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A Machine Learning based Multi-objective Multidisciplinary Design Optimization (MMDO) for Lightweighting the Automotive Structures

Mahindra and Mahindra-Ranga Srinivas Gunti
  • Technical Paper
  • 2019-28-2424
To be published on 2019-11-21 by SAE International in United States
The present work involves Machine Learning (ML) based Multi-objective Multidisciplinary Design Optimization (MMDO) for lightweighting the automotive structures. The challenge in deployment of MMDO algorithms in solving real-world automotive structural design problems is the enormous time involved in solving full vehicle finite element models that involve large number of design variables and multiple performance constraints pertaining to vehicle dynamics, durability, crash and NVH domains. With the availability of powerful workstations and using the advanced Computer Aided Engineering (CAE) tools, it has become possible to generate huge sets of simulation data pertaining to multiple domains. In the present work, lightweigting of the vehicle structure is achieved, considered the vehicular hardpoint locations and the gages of the vehicle structures as the design variables and performance parameters pertaining to vehicle dynamics, structural durability, front-end intrusions during an IIHS offset impact test and the modal frequencies of few critical structural members as the constraint variables. Artificial Neural Networks (ANN) based algorithms were used for developing the predictive models of various performance parameters. The predictive models were then used to…

Machine Learning considerations in the context of Automotive Functional Safety Requirements for Autonomous Vehicles

General Motors-Vijai K Gopalakrishnan
  • Technical Paper
  • 2019-28-2519
To be published on 2019-11-21 by SAE International in United States
We are currently in the age of developing Autonomous Vehicles (AV). Never before in history, the environment has been as conducive as today for these developments to come together to deliver a mass produced autonomous car for use by general public on the roads. Several enhancements in hardware, software, standards and even business models are paving the way for rapid development of AVs, bringing them closer to production reality. Safety is an indispensable consideration when it comes to transportation products, and ground vehicle development is no different. We have several established standards. When it comes to Autonomous Vehicle development, an important consideration is ISO 26262 for, Automotive Functional Safety. Going from generic frameworks such as Failure Mode and Effects Analyses (FMEA) and Hazard and operability study (HAZOP) to Functional Safety, Safety of Intended Functionality, and Automotive Safety Integrity Levels specific is a natural progression. This, in specific to AV development context with a renewed perspective is the need of the hour. The fundamental assumption of a human driver being part of the vehicle, considered in…


General Motors Technical Center India-Vijayasarathy Subramanian, Anandakumar Marappan, Biju Kumar, Masani Sivakrishna
  • Technical Paper
  • 2019-28-2525
To be published on 2019-11-21 by SAE International in United States
Shared Mobility is changing the trends in Automotive Industry and its one of the Disruptions. The current vehicle customer usage and life of components are designed majorly for personal vehicle and with factors that comprehend usage of shared vehicles. The usage pattern for customer differ between personal vehicle, shared vehicle & Taxi. In the era of Autonomous and Shared mobility systems, the customer usage and expectation is high. The vehicle needs systems that will control customer interactions (Self-Expressive) & fix the issues on their own (Self-Healing). These two systems / methods will help in increasing customer satisfaction and life of the vehicle. We will be focusing on vehicle Closure hardware & mechanisms and look for opportunities to improve product life and customer experience in ride share and shared mobility vehicles by enabling integrated designs, which will Self-Express & Self-Heal. Vehicle closures having direct human interfaces with components like closures, handle & other hardware's will be tracked for their performance parameters and usage pattern. The performance parameters will be tracked for every customer and mapped to…

Digital Twins for Prognostic Profiling

Altair Engineering India Pvt Ltd-Painuri Thukaram
Altair Engineering India Pvt , Ltd.-Sreeram Mohan
  • Technical Paper
  • 2019-28-2456
To be published on 2019-11-21 by SAE International in United States
Digital Twins for Prognostic Profiling Authors: Sreeram Mohan*, Painuri Thukaram**, Panduranga Rao*** Objective / Question: Ability to have least failures in products on the field with minimum effort from the manufacturers is a major area of focus driven by Industry 4.0 initiatives. Amidst traditional methods of performing system / subsystem level tests often does not enable the complete coverage of a machine health performance predictions. This paper highlights a workable workflow that could be used as a template while considering system design especially employing Digital Twins that help in mimicking real-life scenarios early in the design cycle to increase product’s reliability as well as tend to near zero defects. Methodology: With currently available disruptive technologies , systems are integrated multi-domain 'mechatronics' systems operating in closed-loop/close-interaction. This poses great challenges to system health monitoring as failure of any component can trigger catastrophic system failures. It may be the reason that component failures, as per some aerospace reports, are found to be major contributing factors to aircraft loss-of-control. Essentially, it is either too expensive or impossible to…

