Numerical Simulation Methodology for Robust Optimization Using Six Sigma Analysis Valeo India Private Limited

Format:

Book

Conference/Event

Author/Creator:

Bhatta, Hari Venkata Santosh, author.

Contributor:

Couturier, Benoit

Ganesan, Rajeshkumar

Khajamohideen, Abdur-Rasik

V, Naveenkumar

Conference Name:

International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility (2021-10-08 : Chennai, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

Climate change and global warming are the main threats to our planet. CO2 emissions contribute vastly to climate change and automobiles contribute to CO2 emissions. We can reduce the CO2 emissions from vehicles by various measures, out of which shredding weight is one of the solutions, and even for hybrid or electric vehicles there will be a need for weight reduction for the control of global CO2 emission. We can shred the weight of the automobiles by replacing the components with lighter materials or by optimizing the components by removing excessive material. It is not always possible to change the materials due to its mechanical, thermal properties, manufacturability et cetera This leads to the other method which is removing excessive material. Today, we use different simulation tools like ANSYS for topology or shape optimization.During the traditional optimization, we perform the simulation, based on the available design limits and propose the best optimized design to the customer. This method doesnt consider any uncertainty like manufacturing tolerances, material irregularities etc in the model. Hence the proposed design may not be said as a robust design. The proposed Robust Optimization Methodology takes into account the uncertainties and proposes the robust optimized design.Robust optimization has two stages. In the first stage, we need to calculate the standard deviation (sigma) of output results based on the uncertainty in input parameters. In the second stage, we use the calculated sigma and modify the optimization target based on the required confidence level (for e.g., 3*Sigma (σ) 99.73% Confidence level).In this paper we demonstrate the robust optimization methodology on a clutch damper drive plate and propose robust design which resulted in 15% reduction in weight

Notes:

Vendor supplied data

Publisher Number:

2021-28-0248

Access Restriction:

Restricted for use by site license