Machine Learning Based Approach for Prediction of Hood Oilcanning Performances

计算机科学 人工智能 机器学习 屈曲 汽车工业 算法 工程类 结构工程 航空航天工程
作者
Arunkumar Srinivasan,S Aravamuthan,Bellamkonda Madhurya,Suhas S Kangde
出处
期刊:SAE technical paper series 被引量:1
标识
DOI:10.4271/2023-01-0598
摘要

<div class="section abstract"><div class="htmlview paragraph">Computer Aided Engineering (CAE) simulations are an integral part of the product development process in an automotive industry. The conventional approach involving pre-processing, solving and post-processing is highly time-consuming. Emerging digital technologies such as Machine Learning (ML) can be implemented in early stage of product development cycle to predict key performances without need of traditional CAE. Oil Canning loadcase simulates the displacement and buckling behavior of vehicle outer styling panels. A ML model trained using historical oil canning simulation results can be used to predict the maximum displacement and classify buckling locations. This enables product development team in faster decision making and reduces overall turnaround time. Oil canning FE model features such as stiffness, distance from constraints, etc., are extracted for training database of the ML model. Initially, 32 model features were extracted from the FE model. Domain expertise and variable selection techniques were implemented to clean up the database for dependencies and duplicates. This resulted in identification of 21 key parameters for training the ML model. Database for buckling classification model is highly skewed with only 5% data points with buckling. Synthetic data is generated using SMOTE algorithm to overcome data imbalance. These features are then used to train and validate the ML model for buckling. Predictive model developed using Extreme Gradient boosting (XG Boost) algorithm with R<sup>2</sup> more than 90% for training and test datasets. It predicted maximum displacement with 20% error for 80% test data points. Also, buckling data points are classified with 98% accuracy. Prediction made using the ML model is in good agreement (&lt; 20% error) with CAE results. This resulted in substantial time savings from 11 days to 30 minutes for the prediction of key performances.</div></div>
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
杂菜流完成签到,获得积分10
2秒前
NiceSunnyDay完成签到 ,获得积分10
4秒前
终于花开日完成签到 ,获得积分10
4秒前
ding7862完成签到,获得积分10
6秒前
玥月完成签到 ,获得积分10
7秒前
jimforu完成签到 ,获得积分10
9秒前
10秒前
hadern完成签到,获得积分20
10秒前
谨慎的凝丝完成签到,获得积分10
10秒前
12秒前
君无名完成签到 ,获得积分10
13秒前
13秒前
土豪的钻石完成签到,获得积分10
16秒前
xuhanghang发布了新的文献求助10
16秒前
风花雪月完成签到 ,获得积分10
17秒前
Adrenaline完成签到 ,获得积分10
17秒前
我和你完成签到 ,获得积分10
17秒前
djbj2022发布了新的文献求助10
17秒前
拓跋涵易完成签到,获得积分10
18秒前
无私雅柏完成签到 ,获得积分10
19秒前
半壶月色半边天完成签到 ,获得积分10
19秒前
zx完成签到 ,获得积分10
19秒前
20秒前
Echo完成签到,获得积分10
21秒前
严惜完成签到,获得积分10
24秒前
djbj2022完成签到,获得积分10
24秒前
shijiaoshou完成签到,获得积分10
26秒前
ADcal完成签到 ,获得积分10
29秒前
29秒前
GXLong完成签到,获得积分10
30秒前
祝你勇敢完成签到 ,获得积分10
31秒前
paper reader完成签到,获得积分10
40秒前
呆呆完成签到 ,获得积分10
40秒前
纯真雁菱完成签到,获得积分10
43秒前
xuhanghang完成签到,获得积分10
46秒前
hhh完成签到,获得积分10
47秒前
皮皮蛙完成签到,获得积分10
48秒前
鸡蛋完成签到 ,获得积分10
52秒前
Xiaoxiao应助科研通管家采纳,获得10
52秒前
Xiaoxiao应助科研通管家采纳,获得10
52秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3968578
求助须知:如何正确求助?哪些是违规求助? 3513393
关于积分的说明 11167478
捐赠科研通 3248836
什么是DOI,文献DOI怎么找? 1794499
邀请新用户注册赠送积分活动 875131
科研通“疑难数据库(出版商)”最低求助积分说明 804664