Enhanced Road condition monitoring for developing countries

STMicroelectronics-Saurabh Rawat, Prashant Pandey
  • Technical Paper
  • 2019-28-2462
To be published on 2019-11-21 by SAE International in United States
"According to Data on Road accidents in India by Transport Research Wing of Ministry of Road Transport & Highways, more than 4 Lakhs road accidents happened every year from year 2003 to 2017. Poor road conditions and badly designed roads are the common cause of road accidents besides the driver's negligence. Poor roads and badly designed speed breakers are common in developing countries. Apart from accidents, poor road conditions can cause excessive fuel consumption & damage to vehicles. Road condition monitoring solutions aim to warn the drivers of upcoming bad patch on the road and optionally report road conditions to authorities. There are multiple existing solutions that use motion sensors and GPS to detect a bad patch on the road. The presented solution builds over capability of existing solutions by adding useful features making it more practical and useful. The presented scheme is able to differentiate between a pothole and a speed breaker using a machine learning based approach. It also employs additional sensors like gyroscope and optionally a camera to detect on which side…

A Self-Intelligent Traffic Light Control System based on Traffic Environment using Machine Learning

Maharaja Agrasen Inst. Of Technology-Shubham Upadhyaya
Maharaja Agrasen institute of technology-Ananya Bansal
  • Technical Paper
  • 2019-28-2459
To be published on 2019-11-21 by SAE International in United States
In this paper, we will detect and track vehicles on a video stream and count those going through a defined line and to ultimately give an idea of what the real-time on street situation is across the road network. Our major objective is to optimize the delay in transit of vehicles in odd hours of the day. It uses YOLO object detection technique to detect objects on each of the video frames And SORT (Simple Online and Realtime Tracking algorithm) to track those objects over different frames. Once the objects are detected and tracked over different frames a simple mathematical calculation is applied to count the intersections between the vehicles previous and current frame positions with a defined line. At present, the traffic control systems in India, lack intelligence and act as an open-loop control system, with no feedback or sensing network. Present technologies use Inductive loops and sensors to detect the number of vehicles passing by. This is a very inefficient and expensive way to make traffic lights adaptive. Using a simple CCTV camera…

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, chipping and breakage of the tool based on machine learning approach using statistical feature and decision tree algorithm. 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 algorithm. 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…

Fault Detection in Single Stage Helical Planetary Gearbox using Artificial Neural Networks (ANN) 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
Abstract 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 life time solution. Majority of failures are caused by bearing and gearbox. It also states that wind turbine gearbox failure causes the highest downtime as 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 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 healthy gearbox, sun defect, planet defect and ring defect under loaded conditions are obtained. The signals show the performance characteristics of gearbox condition. These characteristics and their number of occurrences were plotted in a histogram graph. Predominant statistical features which represent the fault…

Design and Implementation of Digital Twin for Predicting Failures in Automobiles using Machine Learning Algorithms

VIT Universtity-Kalivaradhan Ramesh Babu
Vellore Institute of Technology-Ponnuraman Balakrishnan, Chooriyaparambil Damodaran Naiju, Muthaiyan Madiajagan
  • Technical Paper
  • 2019-28-0159
To be published on 2019-10-11 by SAE International in United States
The drastic technological advancements in designing autonomous vehicles and connected cars lead to substantial progression in the commercial values of automobile industries. However, these advancements force the Original Equipment Manufacturers (OEMs) to shift from feedback-based reactive business analysis to operational-data based predictive analysis thereby enhancing both the customer satisfaction as well as business opportunities. The operational data is nothing but the parameters obtained from several parts of an automobile during its operation such as, temperature in radiator, viscosity of the engine oil and force applied over the brake disk. These operational data are gathered using several sensors implanted in different parts of an automobile and are continuously transmitted to backend computers to develop Digital Twin, which is a virtual model of the physical automobile. Later, the gathered operational data are analysed using data mining algorithms to predict the failures of an automobile well in advance, better insights into performance of an automobile thereby recommending alternative design choices and remote service management of failures by a professional technician. This research work primarily focuses towards the creation…

Self-affinity of an Aircraft Pilot's Gaze Direction as a Marker of Visual Tunneling

Bordeaux University-Jean-Marc André, Éric Grivel, Pierrick Legrand
Thales AVS France-Bastien Berthelot, Patrick Mazoyer, Sarah Egea
  • Technical Paper
  • 2019-01-1852
To be published on 2019-09-16 by SAE International in United States
For the last few years, a great deal of interest has been paid to crew monitoring systems in order to address potential safety problems during a flight. They aim at detecting any degraded physiological and/or cognitive state of an aircraft pilot or crew, such as visual tunneling, also called inattentional blindness. Indeed, they might have a negative impact on the performance to pursue the mission with adequate flight safety levels. One of the usual approaches consists in using sensors to collect physiological signals which are then analyzed. Two main families exist to process the signals. The first one combines feature extraction and machine learning whereas the second is based on deep-learning approaches which require a large amount of labeled data. In this work, we focused on the first family. In this case, various features can be deduced from the data by different approaches: spectrum analysis, a priori modeling and nonlinear dynamical system analysis techniques including the estimation of the self-affinity of the signals. In this paper, our purpose was to uncover whether the self-affinity of…