Application of artificial neural networks to the analysis of friction behaviour in a drawbead profile in sheet metal forming

T Trzepieciński, SM Najm - Materials, 2022 - mdpi.com
Drawbeads are used when forming drawpieces with complex shapes to equalise the flow
resistance of a material around the perimeter of the drawpiece or to change the state of …

Machine learning for predicting fracture strain in sheet metal forming

AE Marques, MA Dib, A Khalfallah, MS Soares… - Metals, 2022 - mdpi.com
Machine learning models are built to predict the strain values for which edge cracking
occurs in hole expansion tests. The samples from this test play the role of sheet metal …

Recent Advances and Applications of Machine Learning in Metal Forming Processes

PA Prates, AFG Pereira - Metals, 2022 - mdpi.com
Machine Learning (ML) is a subfield of artificial intelligence, focusing on computational
algorithms that are designed to learn and improve themselves, without the need to be …

Detection of Defective Deep Drawn Sheet Metal Parts by Using Machine Learning Methods for Image Classification

P Tchasse, A Schenek, KR Riedmüller… - Congress of the German …, 2023 - Springer
The progressing digitization in the manufacturing industry and the enhancements in the field
of machine learning (ML) offer new approaches for monitoring manufacturing processes. In …

Rapid deformation calculation for large reflector antennas: a surrogate model method

ZH Zhang, Q Ye, L Fu, JQ Wang… - … in Astronomy and …, 2022 - iopscience.iop.org
The surface accuracy of the large-aperture reflector antenna has a significant influence on
the observation efficiency. Recent researchers have focused on using the finite element (FE) …

Prediction of Buckling and Maximum Displacement of Hood Oilcanning Using Machine Learning

S Aravamuthan, SS Kangde - 2023 - sae.org
Modern day automotive market demands shorter time to market. Traditional product
development involves design, virtual simulation, testing and launch. Considerable amount of …

Machine Learning Based Approach for Prediction of Hood Oilcanning Performances

A Srinivasan, S Aravamuthan, B Madhurya, SS Kangde - 2023 - sae.org
Abstract Computer Aided Engineering (CAE) simulations are an integral part of the product
development process in an automotive industry. The conventional approach involving pre …

Check for updates Detection of Defective Deep Drawn Sheet Metal Parts by Using Machine Learning Methods for Image Classification

P Tchasse, A Schenek, KR Riedmüller… - Production at the …, 2023 - books.google.com
The progressing digitization in the manufacturing industry and the enhancements in the field
of machine learning (ML) offer new approaches for monitoring manufacturing processes. In …

Innovative Virtual Evaluation Process for Outer Panel Stiffness Using Deep Learning Technology

T Uhm, S Oh - 2024 - sae.org
During the vehicle lifecycle, customers are able to directly perceive the outer panel stiffness
of vehicles in various environmental conditions. The outer panel stiffness is an important …