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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…
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Use of Truncated Finite Element Modeling for Efficient Design Optimization of an Automotive Front End Structure

Indian Institute of Science-Anindya Deb, Ranga Srinivas Gunti
Univ of Detroit Mercy-Utpal Dutta
Published 2015-04-14 by SAE International in United States
The present work is concerned with the objective of multi disciplinary design optimization (MDO) of an automotive front end structure using truncated finite element model. A truncated finite element model of a real world vehicle is developed and its efficacy for use in design optimization is demonstrated. The main goal adopted here is minimizing the weight of the front end structure meeting NVH, durability and crash safety targets. Using the Response Surface Method (RSM) and the Design Of Experiments (DOE) technique, second order polynomial response surfaces are generated for prediction of the structural performance parameters such as lowest modal frequency, fatigue life, and peak deceleration value. Using the lowest natural frequency of the front end, fatigue factor of safety and peak deceleration extracted from the NCAP crash pulse as constraint parameters, gages of bumper beam, front rails and shotguns as design variables, the mass of the front end structure (i.e. effectively the total mass of the parts mentioned) is optimized. The optimum solution is then obtained by using genetic algorithm functionality in commercial MATLAB package.…
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A Unified CAE Framework for Assessing an IC Engine Design

Indian Institute of Science-Amardeep Singh, Anindya Deb, Amit Mohan Mensi, Ranga Srinivas Gunti
Published 2015-04-14 by SAE International in United States
Despite the considerable advancements made in the applications of CAE for evaluation of an IC engine, an integrated approach to the design of such engines based on thermo-mechanical considerations appears to be lacking. The usage of heterogeneous tools for thermal, mechanical and vibration analysis in the industry decreases the efficiency of the product development process. In an effort to reduce this bottleneck, a unified framework is presented here according to which heat transfer and thermo-mechanical stress analysis of a four-stroke single cylinder diesel engine is carried out in a unified manner with the aid of a multi-physics explicit finite element analysis tool, LS-DYNA, with robust contact interfaces leading to a realistic representation of engine dynamics.
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