An Automobile Noise Prediction Model Based on Extension Data Mining Algorithm

Lü, Hui; Wang, Tichun

doi:10.18280/ria.330503

Cited by 3 publications

(2 citation statements)

References 17 publications

(21 reference statements)

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“…Commercial software such as Altair DesignAI, and Knowledge base provide AI-powered product design and learning from design data in recent years [14,15]. Apart from machine learning models application in product design, several deep learning applications are discussed in the various articles [10,11], that showcased new possibilities in the product development cycle. In sections 3 and 4, the research methodology and data generation setup are discussed in detail.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning based Approach for Brake Pad Pressure Distribution

Kumar¹,

Agarwal²

2022

IJRASET

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The Brake system in automobiles plays a vital role in customer safety. Brake pads are crucial components that experience huge pressure while slowing down the vehicle’s movement. Designing a brake pad requires sound knowledge of structural design, manufacturing ability, and material properties. Further, an optimized model is chosen based on the size of the brake pad and the brake power requirement. Brake pad pressure distribution is one of the main studies in designing automobile brake pads. Numerical solvers are used to validating the experimental results, which is a repetitive process for every new design under the study. Computer-aided engineering tools are costly and time-consuming for design iterations. The present paper provides a new approach for estimating brake pad pressure distribution using machine learning techniques. The accuracy of the machine learning models for various designs shows promising outcomes.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning based Approach for Brake Pad Pressure Distribution

Kumar¹,

Agarwal²

2022

IJRASET

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“…They demonstrated that multitask ANN models are effective for extracting common characteristics from multiple tasks and modelling the noise and acoustic quality of an induction motor. Lu and Wang [15] developed an ANN prediction model for automobile interior noise based on an extension data mining (EDM) algorithm considering basic vehicle parameters, such as the total number of engine valves, engine torque, vehicle, and vehicle speed as inputs of the model. Çelebi et al [16] used an ANN to predict the sound and vibration of a diesel engine.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations

Tan

Chen²,

et al. 2020

Applied Sciences

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It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51automotive noise limit regulations.

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Research on conductive knowledge mining for dust removal strategy in fully mechanized mining faces

Liang

Yang

2023

Procedia Computer Science

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An Automobile Noise Prediction Model Based on Extension Data Mining Algorithm

Cited by 3 publications

References 17 publications

Machine Learning based Approach for Brake Pad Pressure Distribution

Machine Learning based Approach for Brake Pad Pressure Distribution

A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations

Research on conductive knowledge mining for dust removal strategy in fully mechanized mining faces

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