Your Selections

Neural networks
Show Only


File Formats

Content Types










   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

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…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.


IIT MANDI-Narsa Tummuru
IIT Mandi-Ankit Joshi, Arpan Gupta, Aman soni
  • Technical Paper
  • 2019-28-2485
To be published on 2019-11-21 by SAE International in United States
Abstract The electrification of conventional internal combustion engine vehicle is a need of today’s advanced world to reduce the dependency of the transportation sector on the oil and gases. It can be achieved by replacing the engine by an electric motor which is powerful enough to provide required torque. The important requirement for a vehicle to drive in the hilly region with steep corners is proper torque distribution on each wheel which is taken care by the differential system. When the friction between road and wheels are different from left to right, then the wheel with low friction contact will lose its traction on the road. These situations are unfavorable for driving a vehicle on off-road and extrema conditions like driving in muddy roads or on the ice. These problems can be overcome by providing individual power supply system to separate wheels. If the required torque can be provided to each wheel separately, then the problem can be overcome and the mechanical differential system can be avoided. The mechanical driveline is very bulky, which can…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

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…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.


Continental Germany-Richard Kopold PhD
Continental India-Vivek Venkobarao
  • Technical Paper
  • 2019-28-2443
To be published on 2019-11-21 by SAE International in United States
In a connected vehicle environment, the engine drive cycles operate in synchronized and regulated manner. This requires smooth transitions for improved CO_2 footprint. To arrive at this, there is need for intelligent and faster airpath control at transients. Authors aim to model and control every actuator of a coupled system in a synchronized manner with faster dynamic response. The turbocharger control is vital and forms heart of the system; This demands accurate position prediction of VTG. Deriving a control law for turbocharger is challenging due to the hybridized nature of turbocharger models in engine management system. It becomes extremely critical to estimate accurately, the position of VTG without introduction of any sensing devices. The control engineer always need to solve the trade-off between the controller performance KPI’s – rise time, transient response, controllability, observability and capability – stability and dynamics response etc. Author propose a model which improve the performance and capability of VTG control. Author presents a novel technique to model VTG position. A neural network based supervised learning model is derived. The model…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Assessing the Combined Outcome of Rice Husk Nano Additive and Water Injection Method on the Performance, Emission and Combustion Characters of the Low Viscous Pine Oil in a Diesel Engine

Anna University Chennai-Mebin Samuel P, Devaradjane Gobalakichenin
University College of Engineering Villupuram-Gnanamoorthi V
  • Technical Paper
  • 2019-01-2604
To be published on 2019-10-22 by SAE International in United States
The research work intends to assess the need and improvement of using a low viscous bio oil, RH (Rice Husk) Nano Particles and water injection method in enhancing the performance, emission and combustion characters of a diesel engine. One of the major setbacks for using biodiesel was its higher viscosity. Hence, a low viscous oil (Pine oil) which doesn’t need transesterification process was used as a biofuel in this study. To further improve its characteristics a non-metallic Nano additive produced from rice husk was added at 3 proportions (50, 100, 200 ppm) and the optimal quantity was found as 100ppm based on the BTE (brake thermal efficiency) value of 30.2% at peak load condition. This efficiency value was accompanied by a considerable decrease in pollutants like HC (Hydrocarbon), Smoke, CO (Carbon monoxide). On the contrary NOx (Oxides of Nitrogen) emission was found to be increased for all load values. At peak load, when compared with diesel, pine oil with RH has 19.2% increased NOx emission. To reduce this increased NOx emission, water was injected along…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

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…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

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…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Neural Network Based Virtual Sensor for Throttle Valve Position Estimation in a SI Engine

SITAMS-Chellappan Kavitha
VIT University-Bragadeshwaran Ashok, Sathiaseelan Denis Ashok, Chidambaram Ramesh Kumar
  • Technical Paper
  • 2019-28-0080
To be published on 2019-10-11 by SAE International in United States
Electronic throttle body (ETB) is commonly employed in an intake manifold of a spark ignition engine to vary the airflow quantity by adjusting the throttle valve in it. The actual position of the throttle valve is measured by means of a dual throttle position sensor (TPS) and the signal is feedback into the control unit for accomplishing the closed loop control in order handle the nonlinearities due to friction, limp-home position, aging, parameter variations. This work aims presents a neural networks based novel virtual sensor for the estimation of throttle valve position in the electronic throttle body. Proposed neural network model estimates the actual throttle position using three inputs such as reference throttle angle, angular error and the motor current. In the present work, the dynamic model of the electronic throttle body is used to calculate the current consumed by the motor for corresponding throttle valve movement. Proposed virtual sensor is tested for the sinusoidal and random driving cycle throttle angle input using a Bosch DVE5 electronic throttle body. Estimated throttle valve angle using the…

Q&A: “Neural Lander” Uses AI to Land Drones Smoothly

  • Magazine Article
  • TBMG-34903
Published 2019-08-01 by Tech Briefs Media Group in United States

Soon-Jo Chung is Bren Professor of Aerospace in the Division of Engineering and Applied Science (EAS) at Caltech and research scientist at Jet Propulsion Laboratory. He and his team developed a method for using a deep neural network to help autonomous drones “learn” how to land more safely and smoothly.

Bringing Human-Like Reasoning to Driverless Car Navigation

  • Magazine Article
  • TBMG-34772
Published 2019-07-01 by Tech Briefs Media Group in United States

With the aim of bringing more human-like reasoning to autonomous vehicles, MIT researchers have created a system that uses only simple maps and visual data to enable driverless cars to navigate routes in new, complex environments.