A machine learning framework for accelerating the design process using CAE simulations: An application to finite element analysis in structural crashworthiness

2021 ◽  
Vol 385 ◽  
pp. 114008
Author(s):  
Christopher P. Kohar ◽  
Lars Greve ◽  
Tom K. Eller ◽  
Daniel S. Connolly ◽  
Kaan Inal
Keyword(s):  
Machine Learning ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Design Process ◽  
Element Analysis ◽  
Learning Framework

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

A Design Process for a Foldable Container Structure Using a Finite Element Analysis

2014 ◽  
Vol 1079-1080 ◽  
pp. 348-353
Author(s):  
Sang Heon Yoon ◽  
Yang Jai Shin ◽  
Yong Shin Lee
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Design Process ◽  
Element Analysis ◽  
Iso Standard ◽  
Main Structure ◽  
The Finite Element Analysis ◽  
Standard Regulation

This study is concerned with a design process for a foldable container structure using a finite element analysis. A foldable container structure consists of frames, panels and hinge systems. The main structure of a foldable container carries all the loads while a hinge system is designed to provide its foldability. In this work, finite element structural analyses for the main foldable container structure are carried out based on the ISO standard regulation, whose results are then taken for the design of a hinge system. The finite element analysis with two types of hinge systems are also performed. It is found out that the main structure of a standard 20ft container could be used for the foldable container with the same capacity if the corner edge in the side assembly is strengthened. It is also concluded that the hinge systems proposed in this work could be successfully used in a foldable 20ft container.

Machine learning predictions on fracture toughness of multiscale bio-nano-composites

2020 ◽  
Vol 39 (15-16) ◽  
pp. 587-598 ◽  
Author(s):  
Vahid Daghigh ◽  
Thomas E Lacy ◽  
Hamid Daghigh ◽  
Grace Gu ◽  
Kourosh T Baghaei ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Fracture Toughness ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Nano Composites ◽  
Natural Material ◽  
Element Analysis ◽  
Adaptive Boosting ◽  
Impact Tests

Tailorability is an important advantage of composites. Incorporating new bio-reinforcements into composites can contribute to using agricultural wastes and creating tougher and more reliable materials. Nevertheless, the huge number of possible natural material combinations works against finding optimal composite designs. Here, machine learning was employed to effectively predict fracture toughness properties of multiscale bio-nano-composites. Charpy impact tests were conducted on composites with various combinations of two new bio fillers, pistachio shell powders, and fractal date seed particles, as well as nano-clays and short latania fibers, all which reinforce a poly(propylene)/ethylene–propylene–diene-monomer matrix. The measured energy absorptions obtained were used to calculate strain energy release rates as a fracture toughness parameter using linear elastic fracture mechanics and finite element analysis approaches. Despite the limited number of training data obtained from these impact tests and finite element analysis, the machine learning results were accurate for prediction and optimal design. This study applied the decision tree regressor and adaptive boosting regressor machine learning methods in contrast to the K-nearest neighbor regressor machine learning approach used in our previous study for heat deflection temperature predictions. Scanning electron microscopy, optical microscopy, and transmission electron microscopy were used to study the nano-clay dispersion and impact fracture morphology.

Estimating Collapse Pressure of Centralizer Subs From Machine Learning Models

2019 ◽  
Author(s):  
Ishita Chakraborty
Keyword(s):  
Machine Learning ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Regression Model ◽  
Learning Models ◽  
Element Analysis ◽  
Collapse Pressure ◽  
Geometrical Features ◽  
Past Data ◽  
Machine Learning Models

Abstract Centralizer subs are run in conjunction with the casing strings in the oil/gas wells to ensure that the casing is centralized while it is installed down hole. Centralizer subs are fabricated of stronger material than the casing strings and designed such that it can sustain a higher collapse pressure than the attached tubing string. A typical centralizer sub is a tube with some complex geometrical features, so the collapse pressure of a centralizer sub can only be estimated by conducting a finite element analysis or subjecting it to a collapse pressure test. Both the options are time consuming and expensive. In this work, a machine learning based regression model is used to derive a parametric equation for calculating the collapse pressure of a centralizer sub. The data needed to train and cross validate the regression model is obtained from finite element analysis (FEA). This machine learning based equation provides a closer estimate of the collapse pressure of the centralizer subs to the results obtained from the FEA than the existing collapse prediction equations from API RP 1111. This machine learning based estimation of collapse pressure will help in correctly predicting the collapse rating of the centralizer sub without performing FEA or testing for each individual subs. This approach of building machine learning models from data generated from FEA can be used for analysis of other equipment as well. With the availability of past data collected/generated through years, the recent advances in machine learning can be used to save time and resources.

Study on Structural Analysis for One-Cylinder Vertical Engine Assembly by FEA Method

2014 ◽  
Vol 496-500 ◽  
pp. 597-600
Author(s):  
Yong Wang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Structural Analysis ◽  
Design Process ◽  
Design Approach ◽  
Maximum Pressure ◽  
Structural Components ◽  
Element Analysis ◽  
The One

In this paper, a simulation driven design approach was employed for the design process of the structural components of the one-cylinder vertical Engine. Finite element analysis (FEA) of the one-cylinder vertical engine assembly has been completed for assembly and maximum pressure loads using FEA software.

Design and Numerical Analysis of a Roof-Mounted Bicycle Carrier

2016 ◽  
Vol 251 ◽  
pp. 177-182 ◽  
Author(s):  
Mariusz Ptak ◽  
Jacek Karliński ◽  
Damian Derlukiewicz ◽  
Paulina Działak
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Numerical Analysis ◽  
Design Process ◽  
Dynamic Load ◽  
Total Mass ◽  
Commercial Vehicles ◽  
Element Analysis ◽  
Safety Standards ◽  
Software Packages

The purpose of this paper is to present the design process and subsequent numerical analysis calculations of a new roof-mounted bicycle carrier for vehicles. The bicycle carrier is mounted on the vehicles longitudinal bars. The designed construction is subjected to both static and dynamic load sets to check if it meets the requirements of ISO 11154 norm – which specifies the minimum safety requests for roof load carrier intended for mounting on the roof of passengers cars and light commercial vehicles with a maximum authorized total mass up to 3,5t. To fulfil the specifications associated to safety, standards and traffic laws test four different software packages were used: CATIA V5 and NACA airfoil generator for designing, Cambridge Engineering Selector for choosing the most suitable materials and Abaqus CAE for Finite Element Analysis.

Design of Chassis Frame for All-Terrain Vehicle for Educational Purposes

2014 ◽  
Vol 695 ◽  
pp. 742-745
Author(s):  
Muhammad Zahir Hassan ◽  
John Hendrie ◽  
Abdul Munir Fudhail ◽  
Mohd Azli Salim
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Design Process ◽  
Main Part ◽  
Applied Load ◽  
Maximum Stress ◽  
Design Method ◽  
Element Analysis ◽  
Proper Design ◽  
It Structure

All-terrain vehicle is famously used for various purposes. The design of the chassis of this vehicle is critical in determining the overall strength. In this paper, the design chassis frame for the use of all-terrain vehicle (ATV) is presented. In designing the chassis frame, a proper design method was employed. Finite Element Analysis (FEA) was utilized to determine the maximum stress and displacement of the frame when a particular load is applied onto it. Structure modifications need to be done if the chassis frame could not sustain the applied load. After the design process is completed, the fabrication of the frame is conducted by students of the engineering faculty. The fabricated frame will be used as the main part for a project of which a complete ATV will be developed. The main purpose of the project is to instill the interest among the student in engineering through the application of classroom.

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