Automated sizing of automotive steering ball joints in parametric CAD environment using expert knowledge and feature-based computer-assisted 3D modelling

Geren, Necdet; Akçalı, Osman Oktay; Unver, Ertu; Allport, John

doi:10.1016/j.aei.2022.101630

Cited by 9 publications

(2 citation statements)

References 54 publications

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“…Performance analysis using datasets from Waymo and KITTI shows an accuracy measure between 95% and 99% on various datasets. This indicates good results with potential for enhancement since the datasets used had low variation [67], [99]. By introducing a technique based on depth and ego-motion optimization, the scientists hope to make careful picture processing easier for autonomous cars.…”

Section: D Vehicle Wheel Under Real World Condition With Deep Learningmentioning

confidence: 87%

“…Study [65] presented an integrated platform that combines CAD and CAE and features a Graphical User Interface (GUI) for visualization. Using an integrated environment to optimize the design of an automotive engine cylinder, this method starts with the virtual prototype generated by the CAD system [67]. Research [32] created an injection molding design support Knowledge Base (KB) system centered around CAD for plastic items.…”

Section: Cad/cae-centricmentioning

confidence: 99%

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A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Akande,

Alabi,

Oyinloye

2024

J. Comput. Theor. Appl.

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Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.

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Section: D Vehicle Wheel Under Real World Condition With Deep Learningmentioning

confidence: 87%

Section: Cad/cae-centricmentioning

confidence: 99%