul Hameed, Muralidharan Vaithiyanathan, Mahendran Kesavan
  • Technical Paper
  • 2019-28-0151
To be published on 2019-10-11 by SAE International in United States
Drive train failures are most common in wind turbines. Lots of effort has been made to improve the reliability of the gearbox but the truth is that these efforts do not provide a lifetime solution. Majority of failures are caused by bearing and gearbox. It also states that wind turbine gearbox failure causes the highest downtime as the repair has to be done at Original Equipment Manufacturer [OEM]. This work aims to predict the failures in planetary gearbox using fault diagnosis technique and machine learning algorithms. In the proposed method the failing parts of the planetary gearbox are monitored with the help of accelerometer sensor mounted on the planetary gearbox casing which will record the vibrations. A prototype has been fabricated as a miniature of single stage planetary gearbox. The vibrations of the healthy gearbox, sun defect, planet defect and ring defect under loaded conditions are obtained. The signals show the performance characteristics of the gearbox condition. These characteristics and their number of occurrences were plotted in a histogram graph. Predominant statistical features which represent…
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Investigation of Machinability Characteristics on Turning of Nimonic 90A Using Al2O3 and CNT Nanoparticle in Groundnut Oil

Vellore Institute of Technology-Venkatesan Kannan, Devendiran Sundararajan
  • Technical Paper
  • 2019-28-0072
To be published on 2019-10-11 by SAE International in United States
Nimonic 90A alloy is a nickel-chromium-cobalt alloy and found as a potential material for turbine blades, discs, forgings, a ring section, and hot-working tools. This paper presents the effect of concentration along with cutting speed and feed rate on Fz: cutting force, Ra: surface roughness and Vba: tool wear with the application of two different nanofluids (NFS) on turning of Nimonic 90A by TiAlN PVD carbide cutting inserts. The nanoparticles suspended in oil taken for present investigation are nAl2O3, nCNT, and groundnut oil. The Taguchi L9 orthogonal array and derringer’s desirability response surface has been employed for parameter design and optimal search. 3D surface plots, factor effect plots, Taguchi S/N, and variance tests are used to study the effect of concentration on the machining performance of Nimonic 90A. The statistical analysis revealed % concentration for nCNT and cutting speed for nAl2O3 are found as an influenced parameter on performance characteristics. From the optimization analysis, 0.25% nCNT NFs along with a cutting speed of 40 m/min and 0.17 mm/rev feed rate has proved the better machining…
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Simulation of Aircraft Assembly via ASRP Software

Airbus-Elodie Bonhomme
Peter the Great St. Petersburg Polytechnic University-Nadezhda Zaitseva, Tatiana Pogarskaia, Olga Minevich, Julia Shinder
Published 2019-09-16 by SAE International in United States
ASRP (Assembly Simulation of Riveting Process) software is a special tool for assembly process modelling for large scale airframe parts. On the base of variation simulation, ASRP provides a convenient way to analyze, verify and optimize the arrangement of temporary fasteners. During the assembly of airframe certain criteria on residual gap between parts must be fulfilled. The numerical approach implemented in ASRP allows to evaluate the quality of contact on every stage of assembly process and solve verification and optimization problems for temporary fastener patterns. The paper is devoted to description of several specialized approaches that combine statistical analysis of measured data and numerical simulation using high-performance computing for optimization of fastener patterns, calculation of forces in fasteners needed to close initial gaps, and identification of hazardous areas in junction regions via ASRP software.
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The Right Stuff for Aging Electronics/Intermittence/No Fault Found

Universal Synaptics-Hector I. Knudsen
Published 2019-09-16 by SAE International in United States
For those in the avionics repair and maintenance business, the acronyms NFF (No Fault Found), NTF (No Trouble Found), and CND (Cannot Duplicate) are, unfortunately, all too familiar terms. After several decades of frustration with this illusive phenomenon, it continues to consume an enormous amount of test and diagnostic effort and is the source of considerable cost and discomfort within the multi-level avionics repair model.There are undoubtedly many causes of NFF and all of them should be addressed. The question is: Where do you start and which solution will be the most beneficial?Our particular efforts have focused on the literal or statistical analysis of NFF, recognizing that if the system’s MTBF (Mean Time Between Failure) has decreased, or if the device's NFF rate has increased with age and deterioration, a physical fault is most likely present. However, if it isn’t found during conventional testing then it probably only fails intermittently. Similarly, having an intermittent failure mode, it in all probability cannot be detected or diagnosed at testing time because of known and demonstrated limitations in…
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